import{S as lfn,i as ifn,s as dfn,e as a,k as l,w as v,t as o,M as mfn,c as n,d as t,m as i,a as s,x as T,h as r,b as d,G as e,g as b,y as F,q as M,o as E,B as C,v as cfn,L as G}from"../../chunks/vendor-hf-doc-builder.js";import{T as m3o}from"../../chunks/Tip-hf-doc-builder.js";import{D as R}from"../../chunks/Docstring-hf-doc-builder.js";import{C as B}from"../../chunks/CodeBlock-hf-doc-builder.js";import{I as re}from"../../chunks/IconCopyLink-hf-doc-builder.js";import{E as D}from"../../chunks/ExampleCodeBlock-hf-doc-builder.js";function ffn(k){let g,w,u,f,p,m,h,Je,jd,Cg,yt,Od,Vd,GP,wg,He,oo,Xd,Cs,qP,ws,As,jP,zd,Ls,OP,Wd,Ag,gn;return{c(){g=a("p"),w=o("If your "),u=a("code"),f=o("NewModelConfig"),p=o(" is a subclass of "),m=a("code"),h=o("~transformer.PretrainedConfig"),Je=o(`, make sure its
`),jd=a("code"),Cg=o("model_type"),yt=o(" attribute is set to the same key you use when registering the config (here "),Od=a("code"),Vd=o('"new-model"'),GP=o(")."),wg=l(),He=a("p"),oo=o("Likewise, if your "),Xd=a("code"),Cs=o("NewModel"),qP=o(" is a subclass of "),ws=a("a"),As=o("PreTrainedModel"),jP=o(`, make sure its
`),zd=a("code"),Ls=o("config_class"),OP=o(` attribute is set to the same class you use when registering the model (here
`),Wd=a("code"),Ag=o("NewModelConfig"),gn=o(")."),this.h()},l(ro){g=n(ro,"P",{});var Re=s(g);w=r(Re,"If your "),u=n(Re,"CODE",{});var xO=s(u);f=r(xO,"NewModelConfig"),xO.forEach(t),p=r(Re," is a subclass of "),m=n(Re,"CODE",{});var Qd=s(m);h=r(Qd,"~transformer.PretrainedConfig"),Qd.forEach(t),Je=r(Re,`, make sure its
`),jd=n(Re,"CODE",{});var kO=s(jd);Cg=r(kO,"model_type"),kO.forEach(t),yt=r(Re," attribute is set to the same key you use when registering the config (here "),Od=n(Re,"CODE",{});var $O=s(Od);Vd=r($O,'"new-model"'),$O.forEach(t),GP=r(Re,")."),Re.forEach(t),wg=i(ro),He=n(ro,"P",{});var Bo=s(He);oo=r(Bo,"Likewise, if your "),Xd=n(Bo,"CODE",{});var hn=s(Xd);Cs=r(hn,"NewModel"),hn.forEach(t),qP=r(Bo," is a subclass of "),ws=n(Bo,"A",{href:!0});var SO=s(ws);As=r(SO,"PreTrainedModel"),SO.forEach(t),jP=r(Bo,`, make sure its
`),zd=n(Bo,"CODE",{});var Lg=s(zd);Ls=r(Lg,"config_class"),Lg.forEach(t),OP=r(Bo,` attribute is set to the same class you use when registering the model (here
`),Wd=n(Bo,"CODE",{});var RO=s(Wd);Ag=r(RO,"NewModelConfig"),RO.forEach(t),gn=r(Bo,")."),Bo.forEach(t),this.h()},h(){d(ws,"href","/docs/transformers/main/en/main_classes/model#transformers.PreTrainedModel")},m(ro,Re){b(ro,g,Re),e(g,w),e(g,u),e(u,f),e(g,p),e(g,m),e(m,h),e(g,Je),e(g,jd),e(jd,Cg),e(g,yt),e(g,Od),e(Od,Vd),e(g,GP),b(ro,wg,Re),b(ro,He,Re),e(He,oo),e(He,Xd),e(Xd,Cs),e(He,qP),e(He,ws),e(ws,As),e(He,jP),e(He,zd),e(zd,Ls),e(He,OP),e(He,Wd),e(Wd,Ag),e(He,gn)},d(ro){ro&&t(g),ro&&t(wg),ro&&t(He)}}}function gfn(k){let g,w,u,f,p;return f=new B({props:{code:`from transformers import AutoConfig

# Download configuration from huggingface.co and cache.
config = AutoConfig.from_pretrained("bert-base-uncased")

# Download configuration from huggingface.co (user-uploaded) and cache.
config = AutoConfig.from_pretrained("dbmdz/bert-base-german-cased")

# If configuration file is in a directory (e.g., was saved using *save_pretrained('./test/saved_model/')*).
config = AutoConfig.from_pretrained("./test/bert_saved_model/")

# Load a specific configuration file.
config = AutoConfig.from_pretrained("./test/bert_saved_model/my_configuration.json")

# Change some config attributes when loading a pretrained config.
config = AutoConfig.from_pretrained("bert-base-uncased", output_attentions=True, foo=False)
config.output_attentions

config, unused_kwargs = AutoConfig.from_pretrained(
    "bert-base-uncased", output_attentions=True, foo=False, return_unused_kwargs=True
)
config.output_attentions

unused_kwargs`,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(){g=a("p"),w=o("Examples:"),u=l(),v(f.$$.fragment)},l(m){g=n(m,"P",{});var h=s(g);w=r(h,"Examples:"),h.forEach(t),u=i(m),T(f.$$.fragment,m)},m(m,h){b(m,g,h),e(g,w),b(m,u,h),F(f,m,h),p=!0},p:G,i(m){p||(M(f.$$.fragment,m),p=!0)},o(m){E(f.$$.fragment,m),p=!1},d(m){m&&t(g),m&&t(u),C(f,m)}}}function hfn(k){let g,w,u,f,p;return f=new B({props:{code:`from transformers import AutoTokenizer

# Download vocabulary from huggingface.co and cache.
tokenizer = AutoTokenizer.from_pretrained("bert-base-uncased")

# Download vocabulary from huggingface.co (user-uploaded) and cache.
tokenizer = AutoTokenizer.from_pretrained("dbmdz/bert-base-german-cased")

# If vocabulary files are in a directory (e.g. tokenizer was saved using *save_pretrained('./test/saved_model/')*)
tokenizer = AutoTokenizer.from_pretrained("./test/bert_saved_model/")

# Download vocabulary from huggingface.co and define model-specific arguments
tokenizer = AutoTokenizer.from_pretrained("roberta-base", add_prefix_space=True)`,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>tokenizer = AutoTokenizer.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"># 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(){g=a("p"),w=o("Examples:"),u=l(),v(f.$$.fragment)},l(m){g=n(m,"P",{});var h=s(g);w=r(h,"Examples:"),h.forEach(t),u=i(m),T(f.$$.fragment,m)},m(m,h){b(m,g,h),e(g,w),b(m,u,h),F(f,m,h),p=!0},p:G,i(m){p||(M(f.$$.fragment,m),p=!0)},o(m){E(f.$$.fragment,m),p=!1},d(m){m&&t(g),m&&t(u),C(f,m)}}}function ufn(k){let g,w,u,f,p;return{c(){g=a("p"),w=o("Passing "),u=a("code"),f=o("use_auth_token=True"),p=o(" is required when you want to use a private model.")},l(m){g=n(m,"P",{});var h=s(g);w=r(h,"Passing "),u=n(h,"CODE",{});var Je=s(u);f=r(Je,"use_auth_token=True"),Je.forEach(t),p=r(h," is required when you want to use a private model."),h.forEach(t)},m(m,h){b(m,g,h),e(g,w),e(g,u),e(u,f),e(g,p)},d(m){m&&t(g)}}}function pfn(k){let g,w,u,f,p;return f=new B({props:{code:`from transformers import AutoFeatureExtractor

# Download feature extractor from huggingface.co and cache.
feature_extractor = AutoFeatureExtractor.from_pretrained("facebook/wav2vec2-base-960h")

# If feature extractor files are in a directory (e.g. feature extractor was saved using *save_pretrained('./test/saved_model/')*)
feature_extractor = AutoFeatureExtractor.from_pretrained("./test/saved_model/")`,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>feature_extractor = AutoFeatureExtractor.from_pretrained(<span class="hljs-string">&quot;./test/saved_model/&quot;</span>)`}}),{c(){g=a("p"),w=o("Examples:"),u=l(),v(f.$$.fragment)},l(m){g=n(m,"P",{});var h=s(g);w=r(h,"Examples:"),h.forEach(t),u=i(m),T(f.$$.fragment,m)},m(m,h){b(m,g,h),e(g,w),b(m,u,h),F(f,m,h),p=!0},p:G,i(m){p||(M(f.$$.fragment,m),p=!0)},o(m){E(f.$$.fragment,m),p=!1},d(m){m&&t(g),m&&t(u),C(f,m)}}}function _fn(k){let g,w,u,f,p;return{c(){g=a("p"),w=o("Passing "),u=a("code"),f=o("use_auth_token=True"),p=o(" is required when you want to use a private model.")},l(m){g=n(m,"P",{});var h=s(g);w=r(h,"Passing "),u=n(h,"CODE",{});var Je=s(u);f=r(Je,"use_auth_token=True"),Je.forEach(t),p=r(h," is required when you want to use a private model."),h.forEach(t)},m(m,h){b(m,g,h),e(g,w),e(g,u),e(u,f),e(g,p)},d(m){m&&t(g)}}}function bfn(k){let g,w,u,f,p;return f=new B({props:{code:`from transformers import AutoImageProcessor

# Download image processor from huggingface.co and cache.
image_processor = AutoImageProcessor.from_pretrained("google/vit-base-patch16-224-in21k")

# If image processor files are in a directory (e.g. image processor was saved using *save_pretrained('./test/saved_model/')*)
image_processor = AutoImageProcessor.from_pretrained("./test/saved_model/")`,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>image_processor = AutoImageProcessor.from_pretrained(<span class="hljs-string">&quot;./test/saved_model/&quot;</span>)`}}),{c(){g=a("p"),w=o("Examples:"),u=l(),v(f.$$.fragment)},l(m){g=n(m,"P",{});var h=s(g);w=r(h,"Examples:"),h.forEach(t),u=i(m),T(f.$$.fragment,m)},m(m,h){b(m,g,h),e(g,w),b(m,u,h),F(f,m,h),p=!0},p:G,i(m){p||(M(f.$$.fragment,m),p=!0)},o(m){E(f.$$.fragment,m),p=!1},d(m){m&&t(g),m&&t(u),C(f,m)}}}function vfn(k){let g,w,u,f,p;return{c(){g=a("p"),w=o("Passing "),u=a("code"),f=o("use_auth_token=True"),p=o(" is required when you want to use a private model.")},l(m){g=n(m,"P",{});var h=s(g);w=r(h,"Passing "),u=n(h,"CODE",{});var Je=s(u);f=r(Je,"use_auth_token=True"),Je.forEach(t),p=r(h," is required when you want to use a private model."),h.forEach(t)},m(m,h){b(m,g,h),e(g,w),e(g,u),e(u,f),e(g,p)},d(m){m&&t(g)}}}function Tfn(k){let g,w,u,f,p;return f=new B({props:{code:`from transformers import AutoProcessor

# Download processor from huggingface.co and cache.
processor = AutoProcessor.from_pretrained("facebook/wav2vec2-base-960h")

# If processor files are in a directory (e.g. processor was saved using *save_pretrained('./test/saved_model/')*)
processor = AutoProcessor.from_pretrained("./test/saved_model/")`,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>processor = AutoProcessor.from_pretrained(<span class="hljs-string">&quot;./test/saved_model/&quot;</span>)`}}),{c(){g=a("p"),w=o("Examples:"),u=l(),v(f.$$.fragment)},l(m){g=n(m,"P",{});var h=s(g);w=r(h,"Examples:"),h.forEach(t),u=i(m),T(f.$$.fragment,m)},m(m,h){b(m,g,h),e(g,w),b(m,u,h),F(f,m,h),p=!0},p:G,i(m){p||(M(f.$$.fragment,m),p=!0)},o(m){E(f.$$.fragment,m),p=!1},d(m){m&&t(g),m&&t(u),C(f,m)}}}function Ffn(k){let g,w,u,f,p;return f=new B({props:{code:`from transformers import AutoConfig, AutoModel

# Download configuration from huggingface.co and cache.
config = AutoConfig.from_pretrained("bert-base-cased")
model = AutoModel.from_config(config)`,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(){g=a("p"),w=o("Examples:"),u=l(),v(f.$$.fragment)},l(m){g=n(m,"P",{});var h=s(g);w=r(h,"Examples:"),h.forEach(t),u=i(m),T(f.$$.fragment,m)},m(m,h){b(m,g,h),e(g,w),b(m,u,h),F(f,m,h),p=!0},p:G,i(m){p||(M(f.$$.fragment,m),p=!0)},o(m){E(f.$$.fragment,m),p=!1},d(m){m&&t(g),m&&t(u),C(f,m)}}}function Mfn(k){let g,w,u,f,p;return f=new B({props:{code:`from transformers import AutoConfig, AutoModel

# Download model and configuration from huggingface.co and cache.
model = AutoModel.from_pretrained("bert-base-cased")

# Update configuration during loading
model = AutoModel.from_pretrained("bert-base-cased", output_attentions=True)
model.config.output_attentions

# Loading from a TF checkpoint file instead of a PyTorch model (slower)
config = AutoConfig.from_pretrained("./tf_model/bert_tf_model_config.json")
model = AutoModel.from_pretrained(
    "./tf_model/bert_tf_checkpoint.ckpt.index", from_tf=True, config=config
)`,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(){g=a("p"),w=o("Examples:"),u=l(),v(f.$$.fragment)},l(m){g=n(m,"P",{});var h=s(g);w=r(h,"Examples:"),h.forEach(t),u=i(m),T(f.$$.fragment,m)},m(m,h){b(m,g,h),e(g,w),b(m,u,h),F(f,m,h),p=!0},p:G,i(m){p||(M(f.$$.fragment,m),p=!0)},o(m){E(f.$$.fragment,m),p=!1},d(m){m&&t(g),m&&t(u),C(f,m)}}}function Efn(k){let g,w,u,f,p;return f=new B({props:{code:`from transformers import AutoConfig, TFAutoModel

# Download configuration from huggingface.co and cache.
config = AutoConfig.from_pretrained("bert-base-cased")
model = TFAutoModel.from_config(config)`,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(){g=a("p"),w=o("Examples:"),u=l(),v(f.$$.fragment)},l(m){g=n(m,"P",{});var h=s(g);w=r(h,"Examples:"),h.forEach(t),u=i(m),T(f.$$.fragment,m)},m(m,h){b(m,g,h),e(g,w),b(m,u,h),F(f,m,h),p=!0},p:G,i(m){p||(M(f.$$.fragment,m),p=!0)},o(m){E(f.$$.fragment,m),p=!1},d(m){m&&t(g),m&&t(u),C(f,m)}}}function Cfn(k){let g,w,u,f,p;return f=new B({props:{code:`from transformers import AutoConfig, TFAutoModel

# Download model and configuration from huggingface.co and cache.
model = TFAutoModel.from_pretrained("bert-base-cased")

# Update configuration during loading
model = TFAutoModel.from_pretrained("bert-base-cased", output_attentions=True)
model.config.output_attentions

# Loading from a PyTorch checkpoint file instead of a TensorFlow model (slower)
config = AutoConfig.from_pretrained("./pt_model/bert_pt_model_config.json")
model = TFAutoModel.from_pretrained(
    "./pt_model/bert_pytorch_model.bin", from_pt=True, config=config
)`,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(){g=a("p"),w=o("Examples:"),u=l(),v(f.$$.fragment)},l(m){g=n(m,"P",{});var h=s(g);w=r(h,"Examples:"),h.forEach(t),u=i(m),T(f.$$.fragment,m)},m(m,h){b(m,g,h),e(g,w),b(m,u,h),F(f,m,h),p=!0},p:G,i(m){p||(M(f.$$.fragment,m),p=!0)},o(m){E(f.$$.fragment,m),p=!1},d(m){m&&t(g),m&&t(u),C(f,m)}}}function wfn(k){let g,w,u,f,p;return f=new B({props:{code:`from transformers import AutoConfig, FlaxAutoModel

# Download configuration from huggingface.co and cache.
config = AutoConfig.from_pretrained("bert-base-cased")
model = FlaxAutoModel.from_config(config)`,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(){g=a("p"),w=o("Examples:"),u=l(),v(f.$$.fragment)},l(m){g=n(m,"P",{});var h=s(g);w=r(h,"Examples:"),h.forEach(t),u=i(m),T(f.$$.fragment,m)},m(m,h){b(m,g,h),e(g,w),b(m,u,h),F(f,m,h),p=!0},p:G,i(m){p||(M(f.$$.fragment,m),p=!0)},o(m){E(f.$$.fragment,m),p=!1},d(m){m&&t(g),m&&t(u),C(f,m)}}}function Afn(k){let g,w,u,f,p;return f=new B({props:{code:`from transformers import AutoConfig, FlaxAutoModel

# Download model and configuration from huggingface.co and cache.
model = FlaxAutoModel.from_pretrained("bert-base-cased")

# Update configuration during loading
model = FlaxAutoModel.from_pretrained("bert-base-cased", output_attentions=True)
model.config.output_attentions

# Loading from a PyTorch checkpoint file instead of a TensorFlow model (slower)
config = AutoConfig.from_pretrained("./pt_model/bert_pt_model_config.json")
model = FlaxAutoModel.from_pretrained(
    "./pt_model/bert_pytorch_model.bin", from_pt=True, config=config
)`,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(){g=a("p"),w=o("Examples:"),u=l(),v(f.$$.fragment)},l(m){g=n(m,"P",{});var h=s(g);w=r(h,"Examples:"),h.forEach(t),u=i(m),T(f.$$.fragment,m)},m(m,h){b(m,g,h),e(g,w),b(m,u,h),F(f,m,h),p=!0},p:G,i(m){p||(M(f.$$.fragment,m),p=!0)},o(m){E(f.$$.fragment,m),p=!1},d(m){m&&t(g),m&&t(u),C(f,m)}}}function Lfn(k){let g,w,u,f,p;return f=new B({props:{code:`from transformers import AutoConfig, AutoModelForPreTraining

# Download configuration from huggingface.co and cache.
config = AutoConfig.from_pretrained("bert-base-cased")
model = AutoModelForPreTraining.from_config(config)`,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(){g=a("p"),w=o("Examples:"),u=l(),v(f.$$.fragment)},l(m){g=n(m,"P",{});var h=s(g);w=r(h,"Examples:"),h.forEach(t),u=i(m),T(f.$$.fragment,m)},m(m,h){b(m,g,h),e(g,w),b(m,u,h),F(f,m,h),p=!0},p:G,i(m){p||(M(f.$$.fragment,m),p=!0)},o(m){E(f.$$.fragment,m),p=!1},d(m){m&&t(g),m&&t(u),C(f,m)}}}function yfn(k){let g,w,u,f,p;return f=new B({props:{code:`from transformers import AutoConfig, AutoModelForPreTraining

# Download model and configuration from huggingface.co and cache.
model = AutoModelForPreTraining.from_pretrained("bert-base-cased")

# Update configuration during loading
model = AutoModelForPreTraining.from_pretrained("bert-base-cased", output_attentions=True)
model.config.output_attentions

# Loading from a TF checkpoint file instead of a PyTorch model (slower)
config = AutoConfig.from_pretrained("./tf_model/bert_tf_model_config.json")
model = AutoModelForPreTraining.from_pretrained(
    "./tf_model/bert_tf_checkpoint.ckpt.index", from_tf=True, config=config
)`,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(){g=a("p"),w=o("Examples:"),u=l(),v(f.$$.fragment)},l(m){g=n(m,"P",{});var h=s(g);w=r(h,"Examples:"),h.forEach(t),u=i(m),T(f.$$.fragment,m)},m(m,h){b(m,g,h),e(g,w),b(m,u,h),F(f,m,h),p=!0},p:G,i(m){p||(M(f.$$.fragment,m),p=!0)},o(m){E(f.$$.fragment,m),p=!1},d(m){m&&t(g),m&&t(u),C(f,m)}}}function xfn(k){let g,w,u,f,p;return f=new B({props:{code:`from transformers import AutoConfig, TFAutoModelForPreTraining

# Download configuration from huggingface.co and cache.
config = AutoConfig.from_pretrained("bert-base-cased")
model = TFAutoModelForPreTraining.from_config(config)`,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(){g=a("p"),w=o("Examples:"),u=l(),v(f.$$.fragment)},l(m){g=n(m,"P",{});var h=s(g);w=r(h,"Examples:"),h.forEach(t),u=i(m),T(f.$$.fragment,m)},m(m,h){b(m,g,h),e(g,w),b(m,u,h),F(f,m,h),p=!0},p:G,i(m){p||(M(f.$$.fragment,m),p=!0)},o(m){E(f.$$.fragment,m),p=!1},d(m){m&&t(g),m&&t(u),C(f,m)}}}function kfn(k){let g,w,u,f,p;return f=new B({props:{code:`from transformers import AutoConfig, TFAutoModelForPreTraining

# Download model and configuration from huggingface.co and cache.
model = TFAutoModelForPreTraining.from_pretrained("bert-base-cased")

# Update configuration during loading
model = TFAutoModelForPreTraining.from_pretrained("bert-base-cased", output_attentions=True)
model.config.output_attentions

# Loading from a PyTorch checkpoint file instead of a TensorFlow model (slower)
config = AutoConfig.from_pretrained("./pt_model/bert_pt_model_config.json")
model = TFAutoModelForPreTraining.from_pretrained(
    "./pt_model/bert_pytorch_model.bin", from_pt=True, config=config
)`,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(){g=a("p"),w=o("Examples:"),u=l(),v(f.$$.fragment)},l(m){g=n(m,"P",{});var h=s(g);w=r(h,"Examples:"),h.forEach(t),u=i(m),T(f.$$.fragment,m)},m(m,h){b(m,g,h),e(g,w),b(m,u,h),F(f,m,h),p=!0},p:G,i(m){p||(M(f.$$.fragment,m),p=!0)},o(m){E(f.$$.fragment,m),p=!1},d(m){m&&t(g),m&&t(u),C(f,m)}}}function $fn(k){let g,w,u,f,p;return f=new B({props:{code:`from transformers import AutoConfig, FlaxAutoModelForPreTraining

# Download configuration from huggingface.co and cache.
config = AutoConfig.from_pretrained("bert-base-cased")
model = FlaxAutoModelForPreTraining.from_config(config)`,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(){g=a("p"),w=o("Examples:"),u=l(),v(f.$$.fragment)},l(m){g=n(m,"P",{});var h=s(g);w=r(h,"Examples:"),h.forEach(t),u=i(m),T(f.$$.fragment,m)},m(m,h){b(m,g,h),e(g,w),b(m,u,h),F(f,m,h),p=!0},p:G,i(m){p||(M(f.$$.fragment,m),p=!0)},o(m){E(f.$$.fragment,m),p=!1},d(m){m&&t(g),m&&t(u),C(f,m)}}}function Sfn(k){let g,w,u,f,p;return f=new B({props:{code:`from transformers import AutoConfig, FlaxAutoModelForPreTraining

# Download model and configuration from huggingface.co and cache.
model = FlaxAutoModelForPreTraining.from_pretrained("bert-base-cased")

# Update configuration during loading
model = FlaxAutoModelForPreTraining.from_pretrained("bert-base-cased", output_attentions=True)
model.config.output_attentions

# Loading from a PyTorch checkpoint file instead of a TensorFlow model (slower)
config = AutoConfig.from_pretrained("./pt_model/bert_pt_model_config.json")
model = FlaxAutoModelForPreTraining.from_pretrained(
    "./pt_model/bert_pytorch_model.bin", from_pt=True, config=config
)`,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(){g=a("p"),w=o("Examples:"),u=l(),v(f.$$.fragment)},l(m){g=n(m,"P",{});var h=s(g);w=r(h,"Examples:"),h.forEach(t),u=i(m),T(f.$$.fragment,m)},m(m,h){b(m,g,h),e(g,w),b(m,u,h),F(f,m,h),p=!0},p:G,i(m){p||(M(f.$$.fragment,m),p=!0)},o(m){E(f.$$.fragment,m),p=!1},d(m){m&&t(g),m&&t(u),C(f,m)}}}function Rfn(k){let g,w,u,f,p;return f=new B({props:{code:`from transformers import AutoConfig, AutoModelForCausalLM

# Download configuration from huggingface.co and cache.
config = AutoConfig.from_pretrained("bert-base-cased")
model = AutoModelForCausalLM.from_config(config)`,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(){g=a("p"),w=o("Examples:"),u=l(),v(f.$$.fragment)},l(m){g=n(m,"P",{});var h=s(g);w=r(h,"Examples:"),h.forEach(t),u=i(m),T(f.$$.fragment,m)},m(m,h){b(m,g,h),e(g,w),b(m,u,h),F(f,m,h),p=!0},p:G,i(m){p||(M(f.$$.fragment,m),p=!0)},o(m){E(f.$$.fragment,m),p=!1},d(m){m&&t(g),m&&t(u),C(f,m)}}}function Pfn(k){let g,w,u,f,p;return f=new B({props:{code:`from transformers import AutoConfig, AutoModelForCausalLM

# Download model and configuration from huggingface.co and cache.
model = AutoModelForCausalLM.from_pretrained("bert-base-cased")

# Update configuration during loading
model = AutoModelForCausalLM.from_pretrained("bert-base-cased", output_attentions=True)
model.config.output_attentions

# Loading from a TF checkpoint file instead of a PyTorch model (slower)
config = AutoConfig.from_pretrained("./tf_model/bert_tf_model_config.json")
model = AutoModelForCausalLM.from_pretrained(
    "./tf_model/bert_tf_checkpoint.ckpt.index", from_tf=True, config=config
)`,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(){g=a("p"),w=o("Examples:"),u=l(),v(f.$$.fragment)},l(m){g=n(m,"P",{});var h=s(g);w=r(h,"Examples:"),h.forEach(t),u=i(m),T(f.$$.fragment,m)},m(m,h){b(m,g,h),e(g,w),b(m,u,h),F(f,m,h),p=!0},p:G,i(m){p||(M(f.$$.fragment,m),p=!0)},o(m){E(f.$$.fragment,m),p=!1},d(m){m&&t(g),m&&t(u),C(f,m)}}}function Ifn(k){let g,w,u,f,p;return f=new B({props:{code:`from transformers import AutoConfig, TFAutoModelForCausalLM

# Download configuration from huggingface.co and cache.
config = AutoConfig.from_pretrained("bert-base-cased")
model = TFAutoModelForCausalLM.from_config(config)`,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(){g=a("p"),w=o("Examples:"),u=l(),v(f.$$.fragment)},l(m){g=n(m,"P",{});var h=s(g);w=r(h,"Examples:"),h.forEach(t),u=i(m),T(f.$$.fragment,m)},m(m,h){b(m,g,h),e(g,w),b(m,u,h),F(f,m,h),p=!0},p:G,i(m){p||(M(f.$$.fragment,m),p=!0)},o(m){E(f.$$.fragment,m),p=!1},d(m){m&&t(g),m&&t(u),C(f,m)}}}function Bfn(k){let g,w,u,f,p;return f=new B({props:{code:`from transformers import AutoConfig, TFAutoModelForCausalLM

# Download model and configuration from huggingface.co and cache.
model = TFAutoModelForCausalLM.from_pretrained("bert-base-cased")

# Update configuration during loading
model = TFAutoModelForCausalLM.from_pretrained("bert-base-cased", output_attentions=True)
model.config.output_attentions

# Loading from a PyTorch checkpoint file instead of a TensorFlow model (slower)
config = AutoConfig.from_pretrained("./pt_model/bert_pt_model_config.json")
model = TFAutoModelForCausalLM.from_pretrained(
    "./pt_model/bert_pytorch_model.bin", from_pt=True, config=config
)`,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(){g=a("p"),w=o("Examples:"),u=l(),v(f.$$.fragment)},l(m){g=n(m,"P",{});var h=s(g);w=r(h,"Examples:"),h.forEach(t),u=i(m),T(f.$$.fragment,m)},m(m,h){b(m,g,h),e(g,w),b(m,u,h),F(f,m,h),p=!0},p:G,i(m){p||(M(f.$$.fragment,m),p=!0)},o(m){E(f.$$.fragment,m),p=!1},d(m){m&&t(g),m&&t(u),C(f,m)}}}function Nfn(k){let g,w,u,f,p;return f=new B({props:{code:`from transformers import AutoConfig, FlaxAutoModelForCausalLM

# Download configuration from huggingface.co and cache.
config = AutoConfig.from_pretrained("bert-base-cased")
model = FlaxAutoModelForCausalLM.from_config(config)`,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(){g=a("p"),w=o("Examples:"),u=l(),v(f.$$.fragment)},l(m){g=n(m,"P",{});var h=s(g);w=r(h,"Examples:"),h.forEach(t),u=i(m),T(f.$$.fragment,m)},m(m,h){b(m,g,h),e(g,w),b(m,u,h),F(f,m,h),p=!0},p:G,i(m){p||(M(f.$$.fragment,m),p=!0)},o(m){E(f.$$.fragment,m),p=!1},d(m){m&&t(g),m&&t(u),C(f,m)}}}function Dfn(k){let g,w,u,f,p;return f=new B({props:{code:`from transformers import AutoConfig, FlaxAutoModelForCausalLM

# Download model and configuration from huggingface.co and cache.
model = FlaxAutoModelForCausalLM.from_pretrained("bert-base-cased")

# Update configuration during loading
model = FlaxAutoModelForCausalLM.from_pretrained("bert-base-cased", output_attentions=True)
model.config.output_attentions

# Loading from a PyTorch checkpoint file instead of a TensorFlow model (slower)
config = AutoConfig.from_pretrained("./pt_model/bert_pt_model_config.json")
model = FlaxAutoModelForCausalLM.from_pretrained(
    "./pt_model/bert_pytorch_model.bin", from_pt=True, config=config
)`,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(){g=a("p"),w=o("Examples:"),u=l(),v(f.$$.fragment)},l(m){g=n(m,"P",{});var h=s(g);w=r(h,"Examples:"),h.forEach(t),u=i(m),T(f.$$.fragment,m)},m(m,h){b(m,g,h),e(g,w),b(m,u,h),F(f,m,h),p=!0},p:G,i(m){p||(M(f.$$.fragment,m),p=!0)},o(m){E(f.$$.fragment,m),p=!1},d(m){m&&t(g),m&&t(u),C(f,m)}}}function Gfn(k){let g,w,u,f,p;return f=new B({props:{code:`from transformers import AutoConfig, AutoModelForMaskedLM

# Download configuration from huggingface.co and cache.
config = AutoConfig.from_pretrained("bert-base-cased")
model = AutoModelForMaskedLM.from_config(config)`,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(){g=a("p"),w=o("Examples:"),u=l(),v(f.$$.fragment)},l(m){g=n(m,"P",{});var h=s(g);w=r(h,"Examples:"),h.forEach(t),u=i(m),T(f.$$.fragment,m)},m(m,h){b(m,g,h),e(g,w),b(m,u,h),F(f,m,h),p=!0},p:G,i(m){p||(M(f.$$.fragment,m),p=!0)},o(m){E(f.$$.fragment,m),p=!1},d(m){m&&t(g),m&&t(u),C(f,m)}}}function qfn(k){let g,w,u,f,p;return f=new B({props:{code:`from transformers import AutoConfig, AutoModelForMaskedLM

# Download model and configuration from huggingface.co and cache.
model = AutoModelForMaskedLM.from_pretrained("bert-base-cased")

# Update configuration during loading
model = AutoModelForMaskedLM.from_pretrained("bert-base-cased", output_attentions=True)
model.config.output_attentions

# Loading from a TF checkpoint file instead of a PyTorch model (slower)
config = AutoConfig.from_pretrained("./tf_model/bert_tf_model_config.json")
model = AutoModelForMaskedLM.from_pretrained(
    "./tf_model/bert_tf_checkpoint.ckpt.index", from_tf=True, config=config
)`,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(){g=a("p"),w=o("Examples:"),u=l(),v(f.$$.fragment)},l(m){g=n(m,"P",{});var h=s(g);w=r(h,"Examples:"),h.forEach(t),u=i(m),T(f.$$.fragment,m)},m(m,h){b(m,g,h),e(g,w),b(m,u,h),F(f,m,h),p=!0},p:G,i(m){p||(M(f.$$.fragment,m),p=!0)},o(m){E(f.$$.fragment,m),p=!1},d(m){m&&t(g),m&&t(u),C(f,m)}}}function jfn(k){let g,w,u,f,p;return f=new B({props:{code:`from transformers import AutoConfig, TFAutoModelForMaskedLM

# Download configuration from huggingface.co and cache.
config = AutoConfig.from_pretrained("bert-base-cased")
model = TFAutoModelForMaskedLM.from_config(config)`,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(){g=a("p"),w=o("Examples:"),u=l(),v(f.$$.fragment)},l(m){g=n(m,"P",{});var h=s(g);w=r(h,"Examples:"),h.forEach(t),u=i(m),T(f.$$.fragment,m)},m(m,h){b(m,g,h),e(g,w),b(m,u,h),F(f,m,h),p=!0},p:G,i(m){p||(M(f.$$.fragment,m),p=!0)},o(m){E(f.$$.fragment,m),p=!1},d(m){m&&t(g),m&&t(u),C(f,m)}}}function Ofn(k){let g,w,u,f,p;return f=new B({props:{code:`from transformers import AutoConfig, TFAutoModelForMaskedLM

# Download model and configuration from huggingface.co and cache.
model = TFAutoModelForMaskedLM.from_pretrained("bert-base-cased")

# Update configuration during loading
model = TFAutoModelForMaskedLM.from_pretrained("bert-base-cased", output_attentions=True)
model.config.output_attentions

# Loading from a PyTorch checkpoint file instead of a TensorFlow model (slower)
config = AutoConfig.from_pretrained("./pt_model/bert_pt_model_config.json")
model = TFAutoModelForMaskedLM.from_pretrained(
    "./pt_model/bert_pytorch_model.bin", from_pt=True, config=config
)`,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(){g=a("p"),w=o("Examples:"),u=l(),v(f.$$.fragment)},l(m){g=n(m,"P",{});var h=s(g);w=r(h,"Examples:"),h.forEach(t),u=i(m),T(f.$$.fragment,m)},m(m,h){b(m,g,h),e(g,w),b(m,u,h),F(f,m,h),p=!0},p:G,i(m){p||(M(f.$$.fragment,m),p=!0)},o(m){E(f.$$.fragment,m),p=!1},d(m){m&&t(g),m&&t(u),C(f,m)}}}function Vfn(k){let g,w,u,f,p;return f=new B({props:{code:`from transformers import AutoConfig, FlaxAutoModelForMaskedLM

# Download configuration from huggingface.co and cache.
config = AutoConfig.from_pretrained("bert-base-cased")
model = FlaxAutoModelForMaskedLM.from_config(config)`,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(){g=a("p"),w=o("Examples:"),u=l(),v(f.$$.fragment)},l(m){g=n(m,"P",{});var h=s(g);w=r(h,"Examples:"),h.forEach(t),u=i(m),T(f.$$.fragment,m)},m(m,h){b(m,g,h),e(g,w),b(m,u,h),F(f,m,h),p=!0},p:G,i(m){p||(M(f.$$.fragment,m),p=!0)},o(m){E(f.$$.fragment,m),p=!1},d(m){m&&t(g),m&&t(u),C(f,m)}}}function Xfn(k){let g,w,u,f,p;return f=new B({props:{code:`from transformers import AutoConfig, FlaxAutoModelForMaskedLM

# Download model and configuration from huggingface.co and cache.
model = FlaxAutoModelForMaskedLM.from_pretrained("bert-base-cased")

# Update configuration during loading
model = FlaxAutoModelForMaskedLM.from_pretrained("bert-base-cased", output_attentions=True)
model.config.output_attentions

# Loading from a PyTorch checkpoint file instead of a TensorFlow model (slower)
config = AutoConfig.from_pretrained("./pt_model/bert_pt_model_config.json")
model = FlaxAutoModelForMaskedLM.from_pretrained(
    "./pt_model/bert_pytorch_model.bin", from_pt=True, config=config
)`,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(){g=a("p"),w=o("Examples:"),u=l(),v(f.$$.fragment)},l(m){g=n(m,"P",{});var h=s(g);w=r(h,"Examples:"),h.forEach(t),u=i(m),T(f.$$.fragment,m)},m(m,h){b(m,g,h),e(g,w),b(m,u,h),F(f,m,h),p=!0},p:G,i(m){p||(M(f.$$.fragment,m),p=!0)},o(m){E(f.$$.fragment,m),p=!1},d(m){m&&t(g),m&&t(u),C(f,m)}}}function zfn(k){let g,w,u,f,p;return f=new B({props:{code:`from transformers import AutoConfig, AutoModelForSeq2SeqLM

# Download configuration from huggingface.co and cache.
config = AutoConfig.from_pretrained("t5-base")
model = AutoModelForSeq2SeqLM.from_config(config)`,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(){g=a("p"),w=o("Examples:"),u=l(),v(f.$$.fragment)},l(m){g=n(m,"P",{});var h=s(g);w=r(h,"Examples:"),h.forEach(t),u=i(m),T(f.$$.fragment,m)},m(m,h){b(m,g,h),e(g,w),b(m,u,h),F(f,m,h),p=!0},p:G,i(m){p||(M(f.$$.fragment,m),p=!0)},o(m){E(f.$$.fragment,m),p=!1},d(m){m&&t(g),m&&t(u),C(f,m)}}}function Wfn(k){let g,w,u,f,p;return f=new B({props:{code:`from transformers import AutoConfig, AutoModelForSeq2SeqLM

# Download model and configuration from huggingface.co and cache.
model = AutoModelForSeq2SeqLM.from_pretrained("t5-base")

# Update configuration during loading
model = AutoModelForSeq2SeqLM.from_pretrained("t5-base", output_attentions=True)
model.config.output_attentions

# Loading from a TF checkpoint file instead of a PyTorch model (slower)
config = AutoConfig.from_pretrained("./tf_model/t5_tf_model_config.json")
model = AutoModelForSeq2SeqLM.from_pretrained(
    "./tf_model/t5_tf_checkpoint.ckpt.index", from_tf=True, config=config
)`,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(){g=a("p"),w=o("Examples:"),u=l(),v(f.$$.fragment)},l(m){g=n(m,"P",{});var h=s(g);w=r(h,"Examples:"),h.forEach(t),u=i(m),T(f.$$.fragment,m)},m(m,h){b(m,g,h),e(g,w),b(m,u,h),F(f,m,h),p=!0},p:G,i(m){p||(M(f.$$.fragment,m),p=!0)},o(m){E(f.$$.fragment,m),p=!1},d(m){m&&t(g),m&&t(u),C(f,m)}}}function Qfn(k){let g,w,u,f,p;return f=new B({props:{code:`from transformers import AutoConfig, TFAutoModelForSeq2SeqLM

# Download configuration from huggingface.co and cache.
config = AutoConfig.from_pretrained("t5-base")
model = TFAutoModelForSeq2SeqLM.from_config(config)`,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(){g=a("p"),w=o("Examples:"),u=l(),v(f.$$.fragment)},l(m){g=n(m,"P",{});var h=s(g);w=r(h,"Examples:"),h.forEach(t),u=i(m),T(f.$$.fragment,m)},m(m,h){b(m,g,h),e(g,w),b(m,u,h),F(f,m,h),p=!0},p:G,i(m){p||(M(f.$$.fragment,m),p=!0)},o(m){E(f.$$.fragment,m),p=!1},d(m){m&&t(g),m&&t(u),C(f,m)}}}function Ufn(k){let g,w,u,f,p;return f=new B({props:{code:`from transformers import AutoConfig, TFAutoModelForSeq2SeqLM

# Download model and configuration from huggingface.co and cache.
model = TFAutoModelForSeq2SeqLM.from_pretrained("t5-base")

# Update configuration during loading
model = TFAutoModelForSeq2SeqLM.from_pretrained("t5-base", output_attentions=True)
model.config.output_attentions

# Loading from a PyTorch checkpoint file instead of a TensorFlow model (slower)
config = AutoConfig.from_pretrained("./pt_model/t5_pt_model_config.json")
model = TFAutoModelForSeq2SeqLM.from_pretrained(
    "./pt_model/t5_pytorch_model.bin", from_pt=True, config=config
)`,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(){g=a("p"),w=o("Examples:"),u=l(),v(f.$$.fragment)},l(m){g=n(m,"P",{});var h=s(g);w=r(h,"Examples:"),h.forEach(t),u=i(m),T(f.$$.fragment,m)},m(m,h){b(m,g,h),e(g,w),b(m,u,h),F(f,m,h),p=!0},p:G,i(m){p||(M(f.$$.fragment,m),p=!0)},o(m){E(f.$$.fragment,m),p=!1},d(m){m&&t(g),m&&t(u),C(f,m)}}}function Hfn(k){let g,w,u,f,p;return f=new B({props:{code:`from transformers import AutoConfig, FlaxAutoModelForSeq2SeqLM

# Download configuration from huggingface.co and cache.
config = AutoConfig.from_pretrained("t5-base")
model = FlaxAutoModelForSeq2SeqLM.from_config(config)`,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(){g=a("p"),w=o("Examples:"),u=l(),v(f.$$.fragment)},l(m){g=n(m,"P",{});var h=s(g);w=r(h,"Examples:"),h.forEach(t),u=i(m),T(f.$$.fragment,m)},m(m,h){b(m,g,h),e(g,w),b(m,u,h),F(f,m,h),p=!0},p:G,i(m){p||(M(f.$$.fragment,m),p=!0)},o(m){E(f.$$.fragment,m),p=!1},d(m){m&&t(g),m&&t(u),C(f,m)}}}function Jfn(k){let g,w,u,f,p;return f=new B({props:{code:`from transformers import AutoConfig, FlaxAutoModelForSeq2SeqLM

# Download model and configuration from huggingface.co and cache.
model = FlaxAutoModelForSeq2SeqLM.from_pretrained("t5-base")

# Update configuration during loading
model = FlaxAutoModelForSeq2SeqLM.from_pretrained("t5-base", output_attentions=True)
model.config.output_attentions

# Loading from a PyTorch checkpoint file instead of a TensorFlow model (slower)
config = AutoConfig.from_pretrained("./pt_model/t5_pt_model_config.json")
model = FlaxAutoModelForSeq2SeqLM.from_pretrained(
    "./pt_model/t5_pytorch_model.bin", from_pt=True, config=config
)`,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(){g=a("p"),w=o("Examples:"),u=l(),v(f.$$.fragment)},l(m){g=n(m,"P",{});var h=s(g);w=r(h,"Examples:"),h.forEach(t),u=i(m),T(f.$$.fragment,m)},m(m,h){b(m,g,h),e(g,w),b(m,u,h),F(f,m,h),p=!0},p:G,i(m){p||(M(f.$$.fragment,m),p=!0)},o(m){E(f.$$.fragment,m),p=!1},d(m){m&&t(g),m&&t(u),C(f,m)}}}function Yfn(k){let g,w,u,f,p;return f=new B({props:{code:`from transformers import AutoConfig, AutoModelForSequenceClassification

# Download configuration from huggingface.co and cache.
config = AutoConfig.from_pretrained("bert-base-cased")
model = AutoModelForSequenceClassification.from_config(config)`,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(){g=a("p"),w=o("Examples:"),u=l(),v(f.$$.fragment)},l(m){g=n(m,"P",{});var h=s(g);w=r(h,"Examples:"),h.forEach(t),u=i(m),T(f.$$.fragment,m)},m(m,h){b(m,g,h),e(g,w),b(m,u,h),F(f,m,h),p=!0},p:G,i(m){p||(M(f.$$.fragment,m),p=!0)},o(m){E(f.$$.fragment,m),p=!1},d(m){m&&t(g),m&&t(u),C(f,m)}}}function Zfn(k){let g,w,u,f,p;return f=new B({props:{code:`from transformers import AutoConfig, AutoModelForSequenceClassification

# Download model and configuration from huggingface.co and cache.
model = AutoModelForSequenceClassification.from_pretrained("bert-base-cased")

# Update configuration during loading
model = AutoModelForSequenceClassification.from_pretrained("bert-base-cased", output_attentions=True)
model.config.output_attentions

# Loading from a TF checkpoint file instead of a PyTorch model (slower)
config = AutoConfig.from_pretrained("./tf_model/bert_tf_model_config.json")
model = AutoModelForSequenceClassification.from_pretrained(
    "./tf_model/bert_tf_checkpoint.ckpt.index", from_tf=True, config=config
)`,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(){g=a("p"),w=o("Examples:"),u=l(),v(f.$$.fragment)},l(m){g=n(m,"P",{});var h=s(g);w=r(h,"Examples:"),h.forEach(t),u=i(m),T(f.$$.fragment,m)},m(m,h){b(m,g,h),e(g,w),b(m,u,h),F(f,m,h),p=!0},p:G,i(m){p||(M(f.$$.fragment,m),p=!0)},o(m){E(f.$$.fragment,m),p=!1},d(m){m&&t(g),m&&t(u),C(f,m)}}}function Kfn(k){let g,w,u,f,p;return f=new B({props:{code:`from transformers import AutoConfig, TFAutoModelForSequenceClassification

# Download configuration from huggingface.co and cache.
config = AutoConfig.from_pretrained("bert-base-cased")
model = TFAutoModelForSequenceClassification.from_config(config)`,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(){g=a("p"),w=o("Examples:"),u=l(),v(f.$$.fragment)},l(m){g=n(m,"P",{});var h=s(g);w=r(h,"Examples:"),h.forEach(t),u=i(m),T(f.$$.fragment,m)},m(m,h){b(m,g,h),e(g,w),b(m,u,h),F(f,m,h),p=!0},p:G,i(m){p||(M(f.$$.fragment,m),p=!0)},o(m){E(f.$$.fragment,m),p=!1},d(m){m&&t(g),m&&t(u),C(f,m)}}}function egn(k){let g,w,u,f,p;return f=new B({props:{code:`from transformers import AutoConfig, TFAutoModelForSequenceClassification

# Download model and configuration from huggingface.co and cache.
model = TFAutoModelForSequenceClassification.from_pretrained("bert-base-cased")

# Update configuration during loading
model = TFAutoModelForSequenceClassification.from_pretrained("bert-base-cased", output_attentions=True)
model.config.output_attentions

# Loading from a PyTorch checkpoint file instead of a TensorFlow model (slower)
config = AutoConfig.from_pretrained("./pt_model/bert_pt_model_config.json")
model = TFAutoModelForSequenceClassification.from_pretrained(
    "./pt_model/bert_pytorch_model.bin", from_pt=True, config=config
)`,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(){g=a("p"),w=o("Examples:"),u=l(),v(f.$$.fragment)},l(m){g=n(m,"P",{});var h=s(g);w=r(h,"Examples:"),h.forEach(t),u=i(m),T(f.$$.fragment,m)},m(m,h){b(m,g,h),e(g,w),b(m,u,h),F(f,m,h),p=!0},p:G,i(m){p||(M(f.$$.fragment,m),p=!0)},o(m){E(f.$$.fragment,m),p=!1},d(m){m&&t(g),m&&t(u),C(f,m)}}}function ogn(k){let g,w,u,f,p;return f=new B({props:{code:`from transformers import AutoConfig, FlaxAutoModelForSequenceClassification

# Download configuration from huggingface.co and cache.
config = AutoConfig.from_pretrained("bert-base-cased")
model = FlaxAutoModelForSequenceClassification.from_config(config)`,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(){g=a("p"),w=o("Examples:"),u=l(),v(f.$$.fragment)},l(m){g=n(m,"P",{});var h=s(g);w=r(h,"Examples:"),h.forEach(t),u=i(m),T(f.$$.fragment,m)},m(m,h){b(m,g,h),e(g,w),b(m,u,h),F(f,m,h),p=!0},p:G,i(m){p||(M(f.$$.fragment,m),p=!0)},o(m){E(f.$$.fragment,m),p=!1},d(m){m&&t(g),m&&t(u),C(f,m)}}}function rgn(k){let g,w,u,f,p;return f=new B({props:{code:`from transformers import AutoConfig, FlaxAutoModelForSequenceClassification

# Download model and configuration from huggingface.co and cache.
model = FlaxAutoModelForSequenceClassification.from_pretrained("bert-base-cased")

# Update configuration during loading
model = FlaxAutoModelForSequenceClassification.from_pretrained("bert-base-cased", output_attentions=True)
model.config.output_attentions

# Loading from a PyTorch checkpoint file instead of a TensorFlow model (slower)
config = AutoConfig.from_pretrained("./pt_model/bert_pt_model_config.json")
model = FlaxAutoModelForSequenceClassification.from_pretrained(
    "./pt_model/bert_pytorch_model.bin", from_pt=True, config=config
)`,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(){g=a("p"),w=o("Examples:"),u=l(),v(f.$$.fragment)},l(m){g=n(m,"P",{});var h=s(g);w=r(h,"Examples:"),h.forEach(t),u=i(m),T(f.$$.fragment,m)},m(m,h){b(m,g,h),e(g,w),b(m,u,h),F(f,m,h),p=!0},p:G,i(m){p||(M(f.$$.fragment,m),p=!0)},o(m){E(f.$$.fragment,m),p=!1},d(m){m&&t(g),m&&t(u),C(f,m)}}}function tgn(k){let g,w,u,f,p;return f=new B({props:{code:`from transformers import AutoConfig, AutoModelForMultipleChoice

# Download configuration from huggingface.co and cache.
config = AutoConfig.from_pretrained("bert-base-cased")
model = AutoModelForMultipleChoice.from_config(config)`,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(){g=a("p"),w=o("Examples:"),u=l(),v(f.$$.fragment)},l(m){g=n(m,"P",{});var h=s(g);w=r(h,"Examples:"),h.forEach(t),u=i(m),T(f.$$.fragment,m)},m(m,h){b(m,g,h),e(g,w),b(m,u,h),F(f,m,h),p=!0},p:G,i(m){p||(M(f.$$.fragment,m),p=!0)},o(m){E(f.$$.fragment,m),p=!1},d(m){m&&t(g),m&&t(u),C(f,m)}}}function agn(k){let g,w,u,f,p;return f=new B({props:{code:`from transformers import AutoConfig, AutoModelForMultipleChoice

# Download model and configuration from huggingface.co and cache.
model = AutoModelForMultipleChoice.from_pretrained("bert-base-cased")

# Update configuration during loading
model = AutoModelForMultipleChoice.from_pretrained("bert-base-cased", output_attentions=True)
model.config.output_attentions

# Loading from a TF checkpoint file instead of a PyTorch model (slower)
config = AutoConfig.from_pretrained("./tf_model/bert_tf_model_config.json")
model = AutoModelForMultipleChoice.from_pretrained(
    "./tf_model/bert_tf_checkpoint.ckpt.index", from_tf=True, config=config
)`,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(){g=a("p"),w=o("Examples:"),u=l(),v(f.$$.fragment)},l(m){g=n(m,"P",{});var h=s(g);w=r(h,"Examples:"),h.forEach(t),u=i(m),T(f.$$.fragment,m)},m(m,h){b(m,g,h),e(g,w),b(m,u,h),F(f,m,h),p=!0},p:G,i(m){p||(M(f.$$.fragment,m),p=!0)},o(m){E(f.$$.fragment,m),p=!1},d(m){m&&t(g),m&&t(u),C(f,m)}}}function ngn(k){let g,w,u,f,p;return f=new B({props:{code:`from transformers import AutoConfig, TFAutoModelForMultipleChoice

# Download configuration from huggingface.co and cache.
config = AutoConfig.from_pretrained("bert-base-cased")
model = TFAutoModelForMultipleChoice.from_config(config)`,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(){g=a("p"),w=o("Examples:"),u=l(),v(f.$$.fragment)},l(m){g=n(m,"P",{});var h=s(g);w=r(h,"Examples:"),h.forEach(t),u=i(m),T(f.$$.fragment,m)},m(m,h){b(m,g,h),e(g,w),b(m,u,h),F(f,m,h),p=!0},p:G,i(m){p||(M(f.$$.fragment,m),p=!0)},o(m){E(f.$$.fragment,m),p=!1},d(m){m&&t(g),m&&t(u),C(f,m)}}}function sgn(k){let g,w,u,f,p;return f=new B({props:{code:`from transformers import AutoConfig, TFAutoModelForMultipleChoice

# Download model and configuration from huggingface.co and cache.
model = TFAutoModelForMultipleChoice.from_pretrained("bert-base-cased")

# Update configuration during loading
model = TFAutoModelForMultipleChoice.from_pretrained("bert-base-cased", output_attentions=True)
model.config.output_attentions

# Loading from a PyTorch checkpoint file instead of a TensorFlow model (slower)
config = AutoConfig.from_pretrained("./pt_model/bert_pt_model_config.json")
model = TFAutoModelForMultipleChoice.from_pretrained(
    "./pt_model/bert_pytorch_model.bin", from_pt=True, config=config
)`,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(){g=a("p"),w=o("Examples:"),u=l(),v(f.$$.fragment)},l(m){g=n(m,"P",{});var h=s(g);w=r(h,"Examples:"),h.forEach(t),u=i(m),T(f.$$.fragment,m)},m(m,h){b(m,g,h),e(g,w),b(m,u,h),F(f,m,h),p=!0},p:G,i(m){p||(M(f.$$.fragment,m),p=!0)},o(m){E(f.$$.fragment,m),p=!1},d(m){m&&t(g),m&&t(u),C(f,m)}}}function lgn(k){let g,w,u,f,p;return f=new B({props:{code:`from transformers import AutoConfig, FlaxAutoModelForMultipleChoice

# Download configuration from huggingface.co and cache.
config = AutoConfig.from_pretrained("bert-base-cased")
model = FlaxAutoModelForMultipleChoice.from_config(config)`,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(){g=a("p"),w=o("Examples:"),u=l(),v(f.$$.fragment)},l(m){g=n(m,"P",{});var h=s(g);w=r(h,"Examples:"),h.forEach(t),u=i(m),T(f.$$.fragment,m)},m(m,h){b(m,g,h),e(g,w),b(m,u,h),F(f,m,h),p=!0},p:G,i(m){p||(M(f.$$.fragment,m),p=!0)},o(m){E(f.$$.fragment,m),p=!1},d(m){m&&t(g),m&&t(u),C(f,m)}}}function ign(k){let g,w,u,f,p;return f=new B({props:{code:`from transformers import AutoConfig, FlaxAutoModelForMultipleChoice

# Download model and configuration from huggingface.co and cache.
model = FlaxAutoModelForMultipleChoice.from_pretrained("bert-base-cased")

# Update configuration during loading
model = FlaxAutoModelForMultipleChoice.from_pretrained("bert-base-cased", output_attentions=True)
model.config.output_attentions

# Loading from a PyTorch checkpoint file instead of a TensorFlow model (slower)
config = AutoConfig.from_pretrained("./pt_model/bert_pt_model_config.json")
model = FlaxAutoModelForMultipleChoice.from_pretrained(
    "./pt_model/bert_pytorch_model.bin", from_pt=True, config=config
)`,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(){g=a("p"),w=o("Examples:"),u=l(),v(f.$$.fragment)},l(m){g=n(m,"P",{});var h=s(g);w=r(h,"Examples:"),h.forEach(t),u=i(m),T(f.$$.fragment,m)},m(m,h){b(m,g,h),e(g,w),b(m,u,h),F(f,m,h),p=!0},p:G,i(m){p||(M(f.$$.fragment,m),p=!0)},o(m){E(f.$$.fragment,m),p=!1},d(m){m&&t(g),m&&t(u),C(f,m)}}}function dgn(k){let g,w,u,f,p;return f=new B({props:{code:`from transformers import AutoConfig, AutoModelForNextSentencePrediction

# Download configuration from huggingface.co and cache.
config = AutoConfig.from_pretrained("bert-base-cased")
model = AutoModelForNextSentencePrediction.from_config(config)`,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(){g=a("p"),w=o("Examples:"),u=l(),v(f.$$.fragment)},l(m){g=n(m,"P",{});var h=s(g);w=r(h,"Examples:"),h.forEach(t),u=i(m),T(f.$$.fragment,m)},m(m,h){b(m,g,h),e(g,w),b(m,u,h),F(f,m,h),p=!0},p:G,i(m){p||(M(f.$$.fragment,m),p=!0)},o(m){E(f.$$.fragment,m),p=!1},d(m){m&&t(g),m&&t(u),C(f,m)}}}function mgn(k){let g,w,u,f,p;return f=new B({props:{code:`from transformers import AutoConfig, AutoModelForNextSentencePrediction

# Download model and configuration from huggingface.co and cache.
model = AutoModelForNextSentencePrediction.from_pretrained("bert-base-cased")

# Update configuration during loading
model = AutoModelForNextSentencePrediction.from_pretrained("bert-base-cased", output_attentions=True)
model.config.output_attentions

# Loading from a TF checkpoint file instead of a PyTorch model (slower)
config = AutoConfig.from_pretrained("./tf_model/bert_tf_model_config.json")
model = AutoModelForNextSentencePrediction.from_pretrained(
    "./tf_model/bert_tf_checkpoint.ckpt.index", from_tf=True, config=config
)`,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(){g=a("p"),w=o("Examples:"),u=l(),v(f.$$.fragment)},l(m){g=n(m,"P",{});var h=s(g);w=r(h,"Examples:"),h.forEach(t),u=i(m),T(f.$$.fragment,m)},m(m,h){b(m,g,h),e(g,w),b(m,u,h),F(f,m,h),p=!0},p:G,i(m){p||(M(f.$$.fragment,m),p=!0)},o(m){E(f.$$.fragment,m),p=!1},d(m){m&&t(g),m&&t(u),C(f,m)}}}function cgn(k){let g,w,u,f,p;return f=new B({props:{code:`from transformers import AutoConfig, TFAutoModelForNextSentencePrediction

# Download configuration from huggingface.co and cache.
config = AutoConfig.from_pretrained("bert-base-cased")
model = TFAutoModelForNextSentencePrediction.from_config(config)`,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(){g=a("p"),w=o("Examples:"),u=l(),v(f.$$.fragment)},l(m){g=n(m,"P",{});var h=s(g);w=r(h,"Examples:"),h.forEach(t),u=i(m),T(f.$$.fragment,m)},m(m,h){b(m,g,h),e(g,w),b(m,u,h),F(f,m,h),p=!0},p:G,i(m){p||(M(f.$$.fragment,m),p=!0)},o(m){E(f.$$.fragment,m),p=!1},d(m){m&&t(g),m&&t(u),C(f,m)}}}function fgn(k){let g,w,u,f,p;return f=new B({props:{code:`from transformers import AutoConfig, TFAutoModelForNextSentencePrediction

# Download model and configuration from huggingface.co and cache.
model = TFAutoModelForNextSentencePrediction.from_pretrained("bert-base-cased")

# Update configuration during loading
model = TFAutoModelForNextSentencePrediction.from_pretrained("bert-base-cased", output_attentions=True)
model.config.output_attentions

# Loading from a PyTorch checkpoint file instead of a TensorFlow model (slower)
config = AutoConfig.from_pretrained("./pt_model/bert_pt_model_config.json")
model = TFAutoModelForNextSentencePrediction.from_pretrained(
    "./pt_model/bert_pytorch_model.bin", from_pt=True, config=config
)`,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(){g=a("p"),w=o("Examples:"),u=l(),v(f.$$.fragment)},l(m){g=n(m,"P",{});var h=s(g);w=r(h,"Examples:"),h.forEach(t),u=i(m),T(f.$$.fragment,m)},m(m,h){b(m,g,h),e(g,w),b(m,u,h),F(f,m,h),p=!0},p:G,i(m){p||(M(f.$$.fragment,m),p=!0)},o(m){E(f.$$.fragment,m),p=!1},d(m){m&&t(g),m&&t(u),C(f,m)}}}function ggn(k){let g,w,u,f,p;return f=new B({props:{code:`from transformers import AutoConfig, FlaxAutoModelForNextSentencePrediction

# Download configuration from huggingface.co and cache.
config = AutoConfig.from_pretrained("bert-base-cased")
model = FlaxAutoModelForNextSentencePrediction.from_config(config)`,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(){g=a("p"),w=o("Examples:"),u=l(),v(f.$$.fragment)},l(m){g=n(m,"P",{});var h=s(g);w=r(h,"Examples:"),h.forEach(t),u=i(m),T(f.$$.fragment,m)},m(m,h){b(m,g,h),e(g,w),b(m,u,h),F(f,m,h),p=!0},p:G,i(m){p||(M(f.$$.fragment,m),p=!0)},o(m){E(f.$$.fragment,m),p=!1},d(m){m&&t(g),m&&t(u),C(f,m)}}}function hgn(k){let g,w,u,f,p;return f=new B({props:{code:`from transformers import AutoConfig, FlaxAutoModelForNextSentencePrediction

# Download model and configuration from huggingface.co and cache.
model = FlaxAutoModelForNextSentencePrediction.from_pretrained("bert-base-cased")

# Update configuration during loading
model = FlaxAutoModelForNextSentencePrediction.from_pretrained("bert-base-cased", output_attentions=True)
model.config.output_attentions

# Loading from a PyTorch checkpoint file instead of a TensorFlow model (slower)
config = AutoConfig.from_pretrained("./pt_model/bert_pt_model_config.json")
model = FlaxAutoModelForNextSentencePrediction.from_pretrained(
    "./pt_model/bert_pytorch_model.bin", from_pt=True, config=config
)`,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(){g=a("p"),w=o("Examples:"),u=l(),v(f.$$.fragment)},l(m){g=n(m,"P",{});var h=s(g);w=r(h,"Examples:"),h.forEach(t),u=i(m),T(f.$$.fragment,m)},m(m,h){b(m,g,h),e(g,w),b(m,u,h),F(f,m,h),p=!0},p:G,i(m){p||(M(f.$$.fragment,m),p=!0)},o(m){E(f.$$.fragment,m),p=!1},d(m){m&&t(g),m&&t(u),C(f,m)}}}function ugn(k){let g,w,u,f,p;return f=new B({props:{code:`from transformers import AutoConfig, AutoModelForTokenClassification

# Download configuration from huggingface.co and cache.
config = AutoConfig.from_pretrained("bert-base-cased")
model = AutoModelForTokenClassification.from_config(config)`,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(){g=a("p"),w=o("Examples:"),u=l(),v(f.$$.fragment)},l(m){g=n(m,"P",{});var h=s(g);w=r(h,"Examples:"),h.forEach(t),u=i(m),T(f.$$.fragment,m)},m(m,h){b(m,g,h),e(g,w),b(m,u,h),F(f,m,h),p=!0},p:G,i(m){p||(M(f.$$.fragment,m),p=!0)},o(m){E(f.$$.fragment,m),p=!1},d(m){m&&t(g),m&&t(u),C(f,m)}}}function pgn(k){let g,w,u,f,p;return f=new B({props:{code:`from transformers import AutoConfig, AutoModelForTokenClassification

# Download model and configuration from huggingface.co and cache.
model = AutoModelForTokenClassification.from_pretrained("bert-base-cased")

# Update configuration during loading
model = AutoModelForTokenClassification.from_pretrained("bert-base-cased", output_attentions=True)
model.config.output_attentions

# Loading from a TF checkpoint file instead of a PyTorch model (slower)
config = AutoConfig.from_pretrained("./tf_model/bert_tf_model_config.json")
model = AutoModelForTokenClassification.from_pretrained(
    "./tf_model/bert_tf_checkpoint.ckpt.index", from_tf=True, config=config
)`,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(){g=a("p"),w=o("Examples:"),u=l(),v(f.$$.fragment)},l(m){g=n(m,"P",{});var h=s(g);w=r(h,"Examples:"),h.forEach(t),u=i(m),T(f.$$.fragment,m)},m(m,h){b(m,g,h),e(g,w),b(m,u,h),F(f,m,h),p=!0},p:G,i(m){p||(M(f.$$.fragment,m),p=!0)},o(m){E(f.$$.fragment,m),p=!1},d(m){m&&t(g),m&&t(u),C(f,m)}}}function _gn(k){let g,w,u,f,p;return f=new B({props:{code:`from transformers import AutoConfig, TFAutoModelForTokenClassification

# Download configuration from huggingface.co and cache.
config = AutoConfig.from_pretrained("bert-base-cased")
model = TFAutoModelForTokenClassification.from_config(config)`,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(){g=a("p"),w=o("Examples:"),u=l(),v(f.$$.fragment)},l(m){g=n(m,"P",{});var h=s(g);w=r(h,"Examples:"),h.forEach(t),u=i(m),T(f.$$.fragment,m)},m(m,h){b(m,g,h),e(g,w),b(m,u,h),F(f,m,h),p=!0},p:G,i(m){p||(M(f.$$.fragment,m),p=!0)},o(m){E(f.$$.fragment,m),p=!1},d(m){m&&t(g),m&&t(u),C(f,m)}}}function bgn(k){let g,w,u,f,p;return f=new B({props:{code:`from transformers import AutoConfig, TFAutoModelForTokenClassification

# Download model and configuration from huggingface.co and cache.
model = TFAutoModelForTokenClassification.from_pretrained("bert-base-cased")

# Update configuration during loading
model = TFAutoModelForTokenClassification.from_pretrained("bert-base-cased", output_attentions=True)
model.config.output_attentions

# Loading from a PyTorch checkpoint file instead of a TensorFlow model (slower)
config = AutoConfig.from_pretrained("./pt_model/bert_pt_model_config.json")
model = TFAutoModelForTokenClassification.from_pretrained(
    "./pt_model/bert_pytorch_model.bin", from_pt=True, config=config
)`,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(){g=a("p"),w=o("Examples:"),u=l(),v(f.$$.fragment)},l(m){g=n(m,"P",{});var h=s(g);w=r(h,"Examples:"),h.forEach(t),u=i(m),T(f.$$.fragment,m)},m(m,h){b(m,g,h),e(g,w),b(m,u,h),F(f,m,h),p=!0},p:G,i(m){p||(M(f.$$.fragment,m),p=!0)},o(m){E(f.$$.fragment,m),p=!1},d(m){m&&t(g),m&&t(u),C(f,m)}}}function vgn(k){let g,w,u,f,p;return f=new B({props:{code:`from transformers import AutoConfig, FlaxAutoModelForTokenClassification

# Download configuration from huggingface.co and cache.
config = AutoConfig.from_pretrained("bert-base-cased")
model = FlaxAutoModelForTokenClassification.from_config(config)`,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(){g=a("p"),w=o("Examples:"),u=l(),v(f.$$.fragment)},l(m){g=n(m,"P",{});var h=s(g);w=r(h,"Examples:"),h.forEach(t),u=i(m),T(f.$$.fragment,m)},m(m,h){b(m,g,h),e(g,w),b(m,u,h),F(f,m,h),p=!0},p:G,i(m){p||(M(f.$$.fragment,m),p=!0)},o(m){E(f.$$.fragment,m),p=!1},d(m){m&&t(g),m&&t(u),C(f,m)}}}function Tgn(k){let g,w,u,f,p;return f=new B({props:{code:`from transformers import AutoConfig, FlaxAutoModelForTokenClassification

# Download model and configuration from huggingface.co and cache.
model = FlaxAutoModelForTokenClassification.from_pretrained("bert-base-cased")

# Update configuration during loading
model = FlaxAutoModelForTokenClassification.from_pretrained("bert-base-cased", output_attentions=True)
model.config.output_attentions

# Loading from a PyTorch checkpoint file instead of a TensorFlow model (slower)
config = AutoConfig.from_pretrained("./pt_model/bert_pt_model_config.json")
model = FlaxAutoModelForTokenClassification.from_pretrained(
    "./pt_model/bert_pytorch_model.bin", from_pt=True, config=config
)`,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(){g=a("p"),w=o("Examples:"),u=l(),v(f.$$.fragment)},l(m){g=n(m,"P",{});var h=s(g);w=r(h,"Examples:"),h.forEach(t),u=i(m),T(f.$$.fragment,m)},m(m,h){b(m,g,h),e(g,w),b(m,u,h),F(f,m,h),p=!0},p:G,i(m){p||(M(f.$$.fragment,m),p=!0)},o(m){E(f.$$.fragment,m),p=!1},d(m){m&&t(g),m&&t(u),C(f,m)}}}function Fgn(k){let g,w,u,f,p;return f=new B({props:{code:`from transformers import AutoConfig, AutoModelForQuestionAnswering

# Download configuration from huggingface.co and cache.
config = AutoConfig.from_pretrained("bert-base-cased")
model = AutoModelForQuestionAnswering.from_config(config)`,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(){g=a("p"),w=o("Examples:"),u=l(),v(f.$$.fragment)},l(m){g=n(m,"P",{});var h=s(g);w=r(h,"Examples:"),h.forEach(t),u=i(m),T(f.$$.fragment,m)},m(m,h){b(m,g,h),e(g,w),b(m,u,h),F(f,m,h),p=!0},p:G,i(m){p||(M(f.$$.fragment,m),p=!0)},o(m){E(f.$$.fragment,m),p=!1},d(m){m&&t(g),m&&t(u),C(f,m)}}}function Mgn(k){let g,w,u,f,p;return f=new B({props:{code:`from transformers import AutoConfig, AutoModelForQuestionAnswering

# Download model and configuration from huggingface.co and cache.
model = AutoModelForQuestionAnswering.from_pretrained("bert-base-cased")

# Update configuration during loading
model = AutoModelForQuestionAnswering.from_pretrained("bert-base-cased", output_attentions=True)
model.config.output_attentions

# Loading from a TF checkpoint file instead of a PyTorch model (slower)
config = AutoConfig.from_pretrained("./tf_model/bert_tf_model_config.json")
model = AutoModelForQuestionAnswering.from_pretrained(
    "./tf_model/bert_tf_checkpoint.ckpt.index", from_tf=True, config=config
)`,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(){g=a("p"),w=o("Examples:"),u=l(),v(f.$$.fragment)},l(m){g=n(m,"P",{});var h=s(g);w=r(h,"Examples:"),h.forEach(t),u=i(m),T(f.$$.fragment,m)},m(m,h){b(m,g,h),e(g,w),b(m,u,h),F(f,m,h),p=!0},p:G,i(m){p||(M(f.$$.fragment,m),p=!0)},o(m){E(f.$$.fragment,m),p=!1},d(m){m&&t(g),m&&t(u),C(f,m)}}}function Egn(k){let g,w,u,f,p;return f=new B({props:{code:`from transformers import AutoConfig, TFAutoModelForQuestionAnswering

# Download configuration from huggingface.co and cache.
config = AutoConfig.from_pretrained("bert-base-cased")
model = TFAutoModelForQuestionAnswering.from_config(config)`,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(){g=a("p"),w=o("Examples:"),u=l(),v(f.$$.fragment)},l(m){g=n(m,"P",{});var h=s(g);w=r(h,"Examples:"),h.forEach(t),u=i(m),T(f.$$.fragment,m)},m(m,h){b(m,g,h),e(g,w),b(m,u,h),F(f,m,h),p=!0},p:G,i(m){p||(M(f.$$.fragment,m),p=!0)},o(m){E(f.$$.fragment,m),p=!1},d(m){m&&t(g),m&&t(u),C(f,m)}}}function Cgn(k){let g,w,u,f,p;return f=new B({props:{code:`from transformers import AutoConfig, TFAutoModelForQuestionAnswering

# Download model and configuration from huggingface.co and cache.
model = TFAutoModelForQuestionAnswering.from_pretrained("bert-base-cased")

# Update configuration during loading
model = TFAutoModelForQuestionAnswering.from_pretrained("bert-base-cased", output_attentions=True)
model.config.output_attentions

# Loading from a PyTorch checkpoint file instead of a TensorFlow model (slower)
config = AutoConfig.from_pretrained("./pt_model/bert_pt_model_config.json")
model = TFAutoModelForQuestionAnswering.from_pretrained(
    "./pt_model/bert_pytorch_model.bin", from_pt=True, config=config
)`,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(){g=a("p"),w=o("Examples:"),u=l(),v(f.$$.fragment)},l(m){g=n(m,"P",{});var h=s(g);w=r(h,"Examples:"),h.forEach(t),u=i(m),T(f.$$.fragment,m)},m(m,h){b(m,g,h),e(g,w),b(m,u,h),F(f,m,h),p=!0},p:G,i(m){p||(M(f.$$.fragment,m),p=!0)},o(m){E(f.$$.fragment,m),p=!1},d(m){m&&t(g),m&&t(u),C(f,m)}}}function wgn(k){let g,w,u,f,p;return f=new B({props:{code:`from transformers import AutoConfig, FlaxAutoModelForQuestionAnswering

# Download configuration from huggingface.co and cache.
config = AutoConfig.from_pretrained("bert-base-cased")
model = FlaxAutoModelForQuestionAnswering.from_config(config)`,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(){g=a("p"),w=o("Examples:"),u=l(),v(f.$$.fragment)},l(m){g=n(m,"P",{});var h=s(g);w=r(h,"Examples:"),h.forEach(t),u=i(m),T(f.$$.fragment,m)},m(m,h){b(m,g,h),e(g,w),b(m,u,h),F(f,m,h),p=!0},p:G,i(m){p||(M(f.$$.fragment,m),p=!0)},o(m){E(f.$$.fragment,m),p=!1},d(m){m&&t(g),m&&t(u),C(f,m)}}}function Agn(k){let g,w,u,f,p;return f=new B({props:{code:`from transformers import AutoConfig, FlaxAutoModelForQuestionAnswering

# Download model and configuration from huggingface.co and cache.
model = FlaxAutoModelForQuestionAnswering.from_pretrained("bert-base-cased")

# Update configuration during loading
model = FlaxAutoModelForQuestionAnswering.from_pretrained("bert-base-cased", output_attentions=True)
model.config.output_attentions

# Loading from a PyTorch checkpoint file instead of a TensorFlow model (slower)
config = AutoConfig.from_pretrained("./pt_model/bert_pt_model_config.json")
model = FlaxAutoModelForQuestionAnswering.from_pretrained(
    "./pt_model/bert_pytorch_model.bin", from_pt=True, config=config
)`,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(){g=a("p"),w=o("Examples:"),u=l(),v(f.$$.fragment)},l(m){g=n(m,"P",{});var h=s(g);w=r(h,"Examples:"),h.forEach(t),u=i(m),T(f.$$.fragment,m)},m(m,h){b(m,g,h),e(g,w),b(m,u,h),F(f,m,h),p=!0},p:G,i(m){p||(M(f.$$.fragment,m),p=!0)},o(m){E(f.$$.fragment,m),p=!1},d(m){m&&t(g),m&&t(u),C(f,m)}}}function Lgn(k){let g,w,u,f,p;return f=new B({props:{code:`from transformers import AutoConfig, AutoModelForDepthEstimation

# Download configuration from huggingface.co and cache.
config = AutoConfig.from_pretrained("bert-base-cased")
model = AutoModelForDepthEstimation.from_config(config)`,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(){g=a("p"),w=o("Examples:"),u=l(),v(f.$$.fragment)},l(m){g=n(m,"P",{});var h=s(g);w=r(h,"Examples:"),h.forEach(t),u=i(m),T(f.$$.fragment,m)},m(m,h){b(m,g,h),e(g,w),b(m,u,h),F(f,m,h),p=!0},p:G,i(m){p||(M(f.$$.fragment,m),p=!0)},o(m){E(f.$$.fragment,m),p=!1},d(m){m&&t(g),m&&t(u),C(f,m)}}}function ygn(k){let g,w,u,f,p;return f=new B({props:{code:`from transformers import AutoConfig, AutoModelForDepthEstimation

# Download model and configuration from huggingface.co and cache.
model = AutoModelForDepthEstimation.from_pretrained("bert-base-cased")

# Update configuration during loading
model = AutoModelForDepthEstimation.from_pretrained("bert-base-cased", output_attentions=True)
model.config.output_attentions

# Loading from a TF checkpoint file instead of a PyTorch model (slower)
config = AutoConfig.from_pretrained("./tf_model/bert_tf_model_config.json")
model = AutoModelForDepthEstimation.from_pretrained(
    "./tf_model/bert_tf_checkpoint.ckpt.index", from_tf=True, config=config
)`,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(){g=a("p"),w=o("Examples:"),u=l(),v(f.$$.fragment)},l(m){g=n(m,"P",{});var h=s(g);w=r(h,"Examples:"),h.forEach(t),u=i(m),T(f.$$.fragment,m)},m(m,h){b(m,g,h),e(g,w),b(m,u,h),F(f,m,h),p=!0},p:G,i(m){p||(M(f.$$.fragment,m),p=!0)},o(m){E(f.$$.fragment,m),p=!1},d(m){m&&t(g),m&&t(u),C(f,m)}}}function xgn(k){let g,w,u,f,p;return f=new B({props:{code:`from transformers import AutoConfig, AutoModelForImageClassification

# Download configuration from huggingface.co and cache.
config = AutoConfig.from_pretrained("bert-base-cased")
model = AutoModelForImageClassification.from_config(config)`,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(){g=a("p"),w=o("Examples:"),u=l(),v(f.$$.fragment)},l(m){g=n(m,"P",{});var h=s(g);w=r(h,"Examples:"),h.forEach(t),u=i(m),T(f.$$.fragment,m)},m(m,h){b(m,g,h),e(g,w),b(m,u,h),F(f,m,h),p=!0},p:G,i(m){p||(M(f.$$.fragment,m),p=!0)},o(m){E(f.$$.fragment,m),p=!1},d(m){m&&t(g),m&&t(u),C(f,m)}}}function kgn(k){let g,w,u,f,p;return f=new B({props:{code:`from transformers import AutoConfig, AutoModelForImageClassification

# Download model and configuration from huggingface.co and cache.
model = AutoModelForImageClassification.from_pretrained("bert-base-cased")

# Update configuration during loading
model = AutoModelForImageClassification.from_pretrained("bert-base-cased", output_attentions=True)
model.config.output_attentions

# Loading from a TF checkpoint file instead of a PyTorch model (slower)
config = AutoConfig.from_pretrained("./tf_model/bert_tf_model_config.json")
model = AutoModelForImageClassification.from_pretrained(
    "./tf_model/bert_tf_checkpoint.ckpt.index", from_tf=True, config=config
)`,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(){g=a("p"),w=o("Examples:"),u=l(),v(f.$$.fragment)},l(m){g=n(m,"P",{});var h=s(g);w=r(h,"Examples:"),h.forEach(t),u=i(m),T(f.$$.fragment,m)},m(m,h){b(m,g,h),e(g,w),b(m,u,h),F(f,m,h),p=!0},p:G,i(m){p||(M(f.$$.fragment,m),p=!0)},o(m){E(f.$$.fragment,m),p=!1},d(m){m&&t(g),m&&t(u),C(f,m)}}}function $gn(k){let g,w,u,f,p;return f=new B({props:{code:`from transformers import AutoConfig, TFAutoModelForImageClassification

# Download configuration from huggingface.co and cache.
config = AutoConfig.from_pretrained("bert-base-cased")
model = TFAutoModelForImageClassification.from_config(config)`,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(){g=a("p"),w=o("Examples:"),u=l(),v(f.$$.fragment)},l(m){g=n(m,"P",{});var h=s(g);w=r(h,"Examples:"),h.forEach(t),u=i(m),T(f.$$.fragment,m)},m(m,h){b(m,g,h),e(g,w),b(m,u,h),F(f,m,h),p=!0},p:G,i(m){p||(M(f.$$.fragment,m),p=!0)},o(m){E(f.$$.fragment,m),p=!1},d(m){m&&t(g),m&&t(u),C(f,m)}}}function Sgn(k){let g,w,u,f,p;return f=new B({props:{code:`from transformers import AutoConfig, TFAutoModelForImageClassification

# Download model and configuration from huggingface.co and cache.
model = TFAutoModelForImageClassification.from_pretrained("bert-base-cased")

# Update configuration during loading
model = TFAutoModelForImageClassification.from_pretrained("bert-base-cased", output_attentions=True)
model.config.output_attentions

# Loading from a PyTorch checkpoint file instead of a TensorFlow model (slower)
config = AutoConfig.from_pretrained("./pt_model/bert_pt_model_config.json")
model = TFAutoModelForImageClassification.from_pretrained(
    "./pt_model/bert_pytorch_model.bin", from_pt=True, config=config
)`,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(){g=a("p"),w=o("Examples:"),u=l(),v(f.$$.fragment)},l(m){g=n(m,"P",{});var h=s(g);w=r(h,"Examples:"),h.forEach(t),u=i(m),T(f.$$.fragment,m)},m(m,h){b(m,g,h),e(g,w),b(m,u,h),F(f,m,h),p=!0},p:G,i(m){p||(M(f.$$.fragment,m),p=!0)},o(m){E(f.$$.fragment,m),p=!1},d(m){m&&t(g),m&&t(u),C(f,m)}}}function Rgn(k){let g,w,u,f,p;return f=new B({props:{code:`from transformers import AutoConfig, FlaxAutoModelForImageClassification

# Download configuration from huggingface.co and cache.
config = AutoConfig.from_pretrained("bert-base-cased")
model = FlaxAutoModelForImageClassification.from_config(config)`,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(){g=a("p"),w=o("Examples:"),u=l(),v(f.$$.fragment)},l(m){g=n(m,"P",{});var h=s(g);w=r(h,"Examples:"),h.forEach(t),u=i(m),T(f.$$.fragment,m)},m(m,h){b(m,g,h),e(g,w),b(m,u,h),F(f,m,h),p=!0},p:G,i(m){p||(M(f.$$.fragment,m),p=!0)},o(m){E(f.$$.fragment,m),p=!1},d(m){m&&t(g),m&&t(u),C(f,m)}}}function Pgn(k){let g,w,u,f,p;return f=new B({props:{code:`from transformers import AutoConfig, FlaxAutoModelForImageClassification

# Download model and configuration from huggingface.co and cache.
model = FlaxAutoModelForImageClassification.from_pretrained("bert-base-cased")

# Update configuration during loading
model = FlaxAutoModelForImageClassification.from_pretrained("bert-base-cased", output_attentions=True)
model.config.output_attentions

# Loading from a PyTorch checkpoint file instead of a TensorFlow model (slower)
config = AutoConfig.from_pretrained("./pt_model/bert_pt_model_config.json")
model = FlaxAutoModelForImageClassification.from_pretrained(
    "./pt_model/bert_pytorch_model.bin", from_pt=True, config=config
)`,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(){g=a("p"),w=o("Examples:"),u=l(),v(f.$$.fragment)},l(m){g=n(m,"P",{});var h=s(g);w=r(h,"Examples:"),h.forEach(t),u=i(m),T(f.$$.fragment,m)},m(m,h){b(m,g,h),e(g,w),b(m,u,h),F(f,m,h),p=!0},p:G,i(m){p||(M(f.$$.fragment,m),p=!0)},o(m){E(f.$$.fragment,m),p=!1},d(m){m&&t(g),m&&t(u),C(f,m)}}}function Ign(k){let g,w,u,f,p;return f=new B({props:{code:`from transformers import AutoConfig, AutoModelForVideoClassification

# Download configuration from huggingface.co and cache.
config = AutoConfig.from_pretrained("bert-base-cased")
model = AutoModelForVideoClassification.from_config(config)`,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(){g=a("p"),w=o("Examples:"),u=l(),v(f.$$.fragment)},l(m){g=n(m,"P",{});var h=s(g);w=r(h,"Examples:"),h.forEach(t),u=i(m),T(f.$$.fragment,m)},m(m,h){b(m,g,h),e(g,w),b(m,u,h),F(f,m,h),p=!0},p:G,i(m){p||(M(f.$$.fragment,m),p=!0)},o(m){E(f.$$.fragment,m),p=!1},d(m){m&&t(g),m&&t(u),C(f,m)}}}function Bgn(k){let g,w,u,f,p;return f=new B({props:{code:`from transformers import AutoConfig, AutoModelForVideoClassification

# Download model and configuration from huggingface.co and cache.
model = AutoModelForVideoClassification.from_pretrained("bert-base-cased")

# Update configuration during loading
model = AutoModelForVideoClassification.from_pretrained("bert-base-cased", output_attentions=True)
model.config.output_attentions

# Loading from a TF checkpoint file instead of a PyTorch model (slower)
config = AutoConfig.from_pretrained("./tf_model/bert_tf_model_config.json")
model = AutoModelForVideoClassification.from_pretrained(
    "./tf_model/bert_tf_checkpoint.ckpt.index", from_tf=True, config=config
)`,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(){g=a("p"),w=o("Examples:"),u=l(),v(f.$$.fragment)},l(m){g=n(m,"P",{});var h=s(g);w=r(h,"Examples:"),h.forEach(t),u=i(m),T(f.$$.fragment,m)},m(m,h){b(m,g,h),e(g,w),b(m,u,h),F(f,m,h),p=!0},p:G,i(m){p||(M(f.$$.fragment,m),p=!0)},o(m){E(f.$$.fragment,m),p=!1},d(m){m&&t(g),m&&t(u),C(f,m)}}}function Ngn(k){let g,w,u,f,p;return f=new B({props:{code:`from transformers import AutoConfig, AutoModelForMaskedImageModeling

# Download configuration from huggingface.co and cache.
config = AutoConfig.from_pretrained("bert-base-cased")
model = AutoModelForMaskedImageModeling.from_config(config)`,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(){g=a("p"),w=o("Examples:"),u=l(),v(f.$$.fragment)},l(m){g=n(m,"P",{});var h=s(g);w=r(h,"Examples:"),h.forEach(t),u=i(m),T(f.$$.fragment,m)},m(m,h){b(m,g,h),e(g,w),b(m,u,h),F(f,m,h),p=!0},p:G,i(m){p||(M(f.$$.fragment,m),p=!0)},o(m){E(f.$$.fragment,m),p=!1},d(m){m&&t(g),m&&t(u),C(f,m)}}}function Dgn(k){let g,w,u,f,p;return f=new B({props:{code:`from transformers import AutoConfig, AutoModelForMaskedImageModeling

# Download model and configuration from huggingface.co and cache.
model = AutoModelForMaskedImageModeling.from_pretrained("bert-base-cased")

# Update configuration during loading
model = AutoModelForMaskedImageModeling.from_pretrained("bert-base-cased", output_attentions=True)
model.config.output_attentions

# Loading from a TF checkpoint file instead of a PyTorch model (slower)
config = AutoConfig.from_pretrained("./tf_model/bert_tf_model_config.json")
model = AutoModelForMaskedImageModeling.from_pretrained(
    "./tf_model/bert_tf_checkpoint.ckpt.index", from_tf=True, config=config
)`,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(){g=a("p"),w=o("Examples:"),u=l(),v(f.$$.fragment)},l(m){g=n(m,"P",{});var h=s(g);w=r(h,"Examples:"),h.forEach(t),u=i(m),T(f.$$.fragment,m)},m(m,h){b(m,g,h),e(g,w),b(m,u,h),F(f,m,h),p=!0},p:G,i(m){p||(M(f.$$.fragment,m),p=!0)},o(m){E(f.$$.fragment,m),p=!1},d(m){m&&t(g),m&&t(u),C(f,m)}}}function Ggn(k){let g,w,u,f,p;return f=new B({props:{code:`from transformers import AutoConfig, AutoModelForObjectDetection

# Download configuration from huggingface.co and cache.
config = AutoConfig.from_pretrained("bert-base-cased")
model = AutoModelForObjectDetection.from_config(config)`,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(){g=a("p"),w=o("Examples:"),u=l(),v(f.$$.fragment)},l(m){g=n(m,"P",{});var h=s(g);w=r(h,"Examples:"),h.forEach(t),u=i(m),T(f.$$.fragment,m)},m(m,h){b(m,g,h),e(g,w),b(m,u,h),F(f,m,h),p=!0},p:G,i(m){p||(M(f.$$.fragment,m),p=!0)},o(m){E(f.$$.fragment,m),p=!1},d(m){m&&t(g),m&&t(u),C(f,m)}}}function qgn(k){let g,w,u,f,p;return f=new B({props:{code:`from transformers import AutoConfig, AutoModelForObjectDetection

# Download model and configuration from huggingface.co and cache.
model = AutoModelForObjectDetection.from_pretrained("bert-base-cased")

# Update configuration during loading
model = AutoModelForObjectDetection.from_pretrained("bert-base-cased", output_attentions=True)
model.config.output_attentions

# Loading from a TF checkpoint file instead of a PyTorch model (slower)
config = AutoConfig.from_pretrained("./tf_model/bert_tf_model_config.json")
model = AutoModelForObjectDetection.from_pretrained(
    "./tf_model/bert_tf_checkpoint.ckpt.index", from_tf=True, config=config
)`,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(){g=a("p"),w=o("Examples:"),u=l(),v(f.$$.fragment)},l(m){g=n(m,"P",{});var h=s(g);w=r(h,"Examples:"),h.forEach(t),u=i(m),T(f.$$.fragment,m)},m(m,h){b(m,g,h),e(g,w),b(m,u,h),F(f,m,h),p=!0},p:G,i(m){p||(M(f.$$.fragment,m),p=!0)},o(m){E(f.$$.fragment,m),p=!1},d(m){m&&t(g),m&&t(u),C(f,m)}}}function jgn(k){let g,w,u,f,p;return f=new B({props:{code:`from transformers import AutoConfig, AutoModelForImageSegmentation

# Download configuration from huggingface.co and cache.
config = AutoConfig.from_pretrained("bert-base-cased")
model = AutoModelForImageSegmentation.from_config(config)`,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(){g=a("p"),w=o("Examples:"),u=l(),v(f.$$.fragment)},l(m){g=n(m,"P",{});var h=s(g);w=r(h,"Examples:"),h.forEach(t),u=i(m),T(f.$$.fragment,m)},m(m,h){b(m,g,h),e(g,w),b(m,u,h),F(f,m,h),p=!0},p:G,i(m){p||(M(f.$$.fragment,m),p=!0)},o(m){E(f.$$.fragment,m),p=!1},d(m){m&&t(g),m&&t(u),C(f,m)}}}function Ogn(k){let g,w,u,f,p;return f=new B({props:{code:`from transformers import AutoConfig, AutoModelForImageSegmentation

# Download model and configuration from huggingface.co and cache.
model = AutoModelForImageSegmentation.from_pretrained("bert-base-cased")

# Update configuration during loading
model = AutoModelForImageSegmentation.from_pretrained("bert-base-cased", output_attentions=True)
model.config.output_attentions

# Loading from a TF checkpoint file instead of a PyTorch model (slower)
config = AutoConfig.from_pretrained("./tf_model/bert_tf_model_config.json")
model = AutoModelForImageSegmentation.from_pretrained(
    "./tf_model/bert_tf_checkpoint.ckpt.index", from_tf=True, config=config
)`,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(){g=a("p"),w=o("Examples:"),u=l(),v(f.$$.fragment)},l(m){g=n(m,"P",{});var h=s(g);w=r(h,"Examples:"),h.forEach(t),u=i(m),T(f.$$.fragment,m)},m(m,h){b(m,g,h),e(g,w),b(m,u,h),F(f,m,h),p=!0},p:G,i(m){p||(M(f.$$.fragment,m),p=!0)},o(m){E(f.$$.fragment,m),p=!1},d(m){m&&t(g),m&&t(u),C(f,m)}}}function Vgn(k){let g,w,u,f,p;return f=new B({props:{code:`from transformers import AutoConfig, AutoModelForSemanticSegmentation

# Download configuration from huggingface.co and cache.
config = AutoConfig.from_pretrained("bert-base-cased")
model = AutoModelForSemanticSegmentation.from_config(config)`,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(){g=a("p"),w=o("Examples:"),u=l(),v(f.$$.fragment)},l(m){g=n(m,"P",{});var h=s(g);w=r(h,"Examples:"),h.forEach(t),u=i(m),T(f.$$.fragment,m)},m(m,h){b(m,g,h),e(g,w),b(m,u,h),F(f,m,h),p=!0},p:G,i(m){p||(M(f.$$.fragment,m),p=!0)},o(m){E(f.$$.fragment,m),p=!1},d(m){m&&t(g),m&&t(u),C(f,m)}}}function Xgn(k){let g,w,u,f,p;return f=new B({props:{code:`from transformers import AutoConfig, AutoModelForSemanticSegmentation

# Download model and configuration from huggingface.co and cache.
model = AutoModelForSemanticSegmentation.from_pretrained("bert-base-cased")

# Update configuration during loading
model = AutoModelForSemanticSegmentation.from_pretrained("bert-base-cased", output_attentions=True)
model.config.output_attentions

# Loading from a TF checkpoint file instead of a PyTorch model (slower)
config = AutoConfig.from_pretrained("./tf_model/bert_tf_model_config.json")
model = AutoModelForSemanticSegmentation.from_pretrained(
    "./tf_model/bert_tf_checkpoint.ckpt.index", from_tf=True, config=config
)`,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(){g=a("p"),w=o("Examples:"),u=l(),v(f.$$.fragment)},l(m){g=n(m,"P",{});var h=s(g);w=r(h,"Examples:"),h.forEach(t),u=i(m),T(f.$$.fragment,m)},m(m,h){b(m,g,h),e(g,w),b(m,u,h),F(f,m,h),p=!0},p:G,i(m){p||(M(f.$$.fragment,m),p=!0)},o(m){E(f.$$.fragment,m),p=!1},d(m){m&&t(g),m&&t(u),C(f,m)}}}function zgn(k){let g,w,u,f,p;return f=new B({props:{code:`from transformers import AutoConfig, TFAutoModelForSemanticSegmentation

# Download configuration from huggingface.co and cache.
config = AutoConfig.from_pretrained("bert-base-cased")
model = TFAutoModelForSemanticSegmentation.from_config(config)`,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(){g=a("p"),w=o("Examples:"),u=l(),v(f.$$.fragment)},l(m){g=n(m,"P",{});var h=s(g);w=r(h,"Examples:"),h.forEach(t),u=i(m),T(f.$$.fragment,m)},m(m,h){b(m,g,h),e(g,w),b(m,u,h),F(f,m,h),p=!0},p:G,i(m){p||(M(f.$$.fragment,m),p=!0)},o(m){E(f.$$.fragment,m),p=!1},d(m){m&&t(g),m&&t(u),C(f,m)}}}function Wgn(k){let g,w,u,f,p;return f=new B({props:{code:`from transformers import AutoConfig, TFAutoModelForSemanticSegmentation

# Download model and configuration from huggingface.co and cache.
model = TFAutoModelForSemanticSegmentation.from_pretrained("bert-base-cased")

# Update configuration during loading
model = TFAutoModelForSemanticSegmentation.from_pretrained("bert-base-cased", output_attentions=True)
model.config.output_attentions

# Loading from a PyTorch checkpoint file instead of a TensorFlow model (slower)
config = AutoConfig.from_pretrained("./pt_model/bert_pt_model_config.json")
model = TFAutoModelForSemanticSegmentation.from_pretrained(
    "./pt_model/bert_pytorch_model.bin", from_pt=True, config=config
)`,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(){g=a("p"),w=o("Examples:"),u=l(),v(f.$$.fragment)},l(m){g=n(m,"P",{});var h=s(g);w=r(h,"Examples:"),h.forEach(t),u=i(m),T(f.$$.fragment,m)},m(m,h){b(m,g,h),e(g,w),b(m,u,h),F(f,m,h),p=!0},p:G,i(m){p||(M(f.$$.fragment,m),p=!0)},o(m){E(f.$$.fragment,m),p=!1},d(m){m&&t(g),m&&t(u),C(f,m)}}}function Qgn(k){let g,w,u,f,p;return f=new B({props:{code:`from transformers import AutoConfig, AutoModelForInstanceSegmentation

# Download configuration from huggingface.co and cache.
config = AutoConfig.from_pretrained("bert-base-cased")
model = AutoModelForInstanceSegmentation.from_config(config)`,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(){g=a("p"),w=o("Examples:"),u=l(),v(f.$$.fragment)},l(m){g=n(m,"P",{});var h=s(g);w=r(h,"Examples:"),h.forEach(t),u=i(m),T(f.$$.fragment,m)},m(m,h){b(m,g,h),e(g,w),b(m,u,h),F(f,m,h),p=!0},p:G,i(m){p||(M(f.$$.fragment,m),p=!0)},o(m){E(f.$$.fragment,m),p=!1},d(m){m&&t(g),m&&t(u),C(f,m)}}}function Ugn(k){let g,w,u,f,p;return f=new B({props:{code:`from transformers import AutoConfig, AutoModelForInstanceSegmentation

# Download model and configuration from huggingface.co and cache.
model = AutoModelForInstanceSegmentation.from_pretrained("bert-base-cased")

# Update configuration during loading
model = AutoModelForInstanceSegmentation.from_pretrained("bert-base-cased", output_attentions=True)
model.config.output_attentions

# Loading from a TF checkpoint file instead of a PyTorch model (slower)
config = AutoConfig.from_pretrained("./tf_model/bert_tf_model_config.json")
model = AutoModelForInstanceSegmentation.from_pretrained(
    "./tf_model/bert_tf_checkpoint.ckpt.index", from_tf=True, config=config
)`,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(){g=a("p"),w=o("Examples:"),u=l(),v(f.$$.fragment)},l(m){g=n(m,"P",{});var h=s(g);w=r(h,"Examples:"),h.forEach(t),u=i(m),T(f.$$.fragment,m)},m(m,h){b(m,g,h),e(g,w),b(m,u,h),F(f,m,h),p=!0},p:G,i(m){p||(M(f.$$.fragment,m),p=!0)},o(m){E(f.$$.fragment,m),p=!1},d(m){m&&t(g),m&&t(u),C(f,m)}}}function Hgn(k){let g,w,u,f,p;return f=new B({props:{code:`from transformers import AutoConfig, AutoModelForUniversalSegmentation

# Download configuration from huggingface.co and cache.
config = AutoConfig.from_pretrained("bert-base-cased")
model = AutoModelForUniversalSegmentation.from_config(config)`,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(){g=a("p"),w=o("Examples:"),u=l(),v(f.$$.fragment)},l(m){g=n(m,"P",{});var h=s(g);w=r(h,"Examples:"),h.forEach(t),u=i(m),T(f.$$.fragment,m)},m(m,h){b(m,g,h),e(g,w),b(m,u,h),F(f,m,h),p=!0},p:G,i(m){p||(M(f.$$.fragment,m),p=!0)},o(m){E(f.$$.fragment,m),p=!1},d(m){m&&t(g),m&&t(u),C(f,m)}}}function Jgn(k){let g,w,u,f,p;return f=new B({props:{code:`from transformers import AutoConfig, AutoModelForUniversalSegmentation

# Download model and configuration from huggingface.co and cache.
model = AutoModelForUniversalSegmentation.from_pretrained("bert-base-cased")

# Update configuration during loading
model = AutoModelForUniversalSegmentation.from_pretrained("bert-base-cased", output_attentions=True)
model.config.output_attentions

# Loading from a TF checkpoint file instead of a PyTorch model (slower)
config = AutoConfig.from_pretrained("./tf_model/bert_tf_model_config.json")
model = AutoModelForUniversalSegmentation.from_pretrained(
    "./tf_model/bert_tf_checkpoint.ckpt.index", from_tf=True, config=config
)`,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(){g=a("p"),w=o("Examples:"),u=l(),v(f.$$.fragment)},l(m){g=n(m,"P",{});var h=s(g);w=r(h,"Examples:"),h.forEach(t),u=i(m),T(f.$$.fragment,m)},m(m,h){b(m,g,h),e(g,w),b(m,u,h),F(f,m,h),p=!0},p:G,i(m){p||(M(f.$$.fragment,m),p=!0)},o(m){E(f.$$.fragment,m),p=!1},d(m){m&&t(g),m&&t(u),C(f,m)}}}function Ygn(k){let g,w,u,f,p;return f=new B({props:{code:`from transformers import AutoConfig, AutoModelForZeroShotObjectDetection

# Download configuration from huggingface.co and cache.
config = AutoConfig.from_pretrained("bert-base-cased")
model = AutoModelForZeroShotObjectDetection.from_config(config)`,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(){g=a("p"),w=o("Examples:"),u=l(),v(f.$$.fragment)},l(m){g=n(m,"P",{});var h=s(g);w=r(h,"Examples:"),h.forEach(t),u=i(m),T(f.$$.fragment,m)},m(m,h){b(m,g,h),e(g,w),b(m,u,h),F(f,m,h),p=!0},p:G,i(m){p||(M(f.$$.fragment,m),p=!0)},o(m){E(f.$$.fragment,m),p=!1},d(m){m&&t(g),m&&t(u),C(f,m)}}}function Zgn(k){let g,w,u,f,p;return f=new B({props:{code:`from transformers import AutoConfig, AutoModelForZeroShotObjectDetection

# Download model and configuration from huggingface.co and cache.
model = AutoModelForZeroShotObjectDetection.from_pretrained("bert-base-cased")

# Update configuration during loading
model = AutoModelForZeroShotObjectDetection.from_pretrained("bert-base-cased", output_attentions=True)
model.config.output_attentions

# Loading from a TF checkpoint file instead of a PyTorch model (slower)
config = AutoConfig.from_pretrained("./tf_model/bert_tf_model_config.json")
model = AutoModelForZeroShotObjectDetection.from_pretrained(
    "./tf_model/bert_tf_checkpoint.ckpt.index", from_tf=True, config=config
)`,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(){g=a("p"),w=o("Examples:"),u=l(),v(f.$$.fragment)},l(m){g=n(m,"P",{});var h=s(g);w=r(h,"Examples:"),h.forEach(t),u=i(m),T(f.$$.fragment,m)},m(m,h){b(m,g,h),e(g,w),b(m,u,h),F(f,m,h),p=!0},p:G,i(m){p||(M(f.$$.fragment,m),p=!0)},o(m){E(f.$$.fragment,m),p=!1},d(m){m&&t(g),m&&t(u),C(f,m)}}}function Kgn(k){let g,w,u,f,p;return f=new B({props:{code:`from transformers import AutoConfig, AutoModelForAudioClassification

# Download configuration from huggingface.co and cache.
config = AutoConfig.from_pretrained("bert-base-cased")
model = AutoModelForAudioClassification.from_config(config)`,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(){g=a("p"),w=o("Examples:"),u=l(),v(f.$$.fragment)},l(m){g=n(m,"P",{});var h=s(g);w=r(h,"Examples:"),h.forEach(t),u=i(m),T(f.$$.fragment,m)},m(m,h){b(m,g,h),e(g,w),b(m,u,h),F(f,m,h),p=!0},p:G,i(m){p||(M(f.$$.fragment,m),p=!0)},o(m){E(f.$$.fragment,m),p=!1},d(m){m&&t(g),m&&t(u),C(f,m)}}}function ehn(k){let g,w,u,f,p;return f=new B({props:{code:`from transformers import AutoConfig, AutoModelForAudioClassification

# Download model and configuration from huggingface.co and cache.
model = AutoModelForAudioClassification.from_pretrained("bert-base-cased")

# Update configuration during loading
model = AutoModelForAudioClassification.from_pretrained("bert-base-cased", output_attentions=True)
model.config.output_attentions

# Loading from a TF checkpoint file instead of a PyTorch model (slower)
config = AutoConfig.from_pretrained("./tf_model/bert_tf_model_config.json")
model = AutoModelForAudioClassification.from_pretrained(
    "./tf_model/bert_tf_checkpoint.ckpt.index", from_tf=True, config=config
)`,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(){g=a("p"),w=o("Examples:"),u=l(),v(f.$$.fragment)},l(m){g=n(m,"P",{});var h=s(g);w=r(h,"Examples:"),h.forEach(t),u=i(m),T(f.$$.fragment,m)},m(m,h){b(m,g,h),e(g,w),b(m,u,h),F(f,m,h),p=!0},p:G,i(m){p||(M(f.$$.fragment,m),p=!0)},o(m){E(f.$$.fragment,m),p=!1},d(m){m&&t(g),m&&t(u),C(f,m)}}}function ohn(k){let g,w,u,f,p;return f=new B({props:{code:`from transformers import AutoConfig, AutoModelForAudioFrameClassification

# Download configuration from huggingface.co and cache.
config = AutoConfig.from_pretrained("bert-base-cased")
model = AutoModelForAudioFrameClassification.from_config(config)`,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(){g=a("p"),w=o("Examples:"),u=l(),v(f.$$.fragment)},l(m){g=n(m,"P",{});var h=s(g);w=r(h,"Examples:"),h.forEach(t),u=i(m),T(f.$$.fragment,m)},m(m,h){b(m,g,h),e(g,w),b(m,u,h),F(f,m,h),p=!0},p:G,i(m){p||(M(f.$$.fragment,m),p=!0)},o(m){E(f.$$.fragment,m),p=!1},d(m){m&&t(g),m&&t(u),C(f,m)}}}function rhn(k){let g,w,u,f,p;return f=new B({props:{code:`from transformers import AutoConfig, AutoModelForAudioFrameClassification

# Download model and configuration from huggingface.co and cache.
model = AutoModelForAudioFrameClassification.from_pretrained("bert-base-cased")

# Update configuration during loading
model = AutoModelForAudioFrameClassification.from_pretrained("bert-base-cased", output_attentions=True)
model.config.output_attentions

# Loading from a TF checkpoint file instead of a PyTorch model (slower)
config = AutoConfig.from_pretrained("./tf_model/bert_tf_model_config.json")
model = AutoModelForAudioFrameClassification.from_pretrained(
    "./tf_model/bert_tf_checkpoint.ckpt.index", from_tf=True, config=config
)`,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(){g=a("p"),w=o("Examples:"),u=l(),v(f.$$.fragment)},l(m){g=n(m,"P",{});var h=s(g);w=r(h,"Examples:"),h.forEach(t),u=i(m),T(f.$$.fragment,m)},m(m,h){b(m,g,h),e(g,w),b(m,u,h),F(f,m,h),p=!0},p:G,i(m){p||(M(f.$$.fragment,m),p=!0)},o(m){E(f.$$.fragment,m),p=!1},d(m){m&&t(g),m&&t(u),C(f,m)}}}function thn(k){let g,w,u,f,p;return f=new B({props:{code:`from transformers import AutoConfig, AutoModelForCTC

# Download configuration from huggingface.co and cache.
config = AutoConfig.from_pretrained("bert-base-cased")
model = AutoModelForCTC.from_config(config)`,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(){g=a("p"),w=o("Examples:"),u=l(),v(f.$$.fragment)},l(m){g=n(m,"P",{});var h=s(g);w=r(h,"Examples:"),h.forEach(t),u=i(m),T(f.$$.fragment,m)},m(m,h){b(m,g,h),e(g,w),b(m,u,h),F(f,m,h),p=!0},p:G,i(m){p||(M(f.$$.fragment,m),p=!0)},o(m){E(f.$$.fragment,m),p=!1},d(m){m&&t(g),m&&t(u),C(f,m)}}}function ahn(k){let g,w,u,f,p;return f=new B({props:{code:`from transformers import AutoConfig, AutoModelForCTC

# Download model and configuration from huggingface.co and cache.
model = AutoModelForCTC.from_pretrained("bert-base-cased")

# Update configuration during loading
model = AutoModelForCTC.from_pretrained("bert-base-cased", output_attentions=True)
model.config.output_attentions

# Loading from a TF checkpoint file instead of a PyTorch model (slower)
config = AutoConfig.from_pretrained("./tf_model/bert_tf_model_config.json")
model = AutoModelForCTC.from_pretrained(
    "./tf_model/bert_tf_checkpoint.ckpt.index", from_tf=True, config=config
)`,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(){g=a("p"),w=o("Examples:"),u=l(),v(f.$$.fragment)},l(m){g=n(m,"P",{});var h=s(g);w=r(h,"Examples:"),h.forEach(t),u=i(m),T(f.$$.fragment,m)},m(m,h){b(m,g,h),e(g,w),b(m,u,h),F(f,m,h),p=!0},p:G,i(m){p||(M(f.$$.fragment,m),p=!0)},o(m){E(f.$$.fragment,m),p=!1},d(m){m&&t(g),m&&t(u),C(f,m)}}}function nhn(k){let g,w,u,f,p;return f=new B({props:{code:`from transformers import AutoConfig, AutoModelForSpeechSeq2Seq

# Download configuration from huggingface.co and cache.
config = AutoConfig.from_pretrained("bert-base-cased")
model = AutoModelForSpeechSeq2Seq.from_config(config)`,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(){g=a("p"),w=o("Examples:"),u=l(),v(f.$$.fragment)},l(m){g=n(m,"P",{});var h=s(g);w=r(h,"Examples:"),h.forEach(t),u=i(m),T(f.$$.fragment,m)},m(m,h){b(m,g,h),e(g,w),b(m,u,h),F(f,m,h),p=!0},p:G,i(m){p||(M(f.$$.fragment,m),p=!0)},o(m){E(f.$$.fragment,m),p=!1},d(m){m&&t(g),m&&t(u),C(f,m)}}}function shn(k){let g,w,u,f,p;return f=new B({props:{code:`from transformers import AutoConfig, AutoModelForSpeechSeq2Seq

# Download model and configuration from huggingface.co and cache.
model = AutoModelForSpeechSeq2Seq.from_pretrained("bert-base-cased")

# Update configuration during loading
model = AutoModelForSpeechSeq2Seq.from_pretrained("bert-base-cased", output_attentions=True)
model.config.output_attentions

# Loading from a TF checkpoint file instead of a PyTorch model (slower)
config = AutoConfig.from_pretrained("./tf_model/bert_tf_model_config.json")
model = AutoModelForSpeechSeq2Seq.from_pretrained(
    "./tf_model/bert_tf_checkpoint.ckpt.index", from_tf=True, config=config
)`,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(){g=a("p"),w=o("Examples:"),u=l(),v(f.$$.fragment)},l(m){g=n(m,"P",{});var h=s(g);w=r(h,"Examples:"),h.forEach(t),u=i(m),T(f.$$.fragment,m)},m(m,h){b(m,g,h),e(g,w),b(m,u,h),F(f,m,h),p=!0},p:G,i(m){p||(M(f.$$.fragment,m),p=!0)},o(m){E(f.$$.fragment,m),p=!1},d(m){m&&t(g),m&&t(u),C(f,m)}}}function lhn(k){let g,w,u,f,p;return f=new B({props:{code:`from transformers import AutoConfig, TFAutoModelForSpeechSeq2Seq

# Download configuration from huggingface.co and cache.
config = AutoConfig.from_pretrained("bert-base-cased")
model = TFAutoModelForSpeechSeq2Seq.from_config(config)`,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(){g=a("p"),w=o("Examples:"),u=l(),v(f.$$.fragment)},l(m){g=n(m,"P",{});var h=s(g);w=r(h,"Examples:"),h.forEach(t),u=i(m),T(f.$$.fragment,m)},m(m,h){b(m,g,h),e(g,w),b(m,u,h),F(f,m,h),p=!0},p:G,i(m){p||(M(f.$$.fragment,m),p=!0)},o(m){E(f.$$.fragment,m),p=!1},d(m){m&&t(g),m&&t(u),C(f,m)}}}function ihn(k){let g,w,u,f,p;return f=new B({props:{code:`from transformers import AutoConfig, TFAutoModelForSpeechSeq2Seq

# Download model and configuration from huggingface.co and cache.
model = TFAutoModelForSpeechSeq2Seq.from_pretrained("bert-base-cased")

# Update configuration during loading
model = TFAutoModelForSpeechSeq2Seq.from_pretrained("bert-base-cased", output_attentions=True)
model.config.output_attentions

# Loading from a PyTorch checkpoint file instead of a TensorFlow model (slower)
config = AutoConfig.from_pretrained("./pt_model/bert_pt_model_config.json")
model = TFAutoModelForSpeechSeq2Seq.from_pretrained(
    "./pt_model/bert_pytorch_model.bin", from_pt=True, config=config
)`,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(){g=a("p"),w=o("Examples:"),u=l(),v(f.$$.fragment)},l(m){g=n(m,"P",{});var h=s(g);w=r(h,"Examples:"),h.forEach(t),u=i(m),T(f.$$.fragment,m)},m(m,h){b(m,g,h),e(g,w),b(m,u,h),F(f,m,h),p=!0},p:G,i(m){p||(M(f.$$.fragment,m),p=!0)},o(m){E(f.$$.fragment,m),p=!1},d(m){m&&t(g),m&&t(u),C(f,m)}}}function dhn(k){let g,w,u,f,p;return f=new B({props:{code:`from transformers import AutoConfig, AutoModelForAudioXVector

# Download configuration from huggingface.co and cache.
config = AutoConfig.from_pretrained("bert-base-cased")
model = AutoModelForAudioXVector.from_config(config)`,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(){g=a("p"),w=o("Examples:"),u=l(),v(f.$$.fragment)},l(m){g=n(m,"P",{});var h=s(g);w=r(h,"Examples:"),h.forEach(t),u=i(m),T(f.$$.fragment,m)},m(m,h){b(m,g,h),e(g,w),b(m,u,h),F(f,m,h),p=!0},p:G,i(m){p||(M(f.$$.fragment,m),p=!0)},o(m){E(f.$$.fragment,m),p=!1},d(m){m&&t(g),m&&t(u),C(f,m)}}}function mhn(k){let g,w,u,f,p;return f=new B({props:{code:`from transformers import AutoConfig, AutoModelForAudioXVector

# Download model and configuration from huggingface.co and cache.
model = AutoModelForAudioXVector.from_pretrained("bert-base-cased")

# Update configuration during loading
model = AutoModelForAudioXVector.from_pretrained("bert-base-cased", output_attentions=True)
model.config.output_attentions

# Loading from a TF checkpoint file instead of a PyTorch model (slower)
config = AutoConfig.from_pretrained("./tf_model/bert_tf_model_config.json")
model = AutoModelForAudioXVector.from_pretrained(
    "./tf_model/bert_tf_checkpoint.ckpt.index", from_tf=True, config=config
)`,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(){g=a("p"),w=o("Examples:"),u=l(),v(f.$$.fragment)},l(m){g=n(m,"P",{});var h=s(g);w=r(h,"Examples:"),h.forEach(t),u=i(m),T(f.$$.fragment,m)},m(m,h){b(m,g,h),e(g,w),b(m,u,h),F(f,m,h),p=!0},p:G,i(m){p||(M(f.$$.fragment,m),p=!0)},o(m){E(f.$$.fragment,m),p=!1},d(m){m&&t(g),m&&t(u),C(f,m)}}}function chn(k){let g,w,u,f,p;return f=new B({props:{code:`from transformers import AutoConfig, AutoModelForTableQuestionAnswering

# Download configuration from huggingface.co and cache.
config = AutoConfig.from_pretrained("google/tapas-base-finetuned-wtq")
model = AutoModelForTableQuestionAnswering.from_config(config)`,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(){g=a("p"),w=o("Examples:"),u=l(),v(f.$$.fragment)},l(m){g=n(m,"P",{});var h=s(g);w=r(h,"Examples:"),h.forEach(t),u=i(m),T(f.$$.fragment,m)},m(m,h){b(m,g,h),e(g,w),b(m,u,h),F(f,m,h),p=!0},p:G,i(m){p||(M(f.$$.fragment,m),p=!0)},o(m){E(f.$$.fragment,m),p=!1},d(m){m&&t(g),m&&t(u),C(f,m)}}}function fhn(k){let g,w,u,f,p;return f=new B({props:{code:`from transformers import AutoConfig, AutoModelForTableQuestionAnswering

# Download model and configuration from huggingface.co and cache.
model = AutoModelForTableQuestionAnswering.from_pretrained("google/tapas-base-finetuned-wtq")

# Update configuration during loading
model = AutoModelForTableQuestionAnswering.from_pretrained("google/tapas-base-finetuned-wtq", output_attentions=True)
model.config.output_attentions

# Loading from a TF checkpoint file instead of a PyTorch model (slower)
config = AutoConfig.from_pretrained("./tf_model/tapas_tf_model_config.json")
model = AutoModelForTableQuestionAnswering.from_pretrained(
    "./tf_model/tapas_tf_checkpoint.ckpt.index", from_tf=True, config=config
)`,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(){g=a("p"),w=o("Examples:"),u=l(),v(f.$$.fragment)},l(m){g=n(m,"P",{});var h=s(g);w=r(h,"Examples:"),h.forEach(t),u=i(m),T(f.$$.fragment,m)},m(m,h){b(m,g,h),e(g,w),b(m,u,h),F(f,m,h),p=!0},p:G,i(m){p||(M(f.$$.fragment,m),p=!0)},o(m){E(f.$$.fragment,m),p=!1},d(m){m&&t(g),m&&t(u),C(f,m)}}}function ghn(k){let g,w,u,f,p;return f=new B({props:{code:`from transformers import AutoConfig, TFAutoModelForTableQuestionAnswering

# Download configuration from huggingface.co and cache.
config = AutoConfig.from_pretrained("google/tapas-base-finetuned-wtq")
model = TFAutoModelForTableQuestionAnswering.from_config(config)`,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(){g=a("p"),w=o("Examples:"),u=l(),v(f.$$.fragment)},l(m){g=n(m,"P",{});var h=s(g);w=r(h,"Examples:"),h.forEach(t),u=i(m),T(f.$$.fragment,m)},m(m,h){b(m,g,h),e(g,w),b(m,u,h),F(f,m,h),p=!0},p:G,i(m){p||(M(f.$$.fragment,m),p=!0)},o(m){E(f.$$.fragment,m),p=!1},d(m){m&&t(g),m&&t(u),C(f,m)}}}function hhn(k){let g,w,u,f,p;return f=new B({props:{code:`from transformers import AutoConfig, TFAutoModelForTableQuestionAnswering

# Download model and configuration from huggingface.co and cache.
model = TFAutoModelForTableQuestionAnswering.from_pretrained("google/tapas-base-finetuned-wtq")

# Update configuration during loading
model = TFAutoModelForTableQuestionAnswering.from_pretrained("google/tapas-base-finetuned-wtq", output_attentions=True)
model.config.output_attentions

# Loading from a PyTorch checkpoint file instead of a TensorFlow model (slower)
config = AutoConfig.from_pretrained("./pt_model/tapas_pt_model_config.json")
model = TFAutoModelForTableQuestionAnswering.from_pretrained(
    "./pt_model/tapas_pytorch_model.bin", from_pt=True, config=config
)`,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(){g=a("p"),w=o("Examples:"),u=l(),v(f.$$.fragment)},l(m){g=n(m,"P",{});var h=s(g);w=r(h,"Examples:"),h.forEach(t),u=i(m),T(f.$$.fragment,m)},m(m,h){b(m,g,h),e(g,w),b(m,u,h),F(f,m,h),p=!0},p:G,i(m){p||(M(f.$$.fragment,m),p=!0)},o(m){E(f.$$.fragment,m),p=!1},d(m){m&&t(g),m&&t(u),C(f,m)}}}function uhn(k){let g,w,u,f,p;return f=new B({props:{code:`from transformers import AutoConfig, AutoModelForDocumentQuestionAnswering

# Download configuration from huggingface.co and cache.
config = AutoConfig.from_pretrained("impira/layoutlm-document-qa", revision="52e01b3")
model = AutoModelForDocumentQuestionAnswering.from_config(config)`,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(){g=a("p"),w=o("Examples:"),u=l(),v(f.$$.fragment)},l(m){g=n(m,"P",{});var h=s(g);w=r(h,"Examples:"),h.forEach(t),u=i(m),T(f.$$.fragment,m)},m(m,h){b(m,g,h),e(g,w),b(m,u,h),F(f,m,h),p=!0},p:G,i(m){p||(M(f.$$.fragment,m),p=!0)},o(m){E(f.$$.fragment,m),p=!1},d(m){m&&t(g),m&&t(u),C(f,m)}}}function phn(k){let g,w,u,f,p;return f=new B({props:{code:`from transformers import AutoConfig, AutoModelForDocumentQuestionAnswering

# Download model and configuration from huggingface.co and cache.
model = AutoModelForDocumentQuestionAnswering.from_pretrained("impira/layoutlm-document-qa", revision="52e01b3")

# Update configuration during loading
model = AutoModelForDocumentQuestionAnswering.from_pretrained("impira/layoutlm-document-qa", revision="52e01b3", output_attentions=True)
model.config.output_attentions

# Loading from a TF checkpoint file instead of a PyTorch model (slower)
config = AutoConfig.from_pretrained("./tf_model/layoutlm_tf_model_config.json")
model = AutoModelForDocumentQuestionAnswering.from_pretrained(
    "./tf_model/layoutlm_tf_checkpoint.ckpt.index", from_tf=True, config=config
)`,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(){g=a("p"),w=o("Examples:"),u=l(),v(f.$$.fragment)},l(m){g=n(m,"P",{});var h=s(g);w=r(h,"Examples:"),h.forEach(t),u=i(m),T(f.$$.fragment,m)},m(m,h){b(m,g,h),e(g,w),b(m,u,h),F(f,m,h),p=!0},p:G,i(m){p||(M(f.$$.fragment,m),p=!0)},o(m){E(f.$$.fragment,m),p=!1},d(m){m&&t(g),m&&t(u),C(f,m)}}}function _hn(k){let g,w,u,f,p;return f=new B({props:{code:`from transformers import AutoConfig, TFAutoModelForDocumentQuestionAnswering

# Download configuration from huggingface.co and cache.
config = AutoConfig.from_pretrained("impira/layoutlm-document-qa", revision="52e01b3")
model = TFAutoModelForDocumentQuestionAnswering.from_config(config)`,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(){g=a("p"),w=o("Examples:"),u=l(),v(f.$$.fragment)},l(m){g=n(m,"P",{});var h=s(g);w=r(h,"Examples:"),h.forEach(t),u=i(m),T(f.$$.fragment,m)},m(m,h){b(m,g,h),e(g,w),b(m,u,h),F(f,m,h),p=!0},p:G,i(m){p||(M(f.$$.fragment,m),p=!0)},o(m){E(f.$$.fragment,m),p=!1},d(m){m&&t(g),m&&t(u),C(f,m)}}}function bhn(k){let g,w,u,f,p;return f=new B({props:{code:`from transformers import AutoConfig, TFAutoModelForDocumentQuestionAnswering

# Download model and configuration from huggingface.co and cache.
model = TFAutoModelForDocumentQuestionAnswering.from_pretrained("impira/layoutlm-document-qa", revision="52e01b3")

# Update configuration during loading
model = TFAutoModelForDocumentQuestionAnswering.from_pretrained("impira/layoutlm-document-qa", revision="52e01b3", output_attentions=True)
model.config.output_attentions

# Loading from a PyTorch checkpoint file instead of a TensorFlow model (slower)
config = AutoConfig.from_pretrained("./pt_model/layoutlm_pt_model_config.json")
model = TFAutoModelForDocumentQuestionAnswering.from_pretrained(
    "./pt_model/layoutlm_pytorch_model.bin", from_pt=True, config=config
)`,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(){g=a("p"),w=o("Examples:"),u=l(),v(f.$$.fragment)},l(m){g=n(m,"P",{});var h=s(g);w=r(h,"Examples:"),h.forEach(t),u=i(m),T(f.$$.fragment,m)},m(m,h){b(m,g,h),e(g,w),b(m,u,h),F(f,m,h),p=!0},p:G,i(m){p||(M(f.$$.fragment,m),p=!0)},o(m){E(f.$$.fragment,m),p=!1},d(m){m&&t(g),m&&t(u),C(f,m)}}}function vhn(k){let g,w,u,f,p;return f=new B({props:{code:`from transformers import AutoConfig, AutoModelForVisualQuestionAnswering

# Download configuration from huggingface.co and cache.
config = AutoConfig.from_pretrained("dandelin/vilt-b32-finetuned-vqa")
model = AutoModelForVisualQuestionAnswering.from_config(config)`,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(){g=a("p"),w=o("Examples:"),u=l(),v(f.$$.fragment)},l(m){g=n(m,"P",{});var h=s(g);w=r(h,"Examples:"),h.forEach(t),u=i(m),T(f.$$.fragment,m)},m(m,h){b(m,g,h),e(g,w),b(m,u,h),F(f,m,h),p=!0},p:G,i(m){p||(M(f.$$.fragment,m),p=!0)},o(m){E(f.$$.fragment,m),p=!1},d(m){m&&t(g),m&&t(u),C(f,m)}}}function Thn(k){let g,w,u,f,p;return f=new B({props:{code:`from transformers import AutoConfig, AutoModelForVisualQuestionAnswering

# Download model and configuration from huggingface.co and cache.
model = AutoModelForVisualQuestionAnswering.from_pretrained("dandelin/vilt-b32-finetuned-vqa")

# Update configuration during loading
model = AutoModelForVisualQuestionAnswering.from_pretrained("dandelin/vilt-b32-finetuned-vqa", output_attentions=True)
model.config.output_attentions

# Loading from a TF checkpoint file instead of a PyTorch model (slower)
config = AutoConfig.from_pretrained("./tf_model/vilt_tf_model_config.json")
model = AutoModelForVisualQuestionAnswering.from_pretrained(
    "./tf_model/vilt_tf_checkpoint.ckpt.index", from_tf=True, config=config
)`,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(){g=a("p"),w=o("Examples:"),u=l(),v(f.$$.fragment)},l(m){g=n(m,"P",{});var h=s(g);w=r(h,"Examples:"),h.forEach(t),u=i(m),T(f.$$.fragment,m)},m(m,h){b(m,g,h),e(g,w),b(m,u,h),F(f,m,h),p=!0},p:G,i(m){p||(M(f.$$.fragment,m),p=!0)},o(m){E(f.$$.fragment,m),p=!1},d(m){m&&t(g),m&&t(u),C(f,m)}}}function Fhn(k){let g,w,u,f,p;return f=new B({props:{code:`from transformers import AutoConfig, AutoModelForVision2Seq

# Download configuration from huggingface.co and cache.
config = AutoConfig.from_pretrained("bert-base-cased")
model = AutoModelForVision2Seq.from_config(config)`,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(){g=a("p"),w=o("Examples:"),u=l(),v(f.$$.fragment)},l(m){g=n(m,"P",{});var h=s(g);w=r(h,"Examples:"),h.forEach(t),u=i(m),T(f.$$.fragment,m)},m(m,h){b(m,g,h),e(g,w),b(m,u,h),F(f,m,h),p=!0},p:G,i(m){p||(M(f.$$.fragment,m),p=!0)},o(m){E(f.$$.fragment,m),p=!1},d(m){m&&t(g),m&&t(u),C(f,m)}}}function Mhn(k){let g,w,u,f,p;return f=new B({props:{code:`from transformers import AutoConfig, AutoModelForVision2Seq

# Download model and configuration from huggingface.co and cache.
model = AutoModelForVision2Seq.from_pretrained("bert-base-cased")

# Update configuration during loading
model = AutoModelForVision2Seq.from_pretrained("bert-base-cased", output_attentions=True)
model.config.output_attentions

# Loading from a TF checkpoint file instead of a PyTorch model (slower)
config = AutoConfig.from_pretrained("./tf_model/bert_tf_model_config.json")
model = AutoModelForVision2Seq.from_pretrained(
    "./tf_model/bert_tf_checkpoint.ckpt.index", from_tf=True, config=config
)`,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(){g=a("p"),w=o("Examples:"),u=l(),v(f.$$.fragment)},l(m){g=n(m,"P",{});var h=s(g);w=r(h,"Examples:"),h.forEach(t),u=i(m),T(f.$$.fragment,m)},m(m,h){b(m,g,h),e(g,w),b(m,u,h),F(f,m,h),p=!0},p:G,i(m){p||(M(f.$$.fragment,m),p=!0)},o(m){E(f.$$.fragment,m),p=!1},d(m){m&&t(g),m&&t(u),C(f,m)}}}function Ehn(k){let g,w,u,f,p;return f=new B({props:{code:`from transformers import AutoConfig, TFAutoModelForVision2Seq

# Download configuration from huggingface.co and cache.
config = AutoConfig.from_pretrained("bert-base-cased")
model = TFAutoModelForVision2Seq.from_config(config)`,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(){g=a("p"),w=o("Examples:"),u=l(),v(f.$$.fragment)},l(m){g=n(m,"P",{});var h=s(g);w=r(h,"Examples:"),h.forEach(t),u=i(m),T(f.$$.fragment,m)},m(m,h){b(m,g,h),e(g,w),b(m,u,h),F(f,m,h),p=!0},p:G,i(m){p||(M(f.$$.fragment,m),p=!0)},o(m){E(f.$$.fragment,m),p=!1},d(m){m&&t(g),m&&t(u),C(f,m)}}}function Chn(k){let g,w,u,f,p;return f=new B({props:{code:`from transformers import AutoConfig, TFAutoModelForVision2Seq

# Download model and configuration from huggingface.co and cache.
model = TFAutoModelForVision2Seq.from_pretrained("bert-base-cased")

# Update configuration during loading
model = TFAutoModelForVision2Seq.from_pretrained("bert-base-cased", output_attentions=True)
model.config.output_attentions

# Loading from a PyTorch checkpoint file instead of a TensorFlow model (slower)
config = AutoConfig.from_pretrained("./pt_model/bert_pt_model_config.json")
model = TFAutoModelForVision2Seq.from_pretrained(
    "./pt_model/bert_pytorch_model.bin", from_pt=True, config=config
)`,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(){g=a("p"),w=o("Examples:"),u=l(),v(f.$$.fragment)},l(m){g=n(m,"P",{});var h=s(g);w=r(h,"Examples:"),h.forEach(t),u=i(m),T(f.$$.fragment,m)},m(m,h){b(m,g,h),e(g,w),b(m,u,h),F(f,m,h),p=!0},p:G,i(m){p||(M(f.$$.fragment,m),p=!0)},o(m){E(f.$$.fragment,m),p=!1},d(m){m&&t(g),m&&t(u),C(f,m)}}}function whn(k){let g,w,u,f,p;return f=new B({props:{code:`from transformers import AutoConfig, FlaxAutoModelForVision2Seq

# Download configuration from huggingface.co and cache.
config = AutoConfig.from_pretrained("bert-base-cased")
model = FlaxAutoModelForVision2Seq.from_config(config)`,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(){g=a("p"),w=o("Examples:"),u=l(),v(f.$$.fragment)},l(m){g=n(m,"P",{});var h=s(g);w=r(h,"Examples:"),h.forEach(t),u=i(m),T(f.$$.fragment,m)},m(m,h){b(m,g,h),e(g,w),b(m,u,h),F(f,m,h),p=!0},p:G,i(m){p||(M(f.$$.fragment,m),p=!0)},o(m){E(f.$$.fragment,m),p=!1},d(m){m&&t(g),m&&t(u),C(f,m)}}}function Ahn(k){let g,w,u,f,p;return f=new B({props:{code:`from transformers import AutoConfig, FlaxAutoModelForVision2Seq

# Download model and configuration from huggingface.co and cache.
model = FlaxAutoModelForVision2Seq.from_pretrained("bert-base-cased")

# Update configuration during loading
model = FlaxAutoModelForVision2Seq.from_pretrained("bert-base-cased", output_attentions=True)
model.config.output_attentions

# Loading from a PyTorch checkpoint file instead of a TensorFlow model (slower)
config = AutoConfig.from_pretrained("./pt_model/bert_pt_model_config.json")
model = FlaxAutoModelForVision2Seq.from_pretrained(
    "./pt_model/bert_pytorch_model.bin", from_pt=True, config=config
)`,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(){g=a("p"),w=o("Examples:"),u=l(),v(f.$$.fragment)},l(m){g=n(m,"P",{});var h=s(g);w=r(h,"Examples:"),h.forEach(t),u=i(m),T(f.$$.fragment,m)},m(m,h){b(m,g,h),e(g,w),b(m,u,h),F(f,m,h),p=!0},p:G,i(m){p||(M(f.$$.fragment,m),p=!0)},o(m){E(f.$$.fragment,m),p=!1},d(m){m&&t(g),m&&t(u),C(f,m)}}}function Lhn(k){let 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m=new re({}),gn=new B({props:{code:'model = AutoModel.from_pretrained("bert-base-cased")',highlighted:'model = AutoModel.from_pretrained(<span class="hljs-string">&quot;bert-base-cased&quot;</span>)'}}),VP=new re({}),XP=new B({props:{code:`from transformers import AutoConfig, AutoModel

AutoConfig.register("new-model", NewModelConfig)
AutoModel.register(NewModelConfig, NewModel)`,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)`}}),xg=new m3o({props:{warning:!0,$$slots:{default:[ffn]},$$scope:{ctx:k}}}),zP=new re({}),WP=new R({props:{name:"class transformers.AutoConfig",anchor:"transformers.AutoConfig",parameters:[],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/auto/configuration_auto.py#L747"}}),HP=new R({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
should only be set to <code>True</code> for repositories you trust and in which you have read the code, as it will
execute code present on the Hub on your local machine.`,name:"trust_remote_code"},{anchor:"transformers.AutoConfig.from_pretrained.kwargs(additional",description:`<strong>kwargs(additional</strong> keyword arguments, <em>optional</em>) &#x2014;
The values in kwargs of any keys which are configuration attributes will be used to override the loaded
values. Behavior concerning key/value pairs whose keys are <em>not</em> configuration attributes is controlled
by the <code>return_unused_kwargs</code> keyword parameter.`,name:"kwargs(additional"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/auto/configuration_auto.py#L770"}}),Sp=new D({props:{anchor:"transformers.AutoConfig.from_pretrained.example",$$slots:{default:[gfn]},$$scope:{ctx:k}}}),JP=new R({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#L894"}}),YP=new re({}),ZP=new R({props:{name:"class transformers.AutoTokenizer",anchor:"transformers.AutoTokenizer",parameters:[],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/auto/tokenization_auto.py#L469"}}),oI=new R({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;
Will be passed along to the Tokenizer <code>__init__()</code> method.`,name:"inputs"},{anchor:"transformers.AutoTokenizer.from_pretrained.config",description:`<strong>config</strong> (<a href="/docs/transformers/main/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>, <em>optional</em>) &#x2014;
The configuration object used to dertermine 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
git-based system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any
identifier allowed by git.`,name:"revision"},{anchor:"transformers.AutoTokenizer.from_pretrained.subfolder",description:`<strong>subfolder</strong> (<code>str</code>, <em>optional</em>) &#x2014;
In case the relevant files are located inside a subfolder of the model repo on huggingface.co (e.g. for
facebook/rag-token-base), specify it here.`,name:"subfolder"},{anchor:"transformers.AutoTokenizer.from_pretrained.use_fast",description:`<strong>use_fast</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>True</code>) &#x2014;
Use a <a href="https://huggingface.co/docs/tokenizers/index" rel="nofollow">fast Rust-based tokenizer</a> if it is supported for
a given model. If a fast tokenizer is not available for a given model, a normal Python-based tokenizer
is returned instead.`,name:"use_fast"},{anchor:"transformers.AutoTokenizer.from_pretrained.tokenizer_type",description:`<strong>tokenizer_type</strong> (<code>str</code>, <em>optional</em>) &#x2014;
Tokenizer type to be loaded.`,name:"tokenizer_type"},{anchor:"transformers.AutoTokenizer.from_pretrained.trust_remote_code",description:`<strong>trust_remote_code</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to allow for custom models defined on the Hub in their own modeling files. This option
should only be set to <code>True</code> for repositories you trust and in which you have read the code, as it will
execute code present on the Hub on your local machine.`,name:"trust_remote_code"},{anchor:"transformers.AutoTokenizer.from_pretrained.kwargs",description:`<strong>kwargs</strong> (additional keyword arguments, <em>optional</em>) &#x2014;
Will be passed to the Tokenizer <code>__init__()</code> method. Can be used to set special tokens like
<code>bos_token</code>, <code>eos_token</code>, <code>unk_token</code>, <code>sep_token</code>, <code>pad_token</code>, <code>cls_token</code>, <code>mask_token</code>,
<code>additional_special_tokens</code>. See parameters in the <code>__init__()</code> for more details.`,name:"kwargs"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/auto/tokenization_auto.py#L483"}}),F_=new D({props:{anchor:"transformers.AutoTokenizer.from_pretrained.example",$$slots:{default:[hfn]},$$scope:{ctx:k}}}),rI=new R({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#L692"}}),tI=new re({}),aI=new R({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#L217"}}),lI=new R({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.use_auth_token",description:`<strong>use_auth_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:"use_auth_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#L231"}}),L1=new m3o({props:{$$slots:{default:[ufn]},$$scope:{ctx:k}}}),y1=new D({props:{anchor:"transformers.AutoFeatureExtractor.from_pretrained.example",$$slots:{default:[pfn]},$$scope:{ctx:k}}}),iI=new R({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#L359"}}),dI=new re({}),mI=new R({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#L217"}}),gI=new R({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.use_auth_token",description:`<strong>use_auth_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:"use_auth_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
identifier allowed by git.`,name:"revision"},{anchor:"transformers.AutoImageProcessor.from_pretrained.return_unused_kwargs",description:`<strong>return_unused_kwargs</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
If <code>False</code>, then this function returns just the final image processor object. If <code>True</code>, then this
functions returns a <code>Tuple(image_processor, unused_kwargs)</code> where <em>unused_kwargs</em> is a dictionary
consisting of the key/value pairs whose keys are not image processor attributes: i.e., the part of
<code>kwargs</code> which has not been used to update <code>image_processor</code> and is otherwise ignored.`,name:"return_unused_kwargs"},{anchor:"transformers.AutoImageProcessor.from_pretrained.trust_remote_code",description:`<strong>trust_remote_code</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to allow for custom models defined on the Hub in their own modeling files. This option
should only be set to <code>True</code> for repositories you trust and in which you have read the code, as it will
execute code present on the Hub on your local machine.`,name:"trust_remote_code"},{anchor:"transformers.AutoImageProcessor.from_pretrained.kwargs",description:`<strong>kwargs</strong> (<code>Dict[str, Any]</code>, <em>optional</em>) &#x2014;
The values in kwargs of any keys which are image processor attributes will be used to override the
loaded values. Behavior concerning key/value pairs whose keys are <em>not</em> image processor attributes is
controlled by the <code>return_unused_kwargs</code> keyword parameter.`,name:"kwargs"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/auto/image_processing_auto.py#L231"}}),S2=new m3o({props:{$$slots:{default:[_fn]},$$scope:{ctx:k}}}),R2=new D({props:{anchor:"transformers.AutoImageProcessor.from_pretrained.example",$$slots:{default:[bfn]},$$scope:{ctx:k}}}),hI=new R({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#L377"}}),uI=new re({}),pI=new R({props:{name:"class transformers.AutoProcessor",anchor:"transformers.AutoProcessor",parameters:[],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/auto/processing_auto.py#L105"}}),vI=new R({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.use_auth_token",description:`<strong>use_auth_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:"use_auth_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#L119"}}),fb=new m3o({props:{$$slots:{default:[vfn]},$$scope:{ctx:k}}}),gb=new D({props:{anchor:"transformers.AutoProcessor.from_pretrained.example",$$slots:{default:[Tfn]},$$scope:{ctx:k}}}),TI=new R({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#L301"}}),FI=new re({}),MI=new re({}),EI=new R({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#L993"}}),wI=new R({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/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/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#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/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/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/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/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/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/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><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/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/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/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/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/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/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/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/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/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/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/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/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/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/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/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/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><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/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/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/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#L389"}}),_b=new D({props:{anchor:"transformers.AutoModel.from_config.example",$$slots:{default:[Ffn]},$$scope:{ctx:k}}}),AI=new R({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.kwargs",description:`<strong>kwargs</strong> (additional 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#L417"}}),fF=new D({props:{anchor:"transformers.AutoModel.from_pretrained.example",$$slots:{default:[Mfn]},$$scope:{ctx:k}}}),LI=new re({}),yI=new R({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#L460"}}),kI=new R({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/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/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/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/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#L389"}}),hF=new D({props:{anchor:"transformers.TFAutoModel.from_config.example",$$slots:{default:[Efn]},$$scope:{ctx:k}}}),$I=new R({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.kwargs",description:`<strong>kwargs</strong> (additional 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#L417"}}),TM=new D({props:{anchor:"transformers.TFAutoModel.from_pretrained.example",$$slots:{default:[Cfn]},$$scope:{ctx:k}}}),SI=new re({}),RI=new R({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#L257"}}),II=new R({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/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/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/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#L389"}}),MM=new D({props:{anchor:"transformers.FlaxAutoModel.from_config.example",$$slots:{default:[wfn]},$$scope:{ctx:k}}}),BI=new R({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.kwargs",description:`<strong>kwargs</strong> (additional 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#L417"}}),ZM=new D({props:{anchor:"transformers.FlaxAutoModel.from_pretrained.example",$$slots:{default:[Afn]},$$scope:{ctx:k}}}),NI=new re({}),DI=new re({}),GI=new R({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#L1000"}}),jI=new R({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/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/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/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/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/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/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/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>
</ul>`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/auto/auto_factory.py#L389"}}),oE=new D({props:{anchor:"transformers.AutoModelForPreTraining.from_config.example",$$slots:{default:[Lfn]},$$scope:{ctx:k}}}),OI=new R({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.kwargs",description:`<strong>kwargs</strong> (additional 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#L417"}}),o3=new D({props:{anchor:"transformers.AutoModelForPreTraining.from_pretrained.example",$$slots:{default:[yfn]},$$scope:{ctx:k}}}),VI=new re({}),XI=new R({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#L467"}}),WI=new R({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#L389"}}),t3=new D({props:{anchor:"transformers.TFAutoModelForPreTraining.from_config.example",$$slots:{default:[xfn]},$$scope:{ctx:k}}}),QI=new R({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.kwargs",description:`<strong>kwargs</strong> (additional 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#L417"}}),x3=new D({props:{anchor:"transformers.TFAutoModelForPreTraining.from_pretrained.example",$$slots:{default:[kfn]},$$scope:{ctx:k}}}),UI=new re({}),HI=new R({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#L264"}}),YI=new R({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/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#L389"}}),$3=new D({props:{anchor:"transformers.FlaxAutoModelForPreTraining.from_config.example",$$slots:{default:[$fn]},$$scope:{ctx:k}}}),ZI=new R({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.kwargs",description:`<strong>kwargs</strong> (additional 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#L417"}}),W3=new D({props:{anchor:"transformers.FlaxAutoModelForPreTraining.from_pretrained.example",$$slots:{default:[Sfn]},$$scope:{ctx:k}}}),KI=new re({}),eB=new re({}),oB=new R({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#L1015"}}),tB=new R({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/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><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/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/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/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/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/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/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>
</ul>`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/auto/auto_factory.py#L389"}}),H3=new D({props:{anchor:"transformers.AutoModelForCausalLM.from_config.example",$$slots:{default:[Rfn]},$$scope:{ctx:k}}}),aB=new R({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.kwargs",description:`<strong>kwargs</strong> (additional 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#L417"}}),X4=new D({props:{anchor:"transformers.AutoModelForCausalLM.from_pretrained.example",$$slots:{default:[Pfn]},$$scope:{ctx:k}}}),nB=new re({}),sB=new R({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#L482"}}),iB=new R({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#L389"}}),W4=new D({props:{anchor:"transformers.TFAutoModelForCausalLM.from_config.example",$$slots:{default:[Ifn]},$$scope:{ctx:k}}}),dB=new R({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.kwargs",description:`<strong>kwargs</strong> (additional 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#L417"}}),mC=new D({props:{anchor:"transformers.TFAutoModelForCausalLM.from_pretrained.example",$$slots:{default:[Bfn]},$$scope:{ctx:k}}}),mB=new re({}),cB=new R({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#L271"}}),gB=new R({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/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#L389"}}),fC=new D({props:{anchor:"transformers.FlaxAutoModelForCausalLM.from_config.example",$$slots:{default:[Nfn]},$$scope:{ctx:k}}}),hB=new R({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.kwargs",description:`<strong>kwargs</strong> (additional 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#L417"}}),AC=new D({props:{anchor:"transformers.FlaxAutoModelForCausalLM.from_pretrained.example",$$slots:{default:[Dfn]},$$scope:{ctx:k}}}),uB=new re({}),pB=new R({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#L1022"}}),bB=new R({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/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/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/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#L389"}}),yC=new D({props:{anchor:"transformers.AutoModelForMaskedLM.from_config.example",$$slots:{default:[Gfn]},$$scope:{ctx:k}}}),vB=new R({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.kwargs",description:`<strong>kwargs</strong> (additional 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#L417"}}),b5=new D({props:{anchor:"transformers.AutoModelForMaskedLM.from_pretrained.example",$$slots:{default:[qfn]},$$scope:{ctx:k}}}),TB=new re({}),FB=new R({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#L523"}}),EB=new R({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#L389"}}),T5=new D({props:{anchor:"transformers.TFAutoModelForMaskedLM.from_config.example",$$slots:{default:[jfn]},$$scope:{ctx:k}}}),CB=new R({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.kwargs",description:`<strong>kwargs</strong> (additional 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#L417"}}),V5=new D({props:{anchor:"transformers.TFAutoModelForMaskedLM.from_pretrained.example",$$slots:{default:[Ofn]},$$scope:{ctx:k}}}),wB=new re({}),AB=new R({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#L278"}}),yB=new R({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#L389"}}),z5=new D({props:{anchor:"transformers.FlaxAutoModelForMaskedLM.from_config.example",$$slots:{default:[Vfn]},$$scope:{ctx:k}}}),xB=new R({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.kwargs",description:`<strong>kwargs</strong> (additional 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#L417"}}),t0=new D({props:{anchor:"transformers.FlaxAutoModelForMaskedLM.from_pretrained.example",$$slots:{default:[Xfn]},$$scope:{ctx:k}}}),kB=new re({}),$B=new R({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#L1029"}}),RB=new R({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/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/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/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#L389"}}),n0=new D({props:{anchor:"transformers.AutoModelForSeq2SeqLM.from_config.example",$$slots:{default:[zfn]},$$scope:{ctx:k}}}),PB=new R({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.kwargs",description:`<strong>kwargs</strong> (additional 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#L417"}}),y0=new D({props:{anchor:"transformers.AutoModelForSeq2SeqLM.from_pretrained.example",$$slots:{default:[Wfn]},$$scope:{ctx:k}}}),IB=new re({}),BB=new R({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#L530"}}),DB=new R({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#L389"}}),k0=new D({props:{anchor:"transformers.TFAutoModelForSeq2SeqLM.from_config.example",$$slots:{default:[Qfn]},$$scope:{ctx:k}}}),GB=new R({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.kwargs",description:`<strong>kwargs</strong> (additional 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#L417"}}),j0=new D({props:{anchor:"transformers.TFAutoModelForSeq2SeqLM.from_pretrained.example",$$slots:{default:[Ufn]},$$scope:{ctx:k}}}),qB=new re({}),jB=new R({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#L285"}}),VB=new R({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#L389"}}),V0=new D({props:{anchor:"transformers.FlaxAutoModelForSeq2SeqLM.from_config.example",$$slots:{default:[Hfn]},$$scope:{ctx:k}}}),XB=new R({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.kwargs",description:`<strong>kwargs</strong> (additional 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#L417"}}),ew=new D({props:{anchor:"transformers.FlaxAutoModelForSeq2SeqLM.from_pretrained.example",$$slots:{default:[Jfn]},$$scope:{ctx:k}}}),zB=new re({}),WB=new R({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#L1038"}}),UB=new R({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/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/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><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/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/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/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/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/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/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/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/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/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/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#L389"}}),rw=new D({props:{anchor:"transformers.AutoModelForSequenceClassification.from_config.example",$$slots:{default:[Yfn]},$$scope:{ctx:k}}}),HB=new R({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.kwargs",description:`<strong>kwargs</strong> (additional 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#L417"}}),d6=new D({props:{anchor:"transformers.AutoModelForSequenceClassification.from_pretrained.example",$$slots:{default:[Zfn]},$$scope:{ctx:k}}}),JB=new re({}),YB=new R({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#L539"}}),KB=new R({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#L389"}}),c6=new D({props:{anchor:"transformers.TFAutoModelForSequenceClassification.from_config.example",$$slots:{default:[Kfn]},$$scope:{ctx:k}}}),eN=new R({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.kwargs",description:`<strong>kwargs</strong> (additional 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#L417"}}),V6=new D({props:{anchor:"transformers.TFAutoModelForSequenceClassification.from_pretrained.example",$$slots:{default:[egn]},$$scope:{ctx:k}}}),oN=new re({}),rN=new R({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#L294"}}),aN=new R({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#L389"}}),z6=new D({props:{anchor:"transformers.FlaxAutoModelForSequenceClassification.from_config.example",$$slots:{default:[ogn]},$$scope:{ctx:k}}}),nN=new R({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.kwargs",description:`<strong>kwargs</strong> (additional 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#L417"}}),t8=new D({props:{anchor:"transformers.FlaxAutoModelForSequenceClassification.from_pretrained.example",$$slots:{default:[rgn]},$$scope:{ctx:k}}}),sN=new re({}),lN=new R({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#L1094"}}),dN=new R({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/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/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/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/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#L389"}}),n8=new D({props:{anchor:"transformers.AutoModelForMultipleChoice.from_config.example",$$slots:{default:[tgn]},$$scope:{ctx:k}}}),mN=new R({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.kwargs",description:`<strong>kwargs</strong> (additional 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#L417"}}),j8=new D({props:{anchor:"transformers.AutoModelForMultipleChoice.from_pretrained.example",$$slots:{default:[agn]},$$scope:{ctx:k}}}),cN=new re({}),fN=new R({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#L586"}}),hN=new R({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#L389"}}),V8=new D({props:{anchor:"transformers.TFAutoModelForMultipleChoice.from_config.example",$$slots:{default:[ngn]},$$scope:{ctx:k}}}),uN=new R({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.kwargs",description:`<strong>kwargs</strong> (additional 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#L417"}}),iA=new D({props:{anchor:"transformers.TFAutoModelForMultipleChoice.from_pretrained.example",$$slots:{default:[sgn]},$$scope:{ctx:k}}}),pN=new re({}),_N=new R({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#L319"}}),vN=new R({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#L389"}}),mA=new D({props:{anchor:"transformers.FlaxAutoModelForMultipleChoice.from_config.example",$$slots:{default:[lgn]},$$scope:{ctx:k}}}),TN=new R({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
<code>pretrained_model_name_or_path</code> argument).`,name:"from_pt"},{anchor:"transformers.FlaxAutoModelForMultipleChoice.from_pretrained.force_download",description:`<strong>force_download</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to force the (re-)download of the model weights and configuration files, overriding the
cached versions if they exist.`,name:"force_download"},{anchor:"transformers.FlaxAutoModelForMultipleChoice.from_pretrained.resume_download",description:`<strong>resume_download</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to delete incompletely received files. Will attempt to resume the download if such a
file exists.`,name:"resume_download"},{anchor:"transformers.FlaxAutoModelForMultipleChoice.from_pretrained.proxies",description:`<strong>proxies</strong> (<code>Dict[str, str]</code>, <em>optional</em>) &#x2014;
A dictionary of proxy servers to use by protocol or endpoint, e.g., <code>{&apos;http&apos;: &apos;foo.bar:3128&apos;, &apos;http://hostname&apos;: &apos;foo.bar:4012&apos;}</code>. The proxies are used on each request.`,name:"proxies"},{anchor:"transformers.FlaxAutoModelForMultipleChoice.from_pretrained.output_loading_info(bool,",description:`<strong>output_loading_info(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether ot not to also return a dictionary containing missing keys, unexpected keys and error messages.`,name:"output_loading_info(bool,"},{anchor:"transformers.FlaxAutoModelForMultipleChoice.from_pretrained.local_files_only(bool,",description:`<strong>local_files_only(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to only look at local files (e.g., not try downloading the model).`,name:"local_files_only(bool,"},{anchor:"transformers.FlaxAutoModelForMultipleChoice.from_pretrained.revision",description:`<strong>revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a
git-based system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any
identifier allowed by git.`,name:"revision"},{anchor:"transformers.FlaxAutoModelForMultipleChoice.from_pretrained.trust_remote_code",description:`<strong>trust_remote_code</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to allow for custom models defined on the Hub in their own modeling files. This option
should only be set to <code>True</code> for repositories you trust and in which you have read the code, as it will
execute code present on the Hub on your local machine.`,name:"trust_remote_code"},{anchor:"transformers.FlaxAutoModelForMultipleChoice.from_pretrained.kwargs",description:`<strong>kwargs</strong> (additional 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#L417"}}),TA=new D({props:{anchor:"transformers.FlaxAutoModelForMultipleChoice.from_pretrained.example",$$slots:{default:[ign]},$$scope:{ctx:k}}}),FN=new re({}),MN=new R({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#L1101"}}),CN=new R({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#L389"}}),MA=new D({props:{anchor:"transformers.AutoModelForNextSentencePrediction.from_config.example",$$slots:{default:[dgn]},$$scope:{ctx:k}}}),wN=new R({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
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Whether or not to force the (re-)download of the model weights and configuration files, overriding the
cached versions if they exist.`,name:"force_download"},{anchor:"transformers.AutoModelForNextSentencePrediction.from_pretrained.resume_download",description:`<strong>resume_download</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to delete incompletely received files. Will attempt to resume the download if such a
file exists.`,name:"resume_download"},{anchor:"transformers.AutoModelForNextSentencePrediction.from_pretrained.proxies",description:`<strong>proxies</strong> (<code>Dict[str, str]</code>, <em>optional</em>) &#x2014;
A dictionary of proxy servers to use by protocol or endpoint, e.g., <code>{&apos;http&apos;: &apos;foo.bar:3128&apos;, &apos;http://hostname&apos;: &apos;foo.bar:4012&apos;}</code>. The proxies are used on each request.`,name:"proxies"},{anchor:"transformers.AutoModelForNextSentencePrediction.from_pretrained.output_loading_info(bool,",description:`<strong>output_loading_info(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
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.kwargs",description:`<strong>kwargs</strong> (additional 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#L417"}}),$A=new D({props:{anchor:"transformers.AutoModelForNextSentencePrediction.from_pretrained.example",$$slots:{default:[mgn]},$$scope:{ctx:k}}}),AN=new re({}),LN=new R({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#L593"}}),xN=new R({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#L389"}}),RA=new D({props:{anchor:"transformers.TFAutoModelForNextSentencePrediction.from_config.example",$$slots:{default:[cgn]},$$scope:{ctx:k}}}),kN=new R({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.kwargs",description:`<strong>kwargs</strong> (additional 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#L417"}}),BA=new D({props:{anchor:"transformers.TFAutoModelForNextSentencePrediction.from_pretrained.example",$$slots:{default:[fgn]},$$scope:{ctx:k}}}),SN=new re({}),RN=new R({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#L326"}}),IN=new R({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#L389"}}),DA=new D({props:{anchor:"transformers.FlaxAutoModelForNextSentencePrediction.from_config.example",$$slots:{default:[ggn]},$$scope:{ctx:k}}}),BN=new R({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.kwargs",description:`<strong>kwargs</strong> (additional 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#L417"}}),qA=new D({props:{anchor:"transformers.FlaxAutoModelForNextSentencePrediction.from_pretrained.example",$$slots:{default:[hgn]},$$scope:{ctx:k}}}),NN=new re({}),DN=new R({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#L1087"}}),qN=new R({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/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/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><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/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/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/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/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#L389"}}),OA=new D({props:{anchor:"transformers.AutoModelForTokenClassification.from_config.example",$$slots:{default:[ugn]},$$scope:{ctx:k}}}),jN=new R({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.kwargs",description:`<strong>kwargs</strong> (additional 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#L417"}}),P7=new D({props:{anchor:"transformers.AutoModelForTokenClassification.from_pretrained.example",$$slots:{default:[pgn]},$$scope:{ctx:k}}}),ON=new re({}),VN=new R({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#L577"}}),zN=new R({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#L389"}}),B7=new D({props:{anchor:"transformers.TFAutoModelForTokenClassification.from_config.example",$$slots:{default:[_gn]},$$scope:{ctx:k}}}),WN=new R({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.kwargs",description:`<strong>kwargs</strong> (additional 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#L417"}}),sL=new D({props:{anchor:"transformers.TFAutoModelForTokenClassification.from_pretrained.example",$$slots:{default:[bgn]},$$scope:{ctx:k}}}),QN=new re({}),UN=new R({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#L310"}}),JN=new R({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#L389"}}),iL=new D({props:{anchor:"transformers.FlaxAutoModelForTokenClassification.from_config.example",$$slots:{default:[vgn]},$$scope:{ctx:k}}}),YN=new R({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.kwargs",description:`<strong>kwargs</strong> (additional 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#L417"}}),bL=new D({props:{anchor:"transformers.FlaxAutoModelForTokenClassification.from_pretrained.example",$$slots:{default:[Tgn]},$$scope:{ctx:k}}}),ZN=new re({}),KN=new R({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#L1047"}}),oD=new R({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/fnet#transformers.FNetConfig">FNetConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/fnet#transformers.FNetForQuestionAnswering">FNetForQuestionAnswering</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.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/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/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/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/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/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/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/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#L389"}}),TL=new D({props:{anchor:"transformers.AutoModelForQuestionAnswering.from_config.example",$$slots:{default:[Fgn]},$$scope:{ctx:k}}}),rD=new R({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.kwargs",description:`<strong>kwargs</strong> (additional 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#L417"}}),b9=new D({props:{anchor:"transformers.AutoModelForQuestionAnswering.from_pretrained.example",$$slots:{default:[Mgn]},$$scope:{ctx:k}}}),tD=new re({}),aD=new R({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#L548"}}),sD=new R({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#L389"}}),T9=new D({props:{anchor:"transformers.TFAutoModelForQuestionAnswering.from_config.example",$$slots:{default:[Egn]},$$scope:{ctx:k}}}),lD=new R({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.kwargs",description:`<strong>kwargs</strong> (additional 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#L417"}}),V9=new D({props:{anchor:"transformers.TFAutoModelForQuestionAnswering.from_pretrained.example",$$slots:{default:[Cgn]},$$scope:{ctx:k}}}),iD=new re({}),dD=new R({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#L303"}}),cD=new R({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#L389"}}),z9=new D({props:{anchor:"transformers.FlaxAutoModelForQuestionAnswering.from_config.example",$$slots:{default:[wgn]},$$scope:{ctx:k}}}),fD=new R({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.kwargs",description:`<strong>kwargs</strong> (additional 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#L417"}}),ty=new D({props:{anchor:"transformers.FlaxAutoModelForQuestionAnswering.from_pretrained.example",$$slots:{default:[Agn]},$$scope:{ctx:k}}}),gD=new re({}),hD=new re({}),uD=new R({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#L1167"}}),_D=new R({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#L389"}}),sy=new D({props:{anchor:"transformers.AutoModelForDepthEstimation.from_config.example",$$slots:{default:[Lgn]},$$scope:{ctx:k}}}),bD=new R({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.kwargs",description:`<strong>kwargs</strong> (additional 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#L417"}}),my=new D({props:{anchor:"transformers.AutoModelForDepthEstimation.from_pretrained.example",$$slots:{default:[ygn]},$$scope:{ctx:k}}}),TD=new re({}),FD=new R({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#L1110"}}),ED=new R({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/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/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/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/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/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/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#L389"}}),fy=new D({props:{anchor:"transformers.AutoModelForImageClassification.from_config.example",$$slots:{default:[xgn]},$$scope:{ctx:k}}}),CD=new R({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.kwargs",description:`<strong>kwargs</strong> (additional 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#L417"}}),Iy=new D({props:{anchor:"transformers.AutoModelForImageClassification.from_pretrained.example",$$slots:{default:[kgn]},$$scope:{ctx:k}}}),wD=new re({}),AD=new R({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#L498"}}),yD=new R({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/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#L389"}}),Ny=new D({props:{anchor:"transformers.TFAutoModelForImageClassification.from_config.example",$$slots:{default:[$gn]},$$scope:{ctx:k}}}),xD=new R({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.kwargs",description:`<strong>kwargs</strong> (additional 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#L417"}}),Qy=new D({props:{anchor:"transformers.TFAutoModelForImageClassification.from_pretrained.example",$$slots:{default:[Sgn]},$$scope:{ctx:k}}}),kD=new re({}),$D=new R({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#L335"}}),RD=new R({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/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#L389"}}),Hy=new D({props:{anchor:"transformers.FlaxAutoModelForImageClassification.from_config.example",$$slots:{default:[Rgn]},$$scope:{ctx:k}}}),PD=new R({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.kwargs",description:`<strong>kwargs</strong> (additional 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#L417"}}),Zy=new D({props:{anchor:"transformers.FlaxAutoModelForImageClassification.from_pretrained.example",$$slots:{default:[Pgn]},$$scope:{ctx:k}}}),BD=new re({}),ND=new R({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#L1174"}}),GD=new R({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>
</ul>`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/auto/auto_factory.py#L389"}}),ex=new D({props:{anchor:"transformers.AutoModelForVideoClassification.from_config.example",$$slots:{default:[Ign]},$$scope:{ctx:k}}}),qD=new R({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
should only be set to <code>True</code> for repositories you trust and in which you have read the code, as it will
execute code present on the Hub on your local machine.`,name:"trust_remote_code"},{anchor:"transformers.AutoModelForVideoClassification.from_pretrained.kwargs",description:`<strong>kwargs</strong> (additional 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#L417"}}),ax=new D({props:{anchor:"transformers.AutoModelForVideoClassification.from_pretrained.example",$$slots:{default:[Bgn]},$$scope:{ctx:k}}}),OD=new re({}),VD=new R({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#L1231"}}),zD=new R({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/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#L389"}}),sx=new D({props:{anchor:"transformers.AutoModelForMaskedImageModeling.from_config.example",$$slots:{default:[Ngn]},$$scope:{ctx:k}}}),WD=new R({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.kwargs",description:`<strong>kwargs</strong> (additional 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#L417"}}),fx=new D({props:{anchor:"transformers.AutoModelForMaskedImageModeling.from_pretrained.example",$$slots:{default:[Dgn]},$$scope:{ctx:k}}}),QD=new re({}),UD=new R({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#L1151"}}),JD=new R({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#L389"}}),hx=new D({props:{anchor:"transformers.AutoModelForObjectDetection.from_config.example",$$slots:{default:[Ggn]},$$scope:{ctx:k}}}),YD=new R({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.kwargs",description:`<strong>kwargs</strong> (additional 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#L417"}}),Mx=new D({props:{anchor:"transformers.AutoModelForObjectDetection.from_pretrained.example",$$slots:{default:[qgn]},$$scope:{ctx:k}}}),ZD=new re({}),KD=new R({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#L1117"}}),oG=new R({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#L389"}}),Cx=new D({props:{anchor:"transformers.AutoModelForImageSegmentation.from_config.example",$$slots:{default:[jgn]},$$scope:{ctx:k}}}),rG=new R({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.kwargs",description:`<strong>kwargs</strong> (additional 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#L417"}}),Lx=new D({props:{anchor:"transformers.AutoModelForImageSegmentation.from_pretrained.example",$$slots:{default:[Ogn]},$$scope:{ctx:k}}}),tG=new re({}),aG=new R({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#L1124"}}),sG=new R({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/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#L389"}}),xx=new D({props:{anchor:"transformers.AutoModelForSemanticSegmentation.from_config.example",$$slots:{default:[Vgn]},$$scope:{ctx:k}}}),lG=new R({props:{name:"from_pretrained",anchor:"transformers.AutoModelForSemanticSegmentation.from_pretrained",parameters:[{name:"*model_args",val:""},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.AutoModelForSemanticSegmentation.from_pretrained.pretrained_model_name_or_path",description:`<strong>pretrained_model_name_or_path</strong> (<code>str</code> or <code>os.PathLike</code>) &#x2014;
Can be either:</p>
<ul>
<li>A string, the <em>model id</em> of a pretrained model hosted inside a model repo on huggingface.co.
Valid model ids can be located at the root-level, like <code>bert-base-uncased</code>, or namespaced under a
user or organization name, like <code>dbmdz/bert-base-german-cased</code>.</li>
<li>A path to a <em>directory</em> containing model weights saved using
<a href="/docs/transformers/main/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a>, e.g., <code>./my_model_directory/</code>.</li>
<li>A path or url to a <em>tensorflow index checkpoint file</em> (e.g, <code>./tf_model/model.ckpt.index</code>). In
this case, <code>from_tf</code> should be set to <code>True</code> and a configuration object should be provided as
<code>config</code> argument. This loading path is slower than converting the TensorFlow checkpoint in a
PyTorch model using the provided conversion scripts and loading the PyTorch model afterwards.</li>
</ul>`,name:"pretrained_model_name_or_path"},{anchor:"transformers.AutoModelForSemanticSegmentation.from_pretrained.model_args",description:`<strong>model_args</strong> (additional positional arguments, <em>optional</em>) &#x2014;
Will be passed along to the underlying model <code>__init__()</code> method.`,name:"model_args"},{anchor:"transformers.AutoModelForSemanticSegmentation.from_pretrained.config",description:`<strong>config</strong> (<a href="/docs/transformers/main/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.AutoModelForSemanticSegmentation.from_pretrained.state_dict",description:`<strong>state_dict</strong> (<em>Dict[str, torch.Tensor]</em>, <em>optional</em>) &#x2014;
A state dictionary to use instead of a state dictionary loaded from saved weights file.</p>
<p>This option can be used if you want to create a model from a pretrained configuration but load your own
weights. In this case though, you should check if using <a href="/docs/transformers/main/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
<code>pretrained_model_name_or_path</code> argument).`,name:"from_tf"},{anchor:"transformers.AutoModelForSemanticSegmentation.from_pretrained.force_download",description:`<strong>force_download</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to force the (re-)download of the model weights and configuration files, overriding the
cached versions if they exist.`,name:"force_download"},{anchor:"transformers.AutoModelForSemanticSegmentation.from_pretrained.resume_download",description:`<strong>resume_download</strong> (<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.AutoModelForSemanticSegmentation.from_pretrained.proxies",description:`<strong>proxies</strong> (<code>Dict[str, str]</code>, <em>optional</em>) &#x2014;
A dictionary of proxy servers to use by protocol or endpoint, e.g., <code>{&apos;http&apos;: &apos;foo.bar:3128&apos;, &apos;http://hostname&apos;: &apos;foo.bar:4012&apos;}</code>. The proxies are used on each request.`,name:"proxies"},{anchor:"transformers.AutoModelForSemanticSegmentation.from_pretrained.output_loading_info(bool,",description:`<strong>output_loading_info(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether ot not to also return a dictionary containing missing keys, unexpected keys and error messages.`,name:"output_loading_info(bool,"},{anchor:"transformers.AutoModelForSemanticSegmentation.from_pretrained.local_files_only(bool,",description:`<strong>local_files_only(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to only look at local files (e.g., not try downloading the model).`,name:"local_files_only(bool,"},{anchor:"transformers.AutoModelForSemanticSegmentation.from_pretrained.revision",description:`<strong>revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a
git-based system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any
identifier allowed by git.`,name:"revision"},{anchor:"transformers.AutoModelForSemanticSegmentation.from_pretrained.trust_remote_code",description:`<strong>trust_remote_code</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to allow for custom models defined on the Hub in their own modeling files. This option
should only be set to <code>True</code> for repositories you trust and in which you have read the code, as it will
execute code present on the Hub on your local machine.`,name:"trust_remote_code"},{anchor:"transformers.AutoModelForSemanticSegmentation.from_pretrained.kwargs",description:`<strong>kwargs</strong> (additional 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#L417"}}),Dx=new D({props:{anchor:"transformers.AutoModelForSemanticSegmentation.from_pretrained.example",$$slots:{default:[Xgn]},$$scope:{ctx:k}}}),iG=new re({}),dG=new R({props:{name:"class transformers.TFAutoModelForSemanticSegmentation",anchor:"transformers.TFAutoModelForSemanticSegmentation",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/auto/modeling_tf_auto.py#L507"}}),cG=new R({props:{name:"from_config",anchor:"transformers.TFAutoModelForSemanticSegmentation.from_config",parameters:[{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.TFAutoModelForSemanticSegmentation.from_config.config",description:`<strong>config</strong> (<a href="/docs/transformers/main/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.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#L389"}}),qx=new D({props:{anchor:"transformers.TFAutoModelForSemanticSegmentation.from_config.example",$$slots:{default:[zgn]},$$scope:{ctx:k}}}),fG=new R({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
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.TFAutoModelForSemanticSegmentation.from_pretrained.cache_dir",description:`<strong>cache_dir</strong> (<code>str</code> or <code>os.PathLike</code>, <em>optional</em>) &#x2014;
Path to a directory in which a downloaded pretrained model configuration should be cached if the
standard cache should not be used.`,name:"cache_dir"},{anchor:"transformers.TFAutoModelForSemanticSegmentation.from_pretrained.from_pt",description:`<strong>from_pt</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Load the model weights from a PyTorch checkpoint save file (see docstring of
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Whether or not to delete incompletely received files. Will attempt to resume the download if such a
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The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a
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Whether or not to allow for custom models defined on the Hub in their own modeling files. This option
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Can be used to update the configuration object (after it being loaded) and initiate the model (e.g.,
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<ul>
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underlying model&#x2019;s <code>__init__</code> method (we assume all relevant updates to the configuration have
already been done)</li>
<li>If a configuration is not provided, <code>kwargs</code> will be first passed to the configuration class
initialization function (<a href="/docs/transformers/main/en/main_classes/configuration#transformers.PretrainedConfig.from_pretrained">from_pretrained()</a>). Each key of <code>kwargs</code> that
corresponds to a configuration attribute will be used to override said attribute with the
supplied <code>kwargs</code> value. Remaining keys that do not correspond to any configuration attribute
will be passed to the underlying model&#x2019;s <code>__init__</code> function.</li>
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The model class to instantiate is selected based on the configuration class:</p>
<ul>
<li><a href="/docs/transformers/main/en/model_doc/maskformer#transformers.MaskFormerConfig">MaskFormerConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/maskformer#transformers.MaskFormerForInstanceSegmentation">MaskFormerForInstanceSegmentation</a> (MaskFormer model)</li>
</ul>`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/auto/auto_factory.py#L389"}}),Wx=new D({props:{anchor:"transformers.AutoModelForInstanceSegmentation.from_config.example",$$slots:{default:[Qgn]},$$scope:{ctx:k}}}),_G=new R({props:{name:"from_pretrained",anchor:"transformers.AutoModelForInstanceSegmentation.from_pretrained",parameters:[{name:"*model_args",val:""},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.AutoModelForInstanceSegmentation.from_pretrained.pretrained_model_name_or_path",description:`<strong>pretrained_model_name_or_path</strong> (<code>str</code> or <code>os.PathLike</code>) &#x2014;
Can be either:</p>
<ul>
<li>A string, the <em>model id</em> of a pretrained model hosted inside a model repo on huggingface.co.
Valid model ids can be located at the root-level, like <code>bert-base-uncased</code>, or namespaced under a
user or organization name, like <code>dbmdz/bert-base-german-cased</code>.</li>
<li>A path to a <em>directory</em> containing model weights saved using
<a href="/docs/transformers/main/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a>, e.g., <code>./my_model_directory/</code>.</li>
<li>A path or url to a <em>tensorflow index checkpoint file</em> (e.g, <code>./tf_model/model.ckpt.index</code>). In
this case, <code>from_tf</code> should be set to <code>True</code> and a configuration object should be provided as
<code>config</code> argument. This loading path is slower than converting the TensorFlow checkpoint in a
PyTorch model using the provided conversion scripts and loading the PyTorch model afterwards.</li>
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Configuration for the model to use instead of an automatically loaded configuration. Configuration can
be automatically loaded when:</p>
<ul>
<li>The model is a model provided by the library (loaded with the <em>model id</em> string of a pretrained
model).</li>
<li>The model was saved using <a href="/docs/transformers/main/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and is reloaded by supplying the
save directory.</li>
<li>The model is loaded by supplying a local directory as <code>pretrained_model_name_or_path</code> and a
configuration JSON file named <em>config.json</em> is found in the directory.</li>
</ul>`,name:"config"},{anchor:"transformers.AutoModelForInstanceSegmentation.from_pretrained.state_dict",description:`<strong>state_dict</strong> (<em>Dict[str, torch.Tensor]</em>, <em>optional</em>) &#x2014;
A state dictionary to use instead of a state dictionary loaded from saved weights file.</p>
<p>This option can be used if you want to create a model from a pretrained configuration but load your own
weights. In this case though, you should check if using <a href="/docs/transformers/main/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and
<a href="/docs/transformers/main/en/main_classes/model#transformers.PreTrainedModel.from_pretrained">from_pretrained()</a> is not a simpler option.`,name:"state_dict"},{anchor:"transformers.AutoModelForInstanceSegmentation.from_pretrained.cache_dir",description:`<strong>cache_dir</strong> (<code>str</code> or <code>os.PathLike</code>, <em>optional</em>) &#x2014;
Path to a directory in which a downloaded pretrained model configuration should be cached if the
standard cache should not be used.`,name:"cache_dir"},{anchor:"transformers.AutoModelForInstanceSegmentation.from_pretrained.from_tf",description:`<strong>from_tf</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Load the model weights from a TensorFlow checkpoint save file (see docstring of
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Whether or not to force the (re-)download of the model weights and configuration files, overriding the
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Whether or not to delete incompletely received files. Will attempt to resume the download if such a
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A dictionary of proxy servers to use by protocol or endpoint, e.g., <code>{&apos;http&apos;: &apos;foo.bar:3128&apos;, &apos;http://hostname&apos;: &apos;foo.bar:4012&apos;}</code>. The proxies are used on each request.`,name:"proxies"},{anchor:"transformers.AutoModelForInstanceSegmentation.from_pretrained.output_loading_info(bool,",description:`<strong>output_loading_info(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
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Whether or not to only look at local files (e.g., not try downloading the model).`,name:"local_files_only(bool,"},{anchor:"transformers.AutoModelForInstanceSegmentation.from_pretrained.revision",description:`<strong>revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a
git-based system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any
identifier allowed by git.`,name:"revision"},{anchor:"transformers.AutoModelForInstanceSegmentation.from_pretrained.trust_remote_code",description:`<strong>trust_remote_code</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to allow for custom models defined on the Hub in their own modeling files. This option
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execute code present on the Hub on your local machine.`,name:"trust_remote_code"},{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#L417"}}),Hx=new D({props:{anchor:"transformers.AutoModelForInstanceSegmentation.from_pretrained.example",$$slots:{default:[Ugn]},$$scope:{ctx:k}}}),bG=new re({}),vG=new R({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#L1133"}}),FG=new R({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#L389"}}),Yx=new D({props:{anchor:"transformers.AutoModelForUniversalSegmentation.from_config.example",$$slots:{default:[Hgn]},$$scope:{ctx:k}}}),MG=new R({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
should only be set to <code>True</code> for repositories you trust and in which you have read the code, as it will
execute code present on the Hub on your local machine.`,name:"trust_remote_code"},{anchor:"transformers.AutoModelForUniversalSegmentation.from_pretrained.kwargs",description:`<strong>kwargs</strong> (additional 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#L417"}}),tk=new D({props:{anchor:"transformers.AutoModelForUniversalSegmentation.from_pretrained.example",$$slots:{default:[Jgn]},$$scope:{ctx:k}}}),EG=new re({}),CG=new R({props:{name:"class transformers.AutoModelForZeroShotObjectDetection",anchor:"transformers.AutoModelForZeroShotObjectDetection",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/auto/modeling_auto.py#L1158"}}),AG=new R({props:{name:"from_config",anchor:"transformers.AutoModelForZeroShotObjectDetection.from_config",parameters:[{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.AutoModelForZeroShotObjectDetection.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/owlvit#transformers.OwlViTConfig">OwlViTConfig</a> configuration class: <a href="/docs/transformers/main/en/model_doc/owlvit#transformers.OwlViTForObjectDetection">OwlViTForObjectDetection</a> (OWL-ViT model)</li>
</ul>`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/auto/auto_factory.py#L389"}}),nk=new D({props:{anchor:"transformers.AutoModelForZeroShotObjectDetection.from_config.example",$$slots:{default:[Ygn]},$$scope:{ctx:k}}}),LG=new R({props:{name:"from_pretrained",anchor:"transformers.AutoModelForZeroShotObjectDetection.from_pretrained",parameters:[{name:"*model_args",val:""},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.AutoModelForZeroShotObjectDetection.from_pretrained.pretrained_model_name_or_path",description:`<strong>pretrained_model_name_or_path</strong> (<code>str</code> or <code>os.PathLike</code>) &#x2014;
Can be either:</p>
<ul>
<li>A string, the <em>model id</em> of a pretrained model hosted inside a model repo on huggingface.co.
Valid model ids can be located at the root-level, like <code>bert-base-uncased</code>, or namespaced under a
user or organization name, like <code>dbmdz/bert-base-german-cased</code>.</li>
<li>A path to a <em>directory</em> containing model weights saved using
<a href="/docs/transformers/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.AutoModelForZeroShotObjectDetection.from_pretrained.model_args",description:`<strong>model_args</strong> (additional positional arguments, <em>optional</em>) &#x2014;
Will be passed along to the underlying model <code>__init__()</code> method.`,name:"model_args"},{anchor:"transformers.AutoModelForZeroShotObjectDetection.from_pretrained.config",description:`<strong>config</strong> (<a href="/docs/transformers/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.AutoModelForZeroShotObjectDetection.from_pretrained.state_dict",description:`<strong>state_dict</strong> (<em>Dict[str, torch.Tensor]</em>, <em>optional</em>) &#x2014;
A state dictionary to use instead of a state dictionary loaded from saved weights file.</p>
<p>This option can be used if you want to create a model from a pretrained configuration but load your own
weights. In this case though, you should check if using <a href="/docs/transformers/main/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.AutoModelForZeroShotObjectDetection.from_pretrained.cache_dir",description:`<strong>cache_dir</strong> (<code>str</code> or <code>os.PathLike</code>, <em>optional</em>) &#x2014;
Path to a directory in which a downloaded pretrained model configuration should be cached if the
standard cache should not be used.`,name:"cache_dir"},{anchor:"transformers.AutoModelForZeroShotObjectDetection.from_pretrained.from_tf",description:`<strong>from_tf</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Load the model weights from a TensorFlow checkpoint save file (see docstring of
<code>pretrained_model_name_or_path</code> argument).`,name:"from_tf"},{anchor:"transformers.AutoModelForZeroShotObjectDetection.from_pretrained.force_download",description:`<strong>force_download</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to force the (re-)download of the model weights and configuration files, overriding the
cached versions if they exist.`,name:"force_download"},{anchor:"transformers.AutoModelForZeroShotObjectDetection.from_pretrained.resume_download",description:`<strong>resume_download</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to delete incompletely received files. Will attempt to resume the download if such a
file exists.`,name:"resume_download"},{anchor:"transformers.AutoModelForZeroShotObjectDetection.from_pretrained.proxies",description:`<strong>proxies</strong> (<code>Dict[str, str]</code>, <em>optional</em>) &#x2014;
A dictionary of proxy servers to use by protocol or endpoint, e.g., <code>{&apos;http&apos;: &apos;foo.bar:3128&apos;, &apos;http://hostname&apos;: &apos;foo.bar:4012&apos;}</code>. The proxies are used on each request.`,name:"proxies"},{anchor:"transformers.AutoModelForZeroShotObjectDetection.from_pretrained.output_loading_info(bool,",description:`<strong>output_loading_info(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether ot not to also return a dictionary containing missing keys, unexpected keys and error messages.`,name:"output_loading_info(bool,"},{anchor:"transformers.AutoModelForZeroShotObjectDetection.from_pretrained.local_files_only(bool,",description:`<strong>local_files_only(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to only look at local files (e.g., not try downloading the model).`,name:"local_files_only(bool,"},{anchor:"transformers.AutoModelForZeroShotObjectDetection.from_pretrained.revision",description:`<strong>revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a
git-based system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any
identifier allowed by git.`,name:"revision"},{anchor:"transformers.AutoModelForZeroShotObjectDetection.from_pretrained.trust_remote_code",description:`<strong>trust_remote_code</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to allow for custom models defined on the Hub in their own modeling files. This option
should only be set to <code>True</code> for repositories you trust and in which you have read the code, as it will
execute code present on the Hub on your local machine.`,name:"trust_remote_code"},{anchor:"transformers.AutoModelForZeroShotObjectDetection.from_pretrained.kwargs",description:`<strong>kwargs</strong> (additional 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#L417"}}),ik=new D({props:{anchor:"transformers.AutoModelForZeroShotObjectDetection.from_pretrained.example",$$slots:{default:[Zgn]},$$scope:{ctx:k}}}),yG=new re({}),xG=new re({}),kG=new R({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#L1188"}}),SG=new R({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>
</ul>`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/auto/auto_factory.py#L389"}}),ck=new D({props:{anchor:"transformers.AutoModelForAudioClassification.from_config.example",$$slots:{default:[Kgn]},$$scope:{ctx:k}}}),RG=new R({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;
Will be passed along to the underlying model <code>__init__()</code> method.`,name:"model_args"},{anchor:"transformers.AutoModelForAudioClassification.from_pretrained.config",description:`<strong>config</strong> (<a href="/docs/transformers/main/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.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
<code>pretrained_model_name_or_path</code> argument).`,name:"from_tf"},{anchor:"transformers.AutoModelForAudioClassification.from_pretrained.force_download",description:`<strong>force_download</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to force the (re-)download of the model weights and configuration files, overriding the
cached versions if they exist.`,name:"force_download"},{anchor:"transformers.AutoModelForAudioClassification.from_pretrained.resume_download",description:`<strong>resume_download</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to delete incompletely received files. Will attempt to resume the download if such a
file exists.`,name:"resume_download"},{anchor:"transformers.AutoModelForAudioClassification.from_pretrained.proxies",description:`<strong>proxies</strong> (<code>Dict[str, str]</code>, <em>optional</em>) &#x2014;
A dictionary of proxy servers to use by protocol or endpoint, e.g., <code>{&apos;http&apos;: &apos;foo.bar:3128&apos;, &apos;http://hostname&apos;: &apos;foo.bar:4012&apos;}</code>. The proxies are used on each request.`,name:"proxies"},{anchor:"transformers.AutoModelForAudioClassification.from_pretrained.output_loading_info(bool,",description:`<strong>output_loading_info(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether ot not to also return a dictionary containing missing keys, unexpected keys and error messages.`,name:"output_loading_info(bool,"},{anchor:"transformers.AutoModelForAudioClassification.from_pretrained.local_files_only(bool,",description:`<strong>local_files_only(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to only look at local files (e.g., not try downloading the model).`,name:"local_files_only(bool,"},{anchor:"transformers.AutoModelForAudioClassification.from_pretrained.revision",description:`<strong>revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a
git-based system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any
identifier allowed by git.`,name:"revision"},{anchor:"transformers.AutoModelForAudioClassification.from_pretrained.trust_remote_code",description:`<strong>trust_remote_code</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to allow for custom models defined on the Hub in their own modeling files. This option
should only be set to <code>True</code> for repositories you trust and in which you have read the code, as it will
execute code present on the Hub on your local machine.`,name:"trust_remote_code"},{anchor:"transformers.AutoModelForAudioClassification.from_pretrained.kwargs",description:`<strong>kwargs</strong> (additional 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#L417"}}),Ek=new D({props:{anchor:"transformers.AutoModelForAudioClassification.from_pretrained.example",$$slots:{default:[ehn]},$$scope:{ctx:k}}}),PG=new re({}),IG=new R({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#L1211"}}),NG=new R({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#L389"}}),wk=new D({props:{anchor:"transformers.AutoModelForAudioFrameClassification.from_config.example",$$slots:{default:[ohn]},$$scope:{ctx:k}}}),DG=new R({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.kwargs",description:`<strong>kwargs</strong> (additional 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#L417"}}),Sk=new D({props:{anchor:"transformers.AutoModelForAudioFrameClassification.from_pretrained.example",$$slots:{default:[rhn]},$$scope:{ctx:k}}}),GG=new re({}),qG=new R({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#L1195"}}),OG=new R({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#L389"}}),Pk=new D({props:{anchor:"transformers.AutoModelForCTC.from_config.example",$$slots:{default:[thn]},$$scope:{ctx:k}}}),VG=new R({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.kwargs",description:`<strong>kwargs</strong> (additional 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#L417"}}),Wk=new D({props:{anchor:"transformers.AutoModelForCTC.from_pretrained.example",$$slots:{default:[ahn]},$$scope:{ctx:k}}}),XG=new re({}),zG=new R({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#L1202"}}),QG=new R({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/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/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#L389"}}),Uk=new D({props:{anchor:"transformers.AutoModelForSpeechSeq2Seq.from_config.example",$$slots:{default:[nhn]},$$scope:{ctx:k}}}),UG=new R({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.kwargs",description:`<strong>kwargs</strong> (additional 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#L417"}}),Kk=new D({props:{anchor:"transformers.AutoModelForSpeechSeq2Seq.from_pretrained.example",$$slots:{default:[shn]},$$scope:{ctx:k}}}),HG=new re({}),JG=new R({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#L602"}}),ZG=new R({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#L389"}}),o$=new D({props:{anchor:"transformers.TFAutoModelForSpeechSeq2Seq.from_config.example",$$slots:{default:[lhn]},$$scope:{ctx:k}}}),KG=new R({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>
</ul>`,name:"pretrained_model_name_or_path"},{anchor:"transformers.TFAutoModelForSpeechSeq2Seq.from_pretrained.model_args",description:`<strong>model_args</strong> (additional positional arguments, <em>optional</em>) &#x2014;
Will be passed along to the underlying model <code>__init__()</code> method.`,name:"model_args"},{anchor:"transformers.TFAutoModelForSpeechSeq2Seq.from_pretrained.config",description:`<strong>config</strong> (<a href="/docs/transformers/main/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.TFAutoModelForSpeechSeq2Seq.from_pretrained.cache_dir",description:`<strong>cache_dir</strong> (<code>str</code> or <code>os.PathLike</code>, <em>optional</em>) &#x2014;
Path to a directory in which a downloaded pretrained model configuration should be cached if the
standard cache should not be used.`,name:"cache_dir"},{anchor:"transformers.TFAutoModelForSpeechSeq2Seq.from_pretrained.from_pt",description:`<strong>from_pt</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Load the model weights from a PyTorch checkpoint save file (see docstring of
<code>pretrained_model_name_or_path</code> argument).`,name:"from_pt"},{anchor:"transformers.TFAutoModelForSpeechSeq2Seq.from_pretrained.force_download",description:`<strong>force_download</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to force the (re-)download of the model weights and configuration files, overriding the
cached versions if they exist.`,name:"force_download"},{anchor:"transformers.TFAutoModelForSpeechSeq2Seq.from_pretrained.resume_download",description:`<strong>resume_download</strong> (<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.TFAutoModelForSpeechSeq2Seq.from_pretrained.proxies",description:`<strong>proxies</strong> (<code>Dict[str, str]</code>, <em>optional</em>) &#x2014;
A dictionary of proxy servers to use by protocol or endpoint, e.g., <code>{&apos;http&apos;: &apos;foo.bar:3128&apos;, &apos;http://hostname&apos;: &apos;foo.bar:4012&apos;}</code>. The proxies are used on each request.`,name:"proxies"},{anchor:"transformers.TFAutoModelForSpeechSeq2Seq.from_pretrained.output_loading_info(bool,",description:`<strong>output_loading_info(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether ot not to also return a dictionary containing missing keys, unexpected keys and error messages.`,name:"output_loading_info(bool,"},{anchor:"transformers.TFAutoModelForSpeechSeq2Seq.from_pretrained.local_files_only(bool,",description:`<strong>local_files_only(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to only look at local files (e.g., not try downloading the model).`,name:"local_files_only(bool,"},{anchor:"transformers.TFAutoModelForSpeechSeq2Seq.from_pretrained.revision",description:`<strong>revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a
git-based system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any
identifier allowed by git.`,name:"revision"},{anchor:"transformers.TFAutoModelForSpeechSeq2Seq.from_pretrained.trust_remote_code",description:`<strong>trust_remote_code</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to allow for custom models defined on the Hub in their own modeling files. This option
should only be set to <code>True</code> for repositories you trust and in which you have read the code, as it will
execute code present on the Hub on your local machine.`,name:"trust_remote_code"},{anchor:"transformers.TFAutoModelForSpeechSeq2Seq.from_pretrained.kwargs",description:`<strong>kwargs</strong> (additional 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#L417"}}),a$=new D({props:{anchor:"transformers.TFAutoModelForSpeechSeq2Seq.from_pretrained.example",$$slots:{default:[ihn]},$$scope:{ctx:k}}}),oq=new re({}),rq=new R({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#L1220"}}),aq=new R({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#L389"}}),s$=new D({props:{anchor:"transformers.AutoModelForAudioXVector.from_config.example",$$slots:{default:[dhn]},$$scope:{ctx:k}}}),nq=new R({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.kwargs",description:`<strong>kwargs</strong> (additional 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#L417"}}),g$=new D({props:{anchor:"transformers.AutoModelForAudioXVector.from_pretrained.example",$$slots:{default:[mhn]},$$scope:{ctx:k}}}),sq=new re({}),lq=new re({}),iq=new R({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#L1054"}}),mq=new R({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#L389"}}),p$=new D({props:{anchor:"transformers.AutoModelForTableQuestionAnswering.from_config.example",$$slots:{default:[chn]},$$scope:{ctx:k}}}),cq=new R({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.kwargs",description:`<strong>kwargs</strong> (additional 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#L417"}}),v$=new D({props:{anchor:"transformers.AutoModelForTableQuestionAnswering.from_pretrained.example",$$slots:{default:[fhn]},$$scope:{ctx:k}}}),fq=new re({}),gq=new R({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#L566"}}),uq=new R({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#L389"}}),F$=new D({props:{anchor:"transformers.TFAutoModelForTableQuestionAnswering.from_config.example",$$slots:{default:[ghn]},$$scope:{ctx:k}}}),pq=new R({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.kwargs",description:`<strong>kwargs</strong> (additional 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#L417"}}),E$=new D({props:{anchor:"transformers.TFAutoModelForTableQuestionAnswering.from_pretrained.example",$$slots:{default:[hhn]},$$scope:{ctx:k}}}),_q=new re({}),bq=new R({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#L1076"}}),Tq=new R({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#L389"}}),w$=new D({props:{anchor:"transformers.AutoModelForDocumentQuestionAnswering.from_config.example",$$slots:{default:[uhn]},$$scope:{ctx:k}}}),Fq=new R({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.kwargs",description:`<strong>kwargs</strong> (additional 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#L417"}}),k$=new D({props:{anchor:"transformers.AutoModelForDocumentQuestionAnswering.from_pretrained.example",$$slots:{default:[phn]},$$scope:{ctx:k}}}),Mq=new re({}),Eq=new R({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#L555"}}),wq=new R({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#L389"}}),S$=new D({props:{anchor:"transformers.TFAutoModelForDocumentQuestionAnswering.from_config.example",$$slots:{default:[_hn]},$$scope:{ctx:k}}}),Aq=new R({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
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</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
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Load the model weights from a PyTorch checkpoint save file (see docstring of
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Whether or not to force the (re-)download of the model weights and configuration files, overriding the
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Whether or not to delete incompletely received files. Will attempt to resume the download if such a
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A dictionary of proxy servers to use by protocol or endpoint, e.g., <code>{&apos;http&apos;: &apos;foo.bar:3128&apos;, &apos;http://hostname&apos;: &apos;foo.bar:4012&apos;}</code>. The proxies are used on each request.`,name:"proxies"},{anchor:"transformers.TFAutoModelForDocumentQuestionAnswering.from_pretrained.output_loading_info(bool,",description:`<strong>output_loading_info(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
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The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a
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Whether or not to allow for custom models defined on the Hub in their own modeling files. This option
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Can be used to update the configuration object (after it being loaded) and initiate the model (e.g.,
<code>output_attentions=True</code>). Behaves differently depending on whether a <code>config</code> is provided 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#L417"}}),P$=new D({props:{anchor:"transformers.TFAutoModelForDocumentQuestionAnswering.from_pretrained.example",$$slots:{default:[bhn]},$$scope:{ctx:k}}}),Lq=new re({}),yq=new R({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#L1065"}}),kq=new R({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#L389"}}),B$=new D({props:{anchor:"transformers.AutoModelForVisualQuestionAnswering.from_config.example",$$slots:{default:[vhn]},$$scope:{ctx:k}}}),$q=new R({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
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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.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.kwargs",description:`<strong>kwargs</strong> (additional 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#L417"}}),G$=new D({props:{anchor:"transformers.AutoModelForVisualQuestionAnswering.from_pretrained.example",$$slots:{default:[Thn]},$$scope:{ctx:k}}}),Sq=new re({}),Rq=new R({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#L1181"}}),Iq=new R({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/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#L389"}}),j$=new D({props:{anchor:"transformers.AutoModelForVision2Seq.from_config.example",$$slots:{default:[Fhn]},$$scope:{ctx:k}}}),Bq=new R({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.kwargs",description:`<strong>kwargs</strong> (additional 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#L417"}}),X$=new D({props:{anchor:"transformers.AutoModelForVision2Seq.from_pretrained.example",$$slots:{default:[Mhn]},$$scope:{ctx:k}}}),Nq=new re({}),Dq=new R({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#L516"}}),qq=new R({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/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#L389"}}),W$=new D({props:{anchor:"transformers.TFAutoModelForVision2Seq.from_config.example",$$slots:{default:[Ehn]},$$scope:{ctx:k}}}),jq=new R({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
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<li>A path to a <em>directory</em> containing model weights saved using
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model)"),Rrr=l(),A2=a("li"),h5e=a("strong"),Prr=o("vit"),Irr=o(" \u2014 "),gY=a("a"),Brr=o("ViTImageProcessor"),Nrr=o(" (ViT model)"),Drr=l(),L2=a("li"),u5e=a("strong"),Grr=o("vit_hybrid"),qrr=o(" \u2014 "),hY=a("a"),jrr=o("ViTHybridImageProcessor"),Orr=o(" (ViT Hybrid model)"),Vrr=l(),y2=a("li"),p5e=a("strong"),Xrr=o("vit_mae"),zrr=o(" \u2014 "),uY=a("a"),Wrr=o("ViTImageProcessor"),Qrr=o(" (ViTMAE model)"),Urr=l(),x2=a("li"),_5e=a("strong"),Hrr=o("vit_msn"),Jrr=o(" \u2014 "),pY=a("a"),Yrr=o("ViTImageProcessor"),Zrr=o(" (ViTMSN model)"),Krr=l(),k2=a("li"),b5e=a("strong"),etr=o("xclip"),otr=o(" \u2014 "),_Y=a("a"),rtr=o("CLIPImageProcessor"),ttr=o(" (X-CLIP model)"),atr=l(),$2=a("li"),v5e=a("strong"),ntr=o("yolos"),str=o(" \u2014 "),bY=a("a"),ltr=o("YolosImageProcessor"),itr=o(" (YOLOS model)"),dtr=l(),v(S2.$$.fragment),mtr=l(),v(R2.$$.fragment),ctr=l(),P2=a("div"),v(hI.$$.fragment),ftr=l(),T5e=a("p"),gtr=o("Register a new image processor for this class."),dvo=l(),em=a("h2"),I2=a("a"),F5e=a("span"),v(uI.$$.fragment),htr=l(),M5e=a("span"),utr=o("AutoProcessor"),mvo=l(),jo=a("div"),v(pI.$$.fragment),ptr=l(),_I=a("p"),_tr=o(`This is a generic processor class that will be instantiated as one of the processor classes of the library when
created with the `),vY=a("a"),btr=o("AutoProcessor.from_pretrained()"),vtr=o(" class method."),Ttr=l(),bI=a("p"),Ftr=o("This class cannot be instantiated directly using "),E5e=a("code"),Mtr=o("__init__()"),Etr=o(" (throws an error)."),Ctr=l(),no=a("div"),v(vI.$$.fragment),wtr=l(),C5e=a("p"),Atr=o("Instantiate one of the processor classes of the library from a pretrained model vocabulary."),Ltr=l(),om=a("p"),ytr=o("The processor class to instantiate is selected based on the "),w5e=a("code"),xtr=o("model_type"),ktr=o(` property of the config object (either
passed as an argument or loaded from `),A5e=a("code"),$tr=o("pretrained_model_name_or_path"),Str=o(" if possible):"),Rtr=l(),ne=a("ul"),B2=a("li"),L5e=a("strong"),Ptr=o("altclip"),Itr=o(" \u2014 "),TY=a("a"),Btr=o("AltCLIPProcessor"),Ntr=o(" (AltCLIP model)"),Dtr=l(),N2=a("li"),y5e=a("strong"),Gtr=o("blip"),qtr=o(" \u2014 "),x5e=a("code"),jtr=o("BLIPProcessor"),Otr=o(" (BLIP model)"),Vtr=l(),D2=a("li"),k5e=a("strong"),Xtr=o("bridgetower"),ztr=o(" \u2014 "),FY=a("a"),Wtr=o("BridgeTowerProcessor"),Qtr=o(" (BridgeTower model)"),Utr=l(),G2=a("li"),$5e=a("strong"),Htr=o("chinese_clip"),Jtr=o(" \u2014 "),MY=a("a"),Ytr=o("ChineseCLIPProcessor"),Ztr=o(" (Chinese-CLIP model)"),Ktr=l(),q2=a("li"),S5e=a("strong"),ear=o("clip"),oar=o(" \u2014 "),EY=a("a"),rar=o("CLIPProcessor"),tar=o(" (CLIP model)"),aar=l(),j2=a("li"),R5e=a("strong"),nar=o("clipseg"),sar=o(" \u2014 "),CY=a("a"),lar=o("CLIPSegProcessor"),iar=o(" (CLIPSeg model)"),dar=l(),O2=a("li"),P5e=a("strong"),mar=o("flava"),car=o(" \u2014 "),wY=a("a"),far=o("FlavaProcessor"),gar=o(" (FLAVA model)"),har=l(),V2=a("li"),I5e=a("strong"),uar=o("git"),par=o(" \u2014 "),B5e=a("code"),_ar=o("GITProcessor"),bar=o(" (GIT model)"),Tar=l(),X2=a("li"),N5e=a("strong"),Far=o("groupvit"),Mar=o(" \u2014 "),AY=a("a"),Ear=o("CLIPProcessor"),Car=o(" (GroupViT model)"),war=l(),z2=a("li"),D5e=a("strong"),Aar=o("hubert"),Lar=o(" \u2014 "),LY=a("a"),yar=o("Wav2Vec2Processor"),xar=o(" (Hubert model)"),kar=l(),W2=a("li"),G5e=a("strong"),$ar=o("layoutlmv2"),Sar=o(" \u2014 "),yY=a("a"),Rar=o("LayoutLMv2Processor"),Par=o(" (LayoutLMv2 model)"),Iar=l(),Q2=a("li"),q5e=a("strong"),Bar=o("layoutlmv3"),Nar=o(" \u2014 "),xY=a("a"),Dar=o("LayoutLMv3Processor"),Gar=o(" (LayoutLMv3 model)"),qar=l(),U2=a("li"),j5e=a("strong"),jar=o("layoutxlm"),Oar=o(" \u2014 "),kY=a("a"),Var=o("LayoutXLMProcessor"),Xar=o(" (LayoutXLM model)"),zar=l(),H2=a("li"),O5e=a("strong"),War=o("markuplm"),Qar=o(" \u2014 "),$Y=a("a"),Uar=o("MarkupLMProcessor"),Har=o(" (MarkupLM model)"),Jar=l(),J2=a("li"),V5e=a("strong"),Yar=o("oneformer"),Zar=o(" \u2014 "),SY=a("a"),Kar=o("OneFormerProcessor"),enr=o(" (OneFormer model)"),onr=l(),Y2=a("li"),X5e=a("strong"),rnr=o("owlvit"),tnr=o(" \u2014 "),RY=a("a"),anr=o("OwlViTProcessor"),nnr=o(" (OWL-ViT model)"),snr=l(),Z2=a("li"),z5e=a("strong"),lnr=o("sew"),inr=o(" \u2014 "),PY=a("a"),dnr=o("Wav2Vec2Processor"),mnr=o(" (SEW model)"),cnr=l(),K2=a("li"),W5e=a("strong"),fnr=o("sew-d"),gnr=o(" \u2014 "),IY=a("a"),hnr=o("Wav2Vec2Processor"),unr=o(" (SEW-D model)"),pnr=l(),eb=a("li"),Q5e=a("strong"),_nr=o("speech_to_text"),bnr=o(" \u2014 "),BY=a("a"),vnr=o("Speech2TextProcessor"),Tnr=o(" (Speech2Text model)"),Fnr=l(),ob=a("li"),U5e=a("strong"),Mnr=o("speech_to_text_2"),Enr=o(" \u2014 "),NY=a("a"),Cnr=o("Speech2Text2Processor"),wnr=o(" (Speech2Text2 model)"),Anr=l(),rb=a("li"),H5e=a("strong"),Lnr=o("trocr"),ynr=o(" \u2014 "),DY=a("a"),xnr=o("TrOCRProcessor"),knr=o(" (TrOCR model)"),$nr=l(),tb=a("li"),J5e=a("strong"),Snr=o("unispeech"),Rnr=o(" \u2014 "),GY=a("a"),Pnr=o("Wav2Vec2Processor"),Inr=o(" (UniSpeech model)"),Bnr=l(),ab=a("li"),Y5e=a("strong"),Nnr=o("unispeech-sat"),Dnr=o(" \u2014 "),qY=a("a"),Gnr=o("Wav2Vec2Processor"),qnr=o(" (UniSpeechSat model)"),jnr=l(),nb=a("li"),Z5e=a("strong"),Onr=o("vilt"),Vnr=o(" \u2014 "),jY=a("a"),Xnr=o("ViltProcessor"),znr=o(" (ViLT model)"),Wnr=l(),sb=a("li"),K5e=a("strong"),Qnr=o("vision-text-dual-encoder"),Unr=o(" \u2014 "),OY=a("a"),Hnr=o("VisionTextDualEncoderProcessor"),Jnr=o(" (VisionTextDualEncoder model)"),Ynr=l(),lb=a("li"),e0e=a("strong"),Znr=o("wav2vec2"),Knr=o(" \u2014 "),VY=a("a"),esr=o("Wav2Vec2Processor"),osr=o(" (Wav2Vec2 model)"),rsr=l(),ib=a("li"),o0e=a("strong"),tsr=o("wav2vec2-conformer"),asr=o(" \u2014 "),XY=a("a"),nsr=o("Wav2Vec2Processor"),ssr=o(" (Wav2Vec2-Conformer model)"),lsr=l(),db=a("li"),r0e=a("strong"),isr=o("wavlm"),dsr=o(" \u2014 "),zY=a("a"),msr=o("Wav2Vec2Processor"),csr=o(" (WavLM model)"),fsr=l(),mb=a("li"),t0e=a("strong"),gsr=o("whisper"),hsr=o(" \u2014 "),WY=a("a"),usr=o("WhisperProcessor"),psr=o(" (Whisper model)"),_sr=l(),cb=a("li"),a0e=a("strong"),bsr=o("xclip"),vsr=o(" \u2014 "),QY=a("a"),Tsr=o("XCLIPProcessor"),Fsr=o(" (X-CLIP model)"),Msr=l(),v(fb.$$.fragment),Esr=l(),v(gb.$$.fragment),Csr=l(),hb=a("div"),v(TI.$$.fragment),wsr=l(),n0e=a("p"),Asr=o("Register a new processor for this class."),cvo=l(),rm=a("h2"),ub=a("a"),s0e=a("span"),v(FI.$$.fragment),Lsr=l(),l0e=a("span"),ysr=o("Generic model classes"),fvo=l(),UY=a("p"),xsr=o("The following auto classes are available for instantiating a base model class without a specific head."),gvo=l(),tm=a("h3"),pb=a("a"),i0e=a("span"),v(MI.$$.fragment),ksr=l(),d0e=a("span"),$sr=o("AutoModel"),hvo=l(),Oo=a("div"),v(EI.$$.fragment),Ssr=l(),am=a("p"),Rsr=o(`This is a generic model class that will be instantiated as one of the base model classes of the library when created
with the `),HY=a("a"),Psr=o("from_pretrained()"),Isr=o(" class method or the "),JY=a("a"),Bsr=o("from_config()"),Nsr=o(` class
method.`),Dsr=l(),CI=a("p"),Gsr=o("This class cannot be instantiated directly using "),m0e=a("code"),qsr=o("__init__()"),jsr=o(" (throws an error)."),Osr=l(),xt=a("div"),v(wI.$$.fragment),Vsr=l(),c0e=a("p"),Xsr=o("Instantiates one of the base model classes of the library from a configuration."),zsr=l(),nm=a("p"),Wsr=o(`Note:
Loading a model from its configuration file does `),f0e=a("strong"),Qsr=o("not"),Usr=o(` load the model weights. It only affects the
model\u2019s configuration. Use `),YY=a("a"),Hsr=o("from_pretrained()"),Jsr=o(" to load the model weights."),Ysr=l(),v(_b.$$.fragment),Zsr=l(),so=a("div"),v(AI.$$.fragment),Ksr=l(),g0e=a("p"),elr=o("Instantiate one of the base model classes of the library from a pretrained model."),olr=l(),bn=a("p"),rlr=o("The model class to instantiate is selected based on the "),h0e=a("code"),tlr=o("model_type"),alr=o(` property of the config object (either
passed as an argument or loaded from `),u0e=a("code"),nlr=o("pretrained_model_name_or_path"),slr=o(` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),p0e=a("code"),llr=o("pretrained_model_name_or_path"),ilr=o(":"),dlr=l(),y=a("ul"),bb=a("li"),_0e=a("strong"),mlr=o("albert"),clr=o(" \u2014 "),ZY=a("a"),flr=o("AlbertModel"),glr=o(" (ALBERT model)"),hlr=l(),vb=a("li"),b0e=a("strong"),ulr=o("altclip"),plr=o(" \u2014 "),KY=a("a"),_lr=o("AltCLIPModel"),blr=o(" (AltCLIP model)"),vlr=l(),Tb=a("li"),v0e=a("strong"),Tlr=o("audio-spectrogram-transformer"),Flr=o(" \u2014 "),eZ=a("a"),Mlr=o("ASTModel"),Elr=o(" (Audio Spectrogram Transformer model)"),Clr=l(),Fb=a("li"),T0e=a("strong"),wlr=o("bart"),Alr=o(" \u2014 "),oZ=a("a"),Llr=o("BartModel"),ylr=o(" (BART model)"),xlr=l(),Mb=a("li"),F0e=a("strong"),klr=o("beit"),$lr=o(" \u2014 "),rZ=a("a"),Slr=o("BeitModel"),Rlr=o(" (BEiT model)"),Plr=l(),Eb=a("li"),M0e=a("strong"),Ilr=o("bert"),Blr=o(" \u2014 "),tZ=a("a"),Nlr=o("BertModel"),Dlr=o(" (BERT model)"),Glr=l(),Cb=a("li"),E0e=a("strong"),qlr=o("bert-generation"),jlr=o(" \u2014 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model\u2019s configuration. Use `),are=a("a"),bCr=o("from_pretrained()"),vCr=o(" to load the model weights."),TCr=l(),v(MM.$$.fragment),FCr=l(),Hr=a("div"),v(BI.$$.fragment),MCr=l(),PAe=a("p"),ECr=o("Instantiate one of the base model classes of the library from a pretrained model."),CCr=l(),Tn=a("p"),wCr=o("The model class to instantiate is selected based on the "),IAe=a("code"),ACr=o("model_type"),LCr=o(` property of the config object (either
passed as an argument or loaded from `),BAe=a("code"),yCr=o("pretrained_model_name_or_path"),xCr=o(` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),NAe=a("code"),kCr=o("pretrained_model_name_or_path"),$Cr=o(":"),SCr=l(),le=a("ul"),EM=a("li"),DAe=a("strong"),RCr=o("albert"),PCr=o(" \u2014 "),nre=a("a"),ICr=o("FlaxAlbertModel"),BCr=o(" (ALBERT model)"),NCr=l(),CM=a("li"),GAe=a("strong"),DCr=o("bart"),GCr=o(" \u2014 "),sre=a("a"),qCr=o("FlaxBartModel"),jCr=o(" (BART model)"),OCr=l(),wM=a("li"),qAe=a("strong"),VCr=o("beit"),XCr=o(" \u2014 "),lre=a("a"),zCr=o("FlaxBeitModel"),WCr=o(" (BEiT model)"),QCr=l(),AM=a("li"),jAe=a("strong"),UCr=o("bert"),HCr=o(" \u2014 "),ire=a("a"),JCr=o("FlaxBertModel"),YCr=o(" (BERT model)"),ZCr=l(),LM=a("li"),OAe=a("strong"),KCr=o("big_bird"),e5r=o(" \u2014 "),dre=a("a"),o5r=o("FlaxBigBirdModel"),r5r=o(" (BigBird model)"),t5r=l(),yM=a("li"),VAe=a("strong"),a5r=o("blenderbot"),n5r=o(" \u2014 "),mre=a("a"),s5r=o("FlaxBlenderbotModel"),l5r=o(" (Blenderbot model)"),i5r=l(),xM=a("li"),XAe=a("strong"),d5r=o("blenderbot-small"),m5r=o(" \u2014 "),cre=a("a"),c5r=o("FlaxBlenderbotSmallModel"),f5r=o(" (BlenderbotSmall model)"),g5r=l(),kM=a("li"),zAe=a("strong"),h5r=o("clip"),u5r=o(" \u2014 "),fre=a("a"),p5r=o("FlaxCLIPModel"),_5r=o(" (CLIP model)"),b5r=l(),$M=a("li"),WAe=a("strong"),v5r=o("distilbert"),T5r=o(" \u2014 "),gre=a("a"),F5r=o("FlaxDistilBertModel"),M5r=o(" (DistilBERT model)"),E5r=l(),SM=a("li"),QAe=a("strong"),C5r=o("electra"),w5r=o(" \u2014 "),hre=a("a"),A5r=o("FlaxElectraModel"),L5r=o(" (ELECTRA model)"),y5r=l(),RM=a("li"),UAe=a("strong"),x5r=o("gpt-sw3"),k5r=o(" \u2014 "),ure=a("a"),$5r=o("FlaxGPT2Model"),S5r=o(" (GPT-Sw3 model)"),R5r=l(),PM=a("li"),HAe=a("strong"),P5r=o("gpt2"),I5r=o(" \u2014 "),pre=a("a"),B5r=o("FlaxGPT2Model"),N5r=o(" (OpenAI GPT-2 model)"),D5r=l(),IM=a("li"),JAe=a("strong"),G5r=o("gpt_neo"),q5r=o(" \u2014 "),_re=a("a"),j5r=o("FlaxGPTNeoModel"),O5r=o(" (GPT Neo model)"),V5r=l(),BM=a("li"),YAe=a("strong"),X5r=o("gptj"),z5r=o(" \u2014 "),bre=a("a"),W5r=o("FlaxGPTJModel"),Q5r=o(" (GPT-J model)"),U5r=l(),NM=a("li"),ZAe=a("strong"),H5r=o("longt5"),J5r=o(" \u2014 "),vre=a("a"),Y5r=o("FlaxLongT5Model"),Z5r=o(" (LongT5 model)"),K5r=l(),DM=a("li"),KAe=a("strong"),e0r=o("marian"),o0r=o(" \u2014 "),Tre=a("a"),r0r=o("FlaxMarianModel"),t0r=o(" (Marian model)"),a0r=l(),GM=a("li"),e7e=a("strong"),n0r=o("mbart"),s0r=o(" \u2014 "),Fre=a("a"),l0r=o("FlaxMBartModel"),i0r=o(" (mBART model)"),d0r=l(),qM=a("li"),o7e=a("strong"),m0r=o("mt5"),c0r=o(" \u2014 "),Mre=a("a"),f0r=o("FlaxMT5Model"),g0r=o(" (MT5 model)"),h0r=l(),jM=a("li"),r7e=a("strong"),u0r=o("opt"),p0r=o(" \u2014 "),Ere=a("a"),_0r=o("FlaxOPTModel"),b0r=o(" (OPT model)"),v0r=l(),OM=a("li"),t7e=a("strong"),T0r=o("pegasus"),F0r=o(" \u2014 "),Cre=a("a"),M0r=o("FlaxPegasusModel"),E0r=o(" (Pegasus model)"),C0r=l(),VM=a("li"),a7e=a("strong"),w0r=o("roberta"),A0r=o(" \u2014 "),wre=a("a"),L0r=o("FlaxRobertaModel"),y0r=o(" (RoBERTa model)"),x0r=l(),XM=a("li"),n7e=a("strong"),k0r=o("roberta-prelayernorm"),$0r=o(" \u2014 "),Are=a("a"),S0r=o("FlaxRobertaPreLayerNormModel"),R0r=o(" (RoBERTa-PreLayerNorm model)"),P0r=l(),zM=a("li"),s7e=a("strong"),I0r=o("roformer"),B0r=o(" \u2014 "),Lre=a("a"),N0r=o("FlaxRoFormerModel"),D0r=o(" (RoFormer model)"),G0r=l(),WM=a("li"),l7e=a("strong"),q0r=o("t5"),j0r=o(" \u2014 "),yre=a("a"),O0r=o("FlaxT5Model"),V0r=o(" (T5 model)"),X0r=l(),QM=a("li"),i7e=a("strong"),z0r=o("vision-text-dual-encoder"),W0r=o(" \u2014 "),xre=a("a"),Q0r=o("FlaxVisionTextDualEncoderModel"),U0r=o(" (VisionTextDualEncoder model)"),H0r=l(),UM=a("li"),d7e=a("strong"),J0r=o("vit"),Y0r=o(" \u2014 "),kre=a("a"),Z0r=o("FlaxViTModel"),K0r=o(" (ViT model)"),ewr=l(),HM=a("li"),m7e=a("strong"),owr=o("wav2vec2"),rwr=o(" \u2014 "),$re=a("a"),twr=o("FlaxWav2Vec2Model"),awr=o(" (Wav2Vec2 model)"),nwr=l(),JM=a("li"),c7e=a("strong"),swr=o("xglm"),lwr=o(" \u2014 "),Sre=a("a"),iwr=o("FlaxXGLMModel"),dwr=o(" (XGLM model)"),mwr=l(),YM=a("li"),f7e=a("strong"),cwr=o("xlm-roberta"),fwr=o(" \u2014 "),Rre=a("a"),gwr=o("FlaxXLMRobertaModel"),hwr=o(" (XLM-RoBERTa model)"),uwr=l(),v(ZM.$$.fragment),vvo=l(),fm=a("h2"),KM=a("a"),g7e=a("span"),v(NI.$$.fragment),pwr=l(),h7e=a("span"),_wr=o("Generic pretraining classes"),Tvo=l(),Pre=a("p"),bwr=o("The following auto classes are available for instantiating a model with a pretraining head."),Fvo=l(),gm=a("h3"),eE=a("a"),u7e=a("span"),v(DI.$$.fragment),vwr=l(),p7e=a("span"),Twr=o("AutoModelForPreTraining"),Mvo=l(),zo=a("div"),v(GI.$$.fragment),Fwr=l(),hm=a("p"),Mwr=o(`This is a generic model class that will be instantiated as one of the model classes of the library (with a pretraining head) when created
with the `),Ire=a("a"),Ewr=o("from_pretrained()"),Cwr=o(" class method or the "),Bre=a("a"),wwr=o("from_config()"),Awr=o(` class
method.`),Lwr=l(),qI=a("p"),ywr=o("This class cannot be instantiated directly using "),_7e=a("code"),xwr=o("__init__()"),kwr=o(" (throws an error)."),$wr=l(),St=a("div"),v(jI.$$.fragment),Swr=l(),b7e=a("p"),Rwr=o("Instantiates one of the model classes of the library (with a pretraining head) from a configuration."),Pwr=l(),um=a("p"),Iwr=o(`Note:
Loading a model from its configuration file does `),v7e=a("strong"),Bwr=o("not"),Nwr=o(` load the model weights. It only affects the
model\u2019s configuration. Use `),Nre=a("a"),Dwr=o("from_pretrained()"),Gwr=o(" to load the model weights."),qwr=l(),v(oE.$$.fragment),jwr=l(),lo=a("div"),v(OI.$$.fragment),Owr=l(),T7e=a("p"),Vwr=o("Instantiate one of the model classes of the library (with a pretraining head) from a pretrained model."),Xwr=l(),Fn=a("p"),zwr=o("The model class to instantiate is selected based on the "),F7e=a("code"),Wwr=o("model_type"),Qwr=o(` property of the config object (either
passed as an argument or loaded from `),M7e=a("code"),Uwr=o("pretrained_model_name_or_path"),Hwr=o(` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),E7e=a("code"),Jwr=o("pretrained_model_name_or_path"),Ywr=o(":"),Zwr=l(),z=a("ul"),rE=a("li"),C7e=a("strong"),Kwr=o("albert"),e6r=o(" \u2014 "),Dre=a("a"),o6r=o("AlbertForPreTraining"),r6r=o(" (ALBERT model)"),t6r=l(),tE=a("li"),w7e=a("strong"),a6r=o("bart"),n6r=o(" \u2014 "),Gre=a("a"),s6r=o("BartForConditionalGeneration"),l6r=o(" (BART model)"),i6r=l(),aE=a("li"),A7e=a("strong"),d6r=o("bert"),m6r=o(" \u2014 "),qre=a("a"),c6r=o("BertForPreTraining"),f6r=o(" (BERT model)"),g6r=l(),nE=a("li"),L7e=a("strong"),h6r=o("big_bird"),u6r=o(" \u2014 "),jre=a("a"),p6r=o("BigBirdForPreTraining"),_6r=o(" (BigBird model)"),b6r=l(),sE=a("li"),y7e=a("strong"),v6r=o("bloom"),T6r=o(" \u2014 "),Ore=a("a"),F6r=o("BloomForCausalLM"),M6r=o(" (BLOOM model)"),E6r=l(),lE=a("li"),x7e=a("strong"),C6r=o("camembert"),w6r=o(" \u2014 "),Vre=a("a"),A6r=o("CamembertForMaskedLM"),L6r=o(" (CamemBERT model)"),y6r=l(),iE=a("li"),k7e=a("strong"),x6r=o("ctrl"),k6r=o(" \u2014 "),Xre=a("a"),$6r=o("CTRLLMHeadModel"),S6r=o(" (CTRL model)"),R6r=l(),dE=a("li"),$7e=a("strong"),P6r=o("data2vec-text"),I6r=o(" \u2014 "),zre=a("a"),B6r=o("Data2VecTextForMaskedLM"),N6r=o(" (Data2VecText model)"),D6r=l(),mE=a("li"),S7e=a("strong"),G6r=o("deberta"),q6r=o(" \u2014 "),Wre=a("a"),j6r=o("DebertaForMaskedLM"),O6r=o(" (DeBERTa model)"),V6r=l(),cE=a("li"),R7e=a("strong"),X6r=o("deberta-v2"),z6r=o(" \u2014 "),Qre=a("a"),W6r=o("DebertaV2ForMaskedLM"),Q6r=o(" (DeBERTa-v2 model)"),U6r=l(),fE=a("li"),P7e=a("strong"),H6r=o("distilbert"),J6r=o(" \u2014 "),Ure=a("a"),Y6r=o("DistilBertForMaskedLM"),Z6r=o(" (DistilBERT model)"),K6r=l(),gE=a("li"),I7e=a("strong"),e8r=o("electra"),o8r=o(" \u2014 "),Hre=a("a"),r8r=o("ElectraForPreTraining"),t8r=o(" (ELECTRA model)"),a8r=l(),hE=a("li"),B7e=a("strong"),n8r=o("ernie"),s8r=o(" \u2014 "),Jre=a("a"),l8r=o("ErnieForPreTraining"),i8r=o(" (ERNIE model)"),d8r=l(),uE=a("li"),N7e=a("strong"),m8r=o("flaubert"),c8r=o(" \u2014 "),Yre=a("a"),f8r=o("FlaubertWithLMHeadModel"),g8r=o(" (FlauBERT model)"),h8r=l(),pE=a("li"),D7e=a("strong"),u8r=o("flava"),p8r=o(" \u2014 "),Zre=a("a"),_8r=o("FlavaForPreTraining"),b8r=o(" (FLAVA model)"),v8r=l(),_E=a("li"),G7e=a("strong"),T8r=o("fnet"),F8r=o(" \u2014 "),Kre=a("a"),M8r=o("FNetForPreTraining"),E8r=o(" (FNet model)"),C8r=l(),bE=a("li"),q7e=a("strong"),w8r=o("fsmt"),A8r=o(" \u2014 "),ete=a("a"),L8r=o("FSMTForConditionalGeneration"),y8r=o(" (FairSeq Machine-Translation model)"),x8r=l(),vE=a("li"),j7e=a("strong"),k8r=o("funnel"),$8r=o(" \u2014 "),ote=a("a"),S8r=o("FunnelForPreTraining"),R8r=o(" (Funnel Transformer model)"),P8r=l(),TE=a("li"),O7e=a("strong"),I8r=o("gpt-sw3"),B8r=o(" \u2014 "),rte=a("a"),N8r=o("GPT2LMHeadModel"),D8r=o(" (GPT-Sw3 model)"),G8r=l(),FE=a("li"),V7e=a("strong"),q8r=o("gpt2"),j8r=o(" \u2014 "),tte=a("a"),O8r=o("GPT2LMHeadModel"),V8r=o(" (OpenAI GPT-2 model)"),X8r=l(),ME=a("li"),X7e=a("strong"),z8r=o("ibert"),W8r=o(" \u2014 "),ate=a("a"),Q8r=o("IBertForMaskedLM"),U8r=o(" (I-BERT model)"),H8r=l(),EE=a("li"),z7e=a("strong"),J8r=o("layoutlm"),Y8r=o(" \u2014 "),nte=a("a"),Z8r=o("LayoutLMForMaskedLM"),K8r=o(" (LayoutLM model)"),eAr=l(),CE=a("li"),W7e=a("strong"),oAr=o("longformer"),rAr=o(" \u2014 "),ste=a("a"),tAr=o("LongformerForMaskedLM"),aAr=o(" (Longformer model)"),nAr=l(),wE=a("li"),Q7e=a("strong"),sAr=o("luke"),lAr=o(" \u2014 "),lte=a("a"),iAr=o("LukeForMaskedLM"),dAr=o(" (LUKE model)"),mAr=l(),AE=a("li"),U7e=a("strong"),cAr=o("lxmert"),fAr=o(" \u2014 "),ite=a("a"),gAr=o("LxmertForPreTraining"),hAr=o(" (LXMERT model)"),uAr=l(),LE=a("li"),H7e=a("strong"),pAr=o("megatron-bert"),_Ar=o(" \u2014 "),dte=a("a"),bAr=o("MegatronBertForPreTraining"),vAr=o(" (Megatron-BERT model)"),TAr=l(),yE=a("li"),J7e=a("strong"),FAr=o("mobilebert"),MAr=o(" \u2014 "),mte=a("a"),EAr=o("MobileBertForPreTraining"),CAr=o(" (MobileBERT model)"),wAr=l(),xE=a("li"),Y7e=a("strong"),AAr=o("mpnet"),LAr=o(" \u2014 "),cte=a("a"),yAr=o("MPNetForMaskedLM"),xAr=o(" (MPNet model)"),kAr=l(),kE=a("li"),Z7e=a("strong"),$Ar=o("mvp"),SAr=o(" \u2014 "),fte=a("a"),RAr=o("MvpForConditionalGeneration"),PAr=o(" (MVP model)"),IAr=l(),$E=a("li"),K7e=a("strong"),BAr=o("nezha"),NAr=o(" \u2014 "),gte=a("a"),DAr=o("NezhaForPreTraining"),GAr=o(" (Nezha model)"),qAr=l(),SE=a("li"),eLe=a("strong"),jAr=o("openai-gpt"),OAr=o(" \u2014 "),hte=a("a"),VAr=o("OpenAIGPTLMHeadModel"),XAr=o(" (OpenAI GPT model)"),zAr=l(),RE=a("li"),oLe=a("strong"),WAr=o("retribert"),QAr=o(" \u2014 "),ute=a("a"),UAr=o("RetriBertModel"),HAr=o(" (RetriBERT model)"),JAr=l(),PE=a("li"),rLe=a("strong"),YAr=o("roberta"),ZAr=o(" \u2014 "),pte=a("a"),KAr=o("RobertaForMaskedLM"),e7r=o(" (RoBERTa model)"),o7r=l(),IE=a("li"),tLe=a("strong"),r7r=o("roberta-prelayernorm"),t7r=o(" \u2014 "),_te=a("a"),a7r=o("RobertaPreLayerNormForMaskedLM"),n7r=o(" (RoBERTa-PreLayerNorm model)"),s7r=l(),BE=a("li"),aLe=a("strong"),l7r=o("roc_bert"),i7r=o(" \u2014 "),bte=a("a"),d7r=o("RoCBertForPreTraining"),m7r=o(" (RoCBert model)"),c7r=l(),NE=a("li"),nLe=a("strong"),f7r=o("splinter"),g7r=o(" \u2014 "),vte=a("a"),h7r=o("SplinterForPreTraining"),u7r=o(" (Splinter model)"),p7r=l(),DE=a("li"),sLe=a("strong"),_7r=o("squeezebert"),b7r=o(" \u2014 "),Tte=a("a"),v7r=o("SqueezeBertForMaskedLM"),T7r=o(" (SqueezeBERT model)"),F7r=l(),GE=a("li"),lLe=a("strong"),M7r=o("switch_transformers"),E7r=o(" \u2014 "),Fte=a("a"),C7r=o("SwitchTransformersForConditionalGeneration"),w7r=o(" (SwitchTransformers model)"),A7r=l(),qE=a("li"),iLe=a("strong"),L7r=o("t5"),y7r=o(" \u2014 "),Mte=a("a"),x7r=o("T5ForConditionalGeneration"),k7r=o(" (T5 model)"),$7r=l(),jE=a("li"),dLe=a("strong"),S7r=o("tapas"),R7r=o(" \u2014 "),Ete=a("a"),P7r=o("TapasForMaskedLM"),I7r=o(" (TAPAS model)"),B7r=l(),OE=a("li"),mLe=a("strong"),N7r=o("transfo-xl"),D7r=o(" \u2014 "),Cte=a("a"),G7r=o("TransfoXLLMHeadModel"),q7r=o(" (Transformer-XL model)"),j7r=l(),VE=a("li"),cLe=a("strong"),O7r=o("unispeech"),V7r=o(" \u2014 "),wte=a("a"),X7r=o("UniSpeechForPreTraining"),z7r=o(" (UniSpeech model)"),W7r=l(),XE=a("li"),fLe=a("strong"),Q7r=o("unispeech-sat"),U7r=o(" \u2014 "),Ate=a("a"),H7r=o("UniSpeechSatForPreTraining"),J7r=o(" (UniSpeechSat model)"),Y7r=l(),zE=a("li"),gLe=a("strong"),Z7r=o("videomae"),K7r=o(" \u2014 "),Lte=a("a"),eLr=o("VideoMAEForPreTraining"),oLr=o(" (VideoMAE model)"),rLr=l(),WE=a("li"),hLe=a("strong"),tLr=o("visual_bert"),aLr=o(" \u2014 "),yte=a("a"),nLr=o("VisualBertForPreTraining"),sLr=o(" (VisualBERT model)"),lLr=l(),QE=a("li"),uLe=a("strong"),iLr=o("vit_mae"),dLr=o(" \u2014 "),xte=a("a"),mLr=o("ViTMAEForPreTraining"),cLr=o(" (ViTMAE model)"),fLr=l(),UE=a("li"),pLe=a("strong"),gLr=o("wav2vec2"),hLr=o(" \u2014 "),kte=a("a"),uLr=o("Wav2Vec2ForPreTraining"),pLr=o(" (Wav2Vec2 model)"),_Lr=l(),HE=a("li"),_Le=a("strong"),bLr=o("wav2vec2-conformer"),vLr=o(" \u2014 "),$te=a("a"),TLr=o("Wav2Vec2ConformerForPreTraining"),FLr=o(" (Wav2Vec2-Conformer model)"),MLr=l(),JE=a("li"),bLe=a("strong"),ELr=o("xlm"),CLr=o(" \u2014 "),Ste=a("a"),wLr=o("XLMWithLMHeadModel"),ALr=o(" (XLM model)"),LLr=l(),YE=a("li"),vLe=a("strong"),yLr=o("xlm-roberta"),xLr=o(" \u2014 "),Rte=a("a"),kLr=o("XLMRobertaForMaskedLM"),$Lr=o(" (XLM-RoBERTa model)"),SLr=l(),ZE=a("li"),TLe=a("strong"),RLr=o("xlm-roberta-xl"),PLr=o(" \u2014 "),Pte=a("a"),ILr=o("XLMRobertaXLForMaskedLM"),BLr=o(" (XLM-RoBERTa-XL model)"),NLr=l(),KE=a("li"),FLe=a("strong"),DLr=o("xlnet"),GLr=o(" \u2014 "),Ite=a("a"),qLr=o("XLNetLMHeadModel"),jLr=o(" (XLNet model)"),OLr=l(),e3=a("p"),VLr=o("The model is set in evaluation mode by default using "),MLe=a("code"),XLr=o("model.eval()"),zLr=o(` (so for instance, dropout modules are
deactivated). To train the model, you should first set it back in training mode with `),ELe=a("code"),WLr=o("model.train()"),QLr=l(),v(o3.$$.fragment),Evo=l(),pm=a("h3"),r3=a("a"),CLe=a("span"),v(VI.$$.fragment),ULr=l(),wLe=a("span"),HLr=o("TFAutoModelForPreTraining"),Cvo=l(),Wo=a("div"),v(XI.$$.fragment),JLr=l(),_m=a("p"),YLr=o(`This is a generic model class that will be instantiated as one of the model classes of the library (with a pretraining head) when created
with the `),Bte=a("a"),ZLr=o("from_pretrained()"),KLr=o(" class method or the "),Nte=a("a"),e9r=o("from_config()"),o9r=o(` class
method.`),r9r=l(),zI=a("p"),t9r=o("This class cannot be instantiated directly using "),ALe=a("code"),a9r=o("__init__()"),n9r=o(" (throws an error)."),s9r=l(),Rt=a("div"),v(WI.$$.fragment),l9r=l(),LLe=a("p"),i9r=o("Instantiates one of the model classes of the library (with a pretraining head) from a configuration."),d9r=l(),bm=a("p"),m9r=o(`Note:
Loading a model from its configuration file does `),yLe=a("strong"),c9r=o("not"),f9r=o(` load the model weights. It only affects the
model\u2019s configuration. Use `),Dte=a("a"),g9r=o("from_pretrained()"),h9r=o(" to load the model weights."),u9r=l(),v(t3.$$.fragment),p9r=l(),Jr=a("div"),v(QI.$$.fragment),_9r=l(),xLe=a("p"),b9r=o("Instantiate one of the model classes of the library (with a pretraining head) from a pretrained model."),v9r=l(),Mn=a("p"),T9r=o("The model class to instantiate is selected based on the "),kLe=a("code"),F9r=o("model_type"),M9r=o(` property of the config object (either
passed as an argument or loaded from `),$Le=a("code"),E9r=o("pretrained_model_name_or_path"),C9r=o(` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),SLe=a("code"),w9r=o("pretrained_model_name_or_path"),A9r=o(":"),L9r=l(),me=a("ul"),a3=a("li"),RLe=a("strong"),y9r=o("albert"),x9r=o(" \u2014 "),Gte=a("a"),k9r=o("TFAlbertForPreTraining"),$9r=o(" (ALBERT model)"),S9r=l(),n3=a("li"),PLe=a("strong"),R9r=o("bart"),P9r=o(" \u2014 "),qte=a("a"),I9r=o("TFBartForConditionalGeneration"),B9r=o(" (BART model)"),N9r=l(),s3=a("li"),ILe=a("strong"),D9r=o("bert"),G9r=o(" \u2014 "),jte=a("a"),q9r=o("TFBertForPreTraining"),j9r=o(" (BERT model)"),O9r=l(),l3=a("li"),BLe=a("strong"),V9r=o("camembert"),X9r=o(" \u2014 "),Ote=a("a"),z9r=o("TFCamembertForMaskedLM"),W9r=o(" (CamemBERT model)"),Q9r=l(),i3=a("li"),NLe=a("strong"),U9r=o("ctrl"),H9r=o(" \u2014 "),Vte=a("a"),J9r=o("TFCTRLLMHeadModel"),Y9r=o(" (CTRL model)"),Z9r=l(),d3=a("li"),DLe=a("strong"),K9r=o("distilbert"),eyr=o(" \u2014 "),Xte=a("a"),oyr=o("TFDistilBertForMaskedLM"),ryr=o(" (DistilBERT model)"),tyr=l(),m3=a("li"),GLe=a("strong"),ayr=o("electra"),nyr=o(" \u2014 "),zte=a("a"),syr=o("TFElectraForPreTraining"),lyr=o(" (ELECTRA model)"),iyr=l(),c3=a("li"),qLe=a("strong"),dyr=o("flaubert"),myr=o(" \u2014 "),Wte=a("a"),cyr=o("TFFlaubertWithLMHeadModel"),fyr=o(" (FlauBERT model)"),gyr=l(),f3=a("li"),jLe=a("strong"),hyr=o("funnel"),uyr=o(" \u2014 "),Qte=a("a"),pyr=o("TFFunnelForPreTraining"),_yr=o(" (Funnel Transformer model)"),byr=l(),g3=a("li"),OLe=a("strong"),vyr=o("gpt-sw3"),Tyr=o(" \u2014 "),Ute=a("a"),Fyr=o("TFGPT2LMHeadModel"),Myr=o(" (GPT-Sw3 model)"),Eyr=l(),h3=a("li"),VLe=a("strong"),Cyr=o("gpt2"),wyr=o(" \u2014 "),Hte=a("a"),Ayr=o("TFGPT2LMHeadModel"),Lyr=o(" (OpenAI GPT-2 model)"),yyr=l(),u3=a("li"),XLe=a("strong"),xyr=o("layoutlm"),kyr=o(" \u2014 "),Jte=a("a"),$yr=o("TFLayoutLMForMaskedLM"),Syr=o(" (LayoutLM model)"),Ryr=l(),p3=a("li"),zLe=a("strong"),Pyr=o("lxmert"),Iyr=o(" \u2014 "),Yte=a("a"),Byr=o("TFLxmertForPreTraining"),Nyr=o(" (LXMERT model)"),Dyr=l(),_3=a("li"),WLe=a("strong"),Gyr=o("mobilebert"),qyr=o(" \u2014 "),Zte=a("a"),jyr=o("TFMobileBertForPreTraining"),Oyr=o(" (MobileBERT model)"),Vyr=l(),b3=a("li"),QLe=a("strong"),Xyr=o("mpnet"),zyr=o(" \u2014 "),Kte=a("a"),Wyr=o("TFMPNetForMaskedLM"),Qyr=o(" (MPNet model)"),Uyr=l(),v3=a("li"),ULe=a("strong"),Hyr=o("openai-gpt"),Jyr=o(" \u2014 "),eae=a("a"),Yyr=o("TFOpenAIGPTLMHeadModel"),Zyr=o(" (OpenAI GPT model)"),Kyr=l(),T3=a("li"),HLe=a("strong"),exr=o("roberta"),oxr=o(" \u2014 "),oae=a("a"),rxr=o("TFRobertaForMaskedLM"),txr=o(" (RoBERTa model)"),axr=l(),F3=a("li"),JLe=a("strong"),nxr=o("roberta-prelayernorm"),sxr=o(" \u2014 "),rae=a("a"),lxr=o("TFRobertaPreLayerNormForMaskedLM"),ixr=o(" (RoBERTa-PreLayerNorm model)"),dxr=l(),M3=a("li"),YLe=a("strong"),mxr=o("t5"),cxr=o(" \u2014 "),tae=a("a"),fxr=o("TFT5ForConditionalGeneration"),gxr=o(" (T5 model)"),hxr=l(),E3=a("li"),ZLe=a("strong"),uxr=o("tapas"),pxr=o(" \u2014 "),aae=a("a"),_xr=o("TFTapasForMaskedLM"),bxr=o(" (TAPAS model)"),vxr=l(),C3=a("li"),KLe=a("strong"),Txr=o("transfo-xl"),Fxr=o(" \u2014 "),nae=a("a"),Mxr=o("TFTransfoXLLMHeadModel"),Exr=o(" (Transformer-XL model)"),Cxr=l(),w3=a("li"),e9e=a("strong"),wxr=o("vit_mae"),Axr=o(" \u2014 "),sae=a("a"),Lxr=o("TFViTMAEForPreTraining"),yxr=o(" (ViTMAE model)"),xxr=l(),A3=a("li"),o9e=a("strong"),kxr=o("xlm"),$xr=o(" \u2014 "),lae=a("a"),Sxr=o("TFXLMWithLMHeadModel"),Rxr=o(" (XLM model)"),Pxr=l(),L3=a("li"),r9e=a("strong"),Ixr=o("xlm-roberta"),Bxr=o(" \u2014 "),iae=a("a"),Nxr=o("TFXLMRobertaForMaskedLM"),Dxr=o(" (XLM-RoBERTa model)"),Gxr=l(),y3=a("li"),t9e=a("strong"),qxr=o("xlnet"),jxr=o(" \u2014 "),dae=a("a"),Oxr=o("TFXLNetLMHeadModel"),Vxr=o(" (XLNet model)"),Xxr=l(),v(x3.$$.fragment),wvo=l(),vm=a("h3"),k3=a("a"),a9e=a("span"),v(UI.$$.fragment),zxr=l(),n9e=a("span"),Wxr=o("FlaxAutoModelForPreTraining"),Avo=l(),Qo=a("div"),v(HI.$$.fragment),Qxr=l(),Tm=a("p"),Uxr=o(`This is a generic model class that will be instantiated as one of the model classes of the library (with a pretraining head) when created
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method.`),Z$r=l(),rB=a("p"),K$r=o("This class cannot be instantiated directly using "),$9e=a("code"),eSr=o("__init__()"),oSr=o(" (throws an error)."),rSr=l(),It=a("div"),v(tB.$$.fragment),tSr=l(),S9e=a("p"),aSr=o("Instantiates one of the model classes of the library (with a causal language modeling head) from a configuration."),nSr=l(),wm=a("p"),sSr=o(`Note:
Loading a model from its configuration file does `),R9e=a("strong"),lSr=o("not"),iSr=o(` load the model weights. It only affects the
model\u2019s configuration. Use `),kae=a("a"),dSr=o("from_pretrained()"),mSr=o(" to load the model weights."),cSr=l(),v(H3.$$.fragment),fSr=l(),io=a("div"),v(aB.$$.fragment),gSr=l(),P9e=a("p"),hSr=o("Instantiate one of the model classes of the library (with a causal language modeling head) from a pretrained model."),uSr=l(),Cn=a("p"),pSr=o("The model class to instantiate is selected based on the "),I9e=a("code"),_Sr=o("model_type"),bSr=o(` property of the config object (either
passed as an argument or loaded from `),B9e=a("code"),vSr=o("pretrained_model_name_or_path"),TSr=o(` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),N9e=a("code"),FSr=o("pretrained_model_name_or_path"),MSr=o(":"),ESr=l(),H=a("ul"),J3=a("li"),D9e=a("strong"),CSr=o("bart"),wSr=o(" \u2014 "),$ae=a("a"),ASr=o("BartForCausalLM"),LSr=o(" (BART model)"),ySr=l(),Y3=a("li"),G9e=a("strong"),xSr=o("bert"),kSr=o(" \u2014 "),Sae=a("a"),$Sr=o("BertLMHeadModel"),SSr=o(" (BERT model)"),RSr=l(),Z3=a("li"),q9e=a("strong"),PSr=o("bert-generation"),ISr=o(" \u2014 "),Rae=a("a"),BSr=o("BertGenerationDecoder"),NSr=o(" (Bert Generation model)"),DSr=l(),K3=a("li"),j9e=a("strong"),GSr=o("big_bird"),qSr=o(" \u2014 "),Pae=a("a"),jSr=o("BigBirdForCausalLM"),OSr=o(" (BigBird model)"),VSr=l(),e4=a("li"),O9e=a("strong"),XSr=o("bigbird_pegasus"),zSr=o(" \u2014 "),Iae=a("a"),WSr=o("BigBirdPegasusForCausalLM"),QSr=o(" (BigBird-Pegasus model)"),USr=l(),o4=a("li"),V9e=a("strong"),HSr=o("biogpt"),JSr=o(" \u2014 "),Bae=a("a"),YSr=o("BioGptForCausalLM"),ZSr=o(" (BioGpt 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model\u2019s configuration. Use `),Tie=a("a"),Cet=o("from_pretrained()"),wet=o(" to load the model weights."),Aet=l(),v(V0.$$.fragment),Let=l(),tt=a("div"),v(XB.$$.fragment),yet=l(),ASe=a("p"),xet=o("Instantiate one of the model classes of the library (with a sequence-to-sequence language modeling head) from a pretrained model."),ket=l(),Sn=a("p"),$et=o("The model class to instantiate is selected based on the "),LSe=a("code"),Set=o("model_type"),Ret=o(` property of the config object (either
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falling back to using pattern matching on `),xSe=a("code"),Bet=o("pretrained_model_name_or_path"),Net=o(":"),Det=l(),Ie=a("ul"),X0=a("li"),kSe=a("strong"),Get=o("bart"),qet=o(" \u2014 "),Fie=a("a"),jet=o("FlaxBartForConditionalGeneration"),Oet=o(" (BART model)"),Vet=l(),z0=a("li"),$Se=a("strong"),Xet=o("blenderbot"),zet=o(" \u2014 "),Mie=a("a"),Wet=o("FlaxBlenderbotForConditionalGeneration"),Qet=o(" (Blenderbot model)"),Uet=l(),W0=a("li"),SSe=a("strong"),Het=o("blenderbot-small"),Jet=o(" \u2014 "),Eie=a("a"),Yet=o("FlaxBlenderbotSmallForConditionalGeneration"),Zet=o(" (BlenderbotSmall model)"),Ket=l(),Q0=a("li"),RSe=a("strong"),eot=o("encoder-decoder"),oot=o(" \u2014 "),Cie=a("a"),rot=o("FlaxEncoderDecoderModel"),tot=o(" (Encoder decoder model)"),aot=l(),U0=a("li"),PSe=a("strong"),not=o("longt5"),sot=o(" \u2014 "),wie=a("a"),lot=o("FlaxLongT5ForConditionalGeneration"),iot=o(" (LongT5 model)"),dot=l(),H0=a("li"),ISe=a("strong"),mot=o("marian"),cot=o(" \u2014 "),Aie=a("a"),fot=o("FlaxMarianMTModel"),got=o(" (Marian model)"),hot=l(),J0=a("li"),BSe=a("strong"),uot=o("mbart"),pot=o(" \u2014 "),Lie=a("a"),_ot=o("FlaxMBartForConditionalGeneration"),bot=o(" (mBART model)"),vot=l(),Y0=a("li"),NSe=a("strong"),Tot=o("mt5"),Fot=o(" \u2014 "),yie=a("a"),Mot=o("FlaxMT5ForConditionalGeneration"),Eot=o(" (MT5 model)"),Cot=l(),Z0=a("li"),DSe=a("strong"),wot=o("pegasus"),Aot=o(" \u2014 "),xie=a("a"),Lot=o("FlaxPegasusForConditionalGeneration"),yot=o(" (Pegasus model)"),xot=l(),K0=a("li"),GSe=a("strong"),kot=o("t5"),$ot=o(" \u2014 "),kie=a("a"),Sot=o("FlaxT5ForConditionalGeneration"),Rot=o(" (T5 model)"),Pot=l(),v(ew.$$.fragment),Qvo=l(),Jm=a("h3"),ow=a("a"),qSe=a("span"),v(zB.$$.fragment),Iot=l(),jSe=a("span"),Bot=o("AutoModelForSequenceClassification"),Uvo=l(),tr=a("div"),v(WB.$$.fragment),Not=l(),Ym=a("p"),Dot=o(`This is a generic model class that will be instantiated as one of the model classes of the library (with a sequence classification head) when created
with the `),$ie=a("a"),Got=o("from_pretrained()"),qot=o(" class method or the "),Sie=a("a"),jot=o("from_config()"),Oot=o(` class
method.`),Vot=l(),QB=a("p"),Xot=o("This class cannot be instantiated directly using "),OSe=a("code"),zot=o("__init__()"),Wot=o(" (throws an error)."),Qot=l(),Xt=a("div"),v(UB.$$.fragment),Uot=l(),VSe=a("p"),Hot=o("Instantiates one of the model classes of the library (with a sequence classification head) from a configuration."),Jot=l(),Zm=a("p"),Yot=o(`Note:
Loading a model from its configuration file does `),XSe=a("strong"),Zot=o("not"),Kot=o(` load the model weights. It only affects the
model\u2019s configuration. Use `),Rie=a("a"),ert=o("from_pretrained()"),ort=o(" to load the model weights."),rrt=l(),v(rw.$$.fragment),trt=l(),fo=a("div"),v(HB.$$.fragment),art=l(),zSe=a("p"),nrt=o("Instantiate one of the model classes of the library (with a sequence classification head) from a pretrained model."),srt=l(),Rn=a("p"),lrt=o("The model class to instantiate is selected based on the "),WSe=a("code"),irt=o("model_type"),drt=o(` property of the config object (either
passed as an argument or loaded from `),QSe=a("code"),mrt=o("pretrained_model_name_or_path"),crt=o(` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),USe=a("code"),frt=o("pretrained_model_name_or_path"),grt=o(":"),hrt=l(),I=a("ul"),tw=a("li"),HSe=a("strong"),urt=o("albert"),prt=o(" \u2014 "),Pie=a("a"),_rt=o("AlbertForSequenceClassification"),brt=o(" (ALBERT model)"),vrt=l(),aw=a("li"),JSe=a("strong"),Trt=o("bart"),Frt=o(" \u2014 "),Iie=a("a"),Mrt=o("BartForSequenceClassification"),Ert=o(" (BART model)"),Crt=l(),nw=a("li"),YSe=a("strong"),wrt=o("bert"),Art=o(" \u2014 "),Bie=a("a"),Lrt=o("BertForSequenceClassification"),yrt=o(" (BERT model)"),xrt=l(),sw=a("li"),ZSe=a("strong"),krt=o("big_bird"),$rt=o(" \u2014 "),Nie=a("a"),Srt=o("BigBirdForSequenceClassification"),Rrt=o(" (BigBird model)"),Prt=l(),lw=a("li"),KSe=a("strong"),Irt=o("bigbird_pegasus"),Brt=o(" \u2014 "),Die=a("a"),Nrt=o("BigBirdPegasusForSequenceClassification"),Drt=o(" (BigBird-Pegasus model)"),Grt=l(),iw=a("li"),eRe=a("strong"),qrt=o("bloom"),jrt=o(" \u2014 "),Gie=a("a"),Ort=o("BloomForSequenceClassification"),Vrt=o(" (BLOOM model)"),Xrt=l(),dw=a("li"),oRe=a("strong"),zrt=o("camembert"),Wrt=o(" \u2014 "),qie=a("a"),Qrt=o("CamembertForSequenceClassification"),Urt=o(" (CamemBERT model)"),Hrt=l(),mw=a("li"),rRe=a("strong"),Jrt=o("canine"),Yrt=o(" \u2014 "),jie=a("a"),Zrt=o("CanineForSequenceClassification"),Krt=o(" (CANINE model)"),ett=l(),cw=a("li"),tRe=a("strong"),ott=o("convbert"),rtt=o(" \u2014 "),Oie=a("a"),ttt=o("ConvBertForSequenceClassification"),att=o(" (ConvBERT model)"),ntt=l(),fw=a("li"),aRe=a("strong"),stt=o("ctrl"),ltt=o(" \u2014 "),Vie=a("a"),itt=o("CTRLForSequenceClassification"),dtt=o(" (CTRL model)"),mtt=l(),gw=a("li"),nRe=a("strong"),ctt=o("data2vec-text"),ftt=o(" \u2014 "),Xie=a("a"),gtt=o("Data2VecTextForSequenceClassification"),htt=o(" (Data2VecText model)"),utt=l(),hw=a("li"),sRe=a("strong"),ptt=o("deberta"),_tt=o(" \u2014 "),zie=a("a"),btt=o("DebertaForSequenceClassification"),vtt=o(" (DeBERTa model)"),Ttt=l(),uw=a("li"),lRe=a("strong"),Ftt=o("deberta-v2"),Mtt=o(" \u2014 "),Wie=a("a"),Ett=o("DebertaV2ForSequenceClassification"),Ctt=o(" (DeBERTa-v2 model)"),wtt=l(),pw=a("li"),iRe=a("strong"),Att=o("distilbert"),Ltt=o(" \u2014 "),Qie=a("a"),ytt=o("DistilBertForSequenceClassification"),xtt=o(" (DistilBERT model)"),ktt=l(),_w=a("li"),dRe=a("strong"),$tt=o("electra"),Stt=o(" \u2014 "),Uie=a("a"),Rtt=o("ElectraForSequenceClassification"),Ptt=o(" (ELECTRA model)"),Itt=l(),bw=a("li"),mRe=a("strong"),Btt=o("ernie"),Ntt=o(" \u2014 "),Hie=a("a"),Dtt=o("ErnieForSequenceClassification"),Gtt=o(" (ERNIE model)"),qtt=l(),vw=a("li"),cRe=a("strong"),jtt=o("esm"),Ott=o(" \u2014 "),Jie=a("a"),Vtt=o("EsmForSequenceClassification"),Xtt=o(" (ESM model)"),ztt=l(),Tw=a("li"),fRe=a("strong"),Wtt=o("flaubert"),Qtt=o(" \u2014 "),Yie=a("a"),Utt=o("FlaubertForSequenceClassification"),Htt=o(" (FlauBERT model)"),Jtt=l(),Fw=a("li"),gRe=a("strong"),Ytt=o("fnet"),Ztt=o(" \u2014 "),Zie=a("a"),Ktt=o("FNetForSequenceClassification"),eat=o(" (FNet model)"),oat=l(),Mw=a("li"),hRe=a("strong"),rat=o("funnel"),tat=o(" \u2014 "),Kie=a("a"),aat=o("FunnelForSequenceClassification"),nat=o(" (Funnel Transformer model)"),sat=l(),Ew=a("li"),uRe=a("strong"),lat=o("gpt-sw3"),iat=o(" \u2014 "),ede=a("a"),dat=o("GPT2ForSequenceClassification"),mat=o(" (GPT-Sw3 model)"),cat=l(),Cw=a("li"),pRe=a("strong"),fat=o("gpt2"),gat=o(" \u2014 "),ode=a("a"),hat=o("GPT2ForSequenceClassification"),uat=o(" (OpenAI GPT-2 model)"),pat=l(),ww=a("li"),_Re=a("strong"),_at=o("gpt_neo"),bat=o(" \u2014 "),rde=a("a"),vat=o("GPTNeoForSequenceClassification"),Tat=o(" (GPT Neo model)"),Fat=l(),Aw=a("li"),bRe=a("strong"),Mat=o("gptj"),Eat=o(" \u2014 "),tde=a("a"),Cat=o("GPTJForSequenceClassification"),wat=o(" (GPT-J model)"),Aat=l(),Lw=a("li"),vRe=a("strong"),Lat=o("ibert"),yat=o(" \u2014 "),ade=a("a"),xat=o("IBertForSequenceClassification"),kat=o(" (I-BERT model)"),$at=l(),yw=a("li"),TRe=a("strong"),Sat=o("layoutlm"),Rat=o(" \u2014 "),nde=a("a"),Pat=o("LayoutLMForSequenceClassification"),Iat=o(" (LayoutLM model)"),Bat=l(),xw=a("li"),FRe=a("strong"),Nat=o("layoutlmv2"),Dat=o(" \u2014 "),sde=a("a"),Gat=o("LayoutLMv2ForSequenceClassification"),qat=o(" (LayoutLMv2 model)"),jat=l(),kw=a("li"),MRe=a("strong"),Oat=o("layoutlmv3"),Vat=o(" \u2014 "),lde=a("a"),Xat=o("LayoutLMv3ForSequenceClassification"),zat=o(" (LayoutLMv3 model)"),Wat=l(),$w=a("li"),ERe=a("strong"),Qat=o("led"),Uat=o(" \u2014 "),ide=a("a"),Hat=o("LEDForSequenceClassification"),Jat=o(" (LED model)"),Yat=l(),Sw=a("li"),CRe=a("strong"),Zat=o("lilt"),Kat=o(" \u2014 "),dde=a("a"),ent=o("LiltForSequenceClassification"),ont=o(" (LiLT model)"),rnt=l(),Rw=a("li"),wRe=a("strong"),tnt=o("longformer"),ant=o(" \u2014 "),mde=a("a"),nnt=o("LongformerForSequenceClassification"),snt=o(" (Longformer model)"),lnt=l(),Pw=a("li"),ARe=a("strong"),int=o("luke"),dnt=o(" \u2014 "),cde=a("a"),mnt=o("LukeForSequenceClassification"),cnt=o(" (LUKE model)"),fnt=l(),Iw=a("li"),LRe=a("strong"),gnt=o("markuplm"),hnt=o(" \u2014 "),fde=a("a"),unt=o("MarkupLMForSequenceClassification"),pnt=o(" (MarkupLM model)"),_nt=l(),Bw=a("li"),yRe=a("strong"),bnt=o("mbart"),vnt=o(" \u2014 "),gde=a("a"),Tnt=o("MBartForSequenceClassification"),Fnt=o(" (mBART model)"),Mnt=l(),Nw=a("li"),xRe=a("strong"),Ent=o("megatron-bert"),Cnt=o(" \u2014 "),hde=a("a"),wnt=o("MegatronBertForSequenceClassification"),Ant=o(" (Megatron-BERT model)"),Lnt=l(),Dw=a("li"),kRe=a("strong"),ynt=o("mobilebert"),xnt=o(" \u2014 "),ude=a("a"),knt=o("MobileBertForSequenceClassification"),$nt=o(" (MobileBERT model)"),Snt=l(),Gw=a("li"),$Re=a("strong"),Rnt=o("mpnet"),Pnt=o(" \u2014 "),pde=a("a"),Int=o("MPNetForSequenceClassification"),Bnt=o(" (MPNet model)"),Nnt=l(),qw=a("li"),SRe=a("strong"),Dnt=o("mvp"),Gnt=o(" \u2014 "),_de=a("a"),qnt=o("MvpForSequenceClassification"),jnt=o(" (MVP model)"),Ont=l(),jw=a("li"),RRe=a("strong"),Vnt=o("nezha"),Xnt=o(" \u2014 "),bde=a("a"),znt=o("NezhaForSequenceClassification"),Wnt=o(" (Nezha model)"),Qnt=l(),Ow=a("li"),PRe=a("strong"),Unt=o("nystromformer"),Hnt=o(" \u2014 "),vde=a("a"),Jnt=o("NystromformerForSequenceClassification"),Ynt=o(" (Nystr\xF6mformer model)"),Znt=l(),Vw=a("li"),IRe=a("strong"),Knt=o("openai-gpt"),est=o(" \u2014 "),Tde=a("a"),ost=o("OpenAIGPTForSequenceClassification"),rst=o(" (OpenAI GPT model)"),tst=l(),Xw=a("li"),BRe=a("strong"),ast=o("opt"),nst=o(" \u2014 "),Fde=a("a"),sst=o("OPTForSequenceClassification"),lst=o(" (OPT model)"),ist=l(),zw=a("li"),NRe=a("strong"),dst=o("perceiver"),mst=o(" \u2014 "),Mde=a("a"),cst=o("PerceiverForSequenceClassification"),fst=o(" (Perceiver model)"),gst=l(),Ww=a("li"),DRe=a("strong"),hst=o("plbart"),ust=o(" \u2014 "),Ede=a("a"),pst=o("PLBartForSequenceClassification"),_st=o(" (PLBart model)"),bst=l(),Qw=a("li"),GRe=a("strong"),vst=o("qdqbert"),Tst=o(" \u2014 "),Cde=a("a"),Fst=o("QDQBertForSequenceClassification"),Mst=o(" (QDQBert model)"),Est=l(),Uw=a("li"),qRe=a("strong"),Cst=o("reformer"),wst=o(" \u2014 "),wde=a("a"),Ast=o("ReformerForSequenceClassification"),Lst=o(" (Reformer model)"),yst=l(),Hw=a("li"),jRe=a("strong"),xst=o("rembert"),kst=o(" \u2014 "),Ade=a("a"),$st=o("RemBertForSequenceClassification"),Sst=o(" (RemBERT model)"),Rst=l(),Jw=a("li"),ORe=a("strong"),Pst=o("roberta"),Ist=o(" \u2014 "),Lde=a("a"),Bst=o("RobertaForSequenceClassification"),Nst=o(" (RoBERTa model)"),Dst=l(),Yw=a("li"),VRe=a("strong"),Gst=o("roberta-prelayernorm"),qst=o(" \u2014 "),yde=a("a"),jst=o("RobertaPreLayerNormForSequenceClassification"),Ost=o(" (RoBERTa-PreLayerNorm model)"),Vst=l(),Zw=a("li"),XRe=a("strong"),Xst=o("roc_bert"),zst=o(" \u2014 "),xde=a("a"),Wst=o("RoCBertForSequenceClassification"),Qst=o(" (RoCBert model)"),Ust=l(),Kw=a("li"),zRe=a("strong"),Hst=o("roformer"),Jst=o(" \u2014 "),kde=a("a"),Yst=o("RoFormerForSequenceClassification"),Zst=o(" (RoFormer model)"),Kst=l(),e6=a("li"),WRe=a("strong"),elt=o("squeezebert"),olt=o(" \u2014 "),$de=a("a"),rlt=o("SqueezeBertForSequenceClassification"),tlt=o(" (SqueezeBERT model)"),alt=l(),o6=a("li"),QRe=a("strong"),nlt=o("tapas"),slt=o(" \u2014 "),Sde=a("a"),llt=o("TapasForSequenceClassification"),ilt=o(" (TAPAS model)"),dlt=l(),r6=a("li"),URe=a("strong"),mlt=o("transfo-xl"),clt=o(" \u2014 "),Rde=a("a"),flt=o("TransfoXLForSequenceClassification"),glt=o(" (Transformer-XL model)"),hlt=l(),t6=a("li"),HRe=a("strong"),ult=o("xlm"),plt=o(" \u2014 "),Pde=a("a"),_lt=o("XLMForSequenceClassification"),blt=o(" (XLM model)"),vlt=l(),a6=a("li"),JRe=a("strong"),Tlt=o("xlm-roberta"),Flt=o(" \u2014 "),Ide=a("a"),Mlt=o("XLMRobertaForSequenceClassification"),Elt=o(" (XLM-RoBERTa model)"),Clt=l(),n6=a("li"),YRe=a("strong"),wlt=o("xlm-roberta-xl"),Alt=o(" \u2014 "),Bde=a("a"),Llt=o("XLMRobertaXLForSequenceClassification"),ylt=o(" (XLM-RoBERTa-XL model)"),xlt=l(),s6=a("li"),ZRe=a("strong"),klt=o("xlnet"),$lt=o(" \u2014 "),Nde=a("a"),Slt=o("XLNetForSequenceClassification"),Rlt=o(" (XLNet model)"),Plt=l(),l6=a("li"),KRe=a("strong"),Ilt=o("yoso"),Blt=o(" \u2014 "),Dde=a("a"),Nlt=o("YosoForSequenceClassification"),Dlt=o(" (YOSO model)"),Glt=l(),i6=a("p"),qlt=o("The model is set in evaluation mode by default using "),ePe=a("code"),jlt=o("model.eval()"),Olt=o(` (so for instance, dropout modules are
deactivated). To train the model, you should first set it back in training mode with `),oPe=a("code"),Vlt=o("model.train()"),Xlt=l(),v(d6.$$.fragment),Hvo=l(),Km=a("h3"),m6=a("a"),rPe=a("span"),v(JB.$$.fragment),zlt=l(),tPe=a("span"),Wlt=o("TFAutoModelForSequenceClassification"),Jvo=l(),ar=a("div"),v(YB.$$.fragment),Qlt=l(),ec=a("p"),Ult=o(`This is a generic model class that will be instantiated as one of the model classes of the library (with a sequence classification head) when created
with the `),Gde=a("a"),Hlt=o("from_pretrained()"),Jlt=o(" class method or the "),qde=a("a"),Ylt=o("from_config()"),Zlt=o(` class
method.`),Klt=l(),ZB=a("p"),eit=o("This class cannot be instantiated directly using "),aPe=a("code"),oit=o("__init__()"),rit=o(" (throws an error)."),tit=l(),zt=a("div"),v(KB.$$.fragment),ait=l(),nPe=a("p"),nit=o("Instantiates one of the model classes of the library (with a sequence classification head) from a configuration."),sit=l(),oc=a("p"),lit=o(`Note:
Loading a model from its configuration file does `),sPe=a("strong"),iit=o("not"),dit=o(` load the model weights. It only affects the
model\u2019s configuration. Use `),jde=a("a"),mit=o("from_pretrained()"),cit=o(" to load the model weights."),fit=l(),v(c6.$$.fragment),git=l(),at=a("div"),v(eN.$$.fragment),hit=l(),lPe=a("p"),uit=o("Instantiate one of the model classes of the library (with a sequence classification head) from a pretrained model."),pit=l(),Pn=a("p"),_it=o("The model class to instantiate is selected based on the "),iPe=a("code"),bit=o("model_type"),vit=o(` property of the config object (either
passed as an argument or loaded from `),dPe=a("code"),Tit=o("pretrained_model_name_or_path"),Fit=o(` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),mPe=a("code"),Mit=o("pretrained_model_name_or_path"),Eit=o(":"),Cit=l(),ae=a("ul"),f6=a("li"),cPe=a("strong"),wit=o("albert"),Ait=o(" \u2014 "),Ode=a("a"),Lit=o("TFAlbertForSequenceClassification"),yit=o(" (ALBERT model)"),xit=l(),g6=a("li"),fPe=a("strong"),kit=o("bart"),$it=o(" \u2014 "),Vde=a("a"),Sit=o("TFBartForSequenceClassification"),Rit=o(" (BART model)"),Pit=l(),h6=a("li"),gPe=a("strong"),Iit=o("bert"),Bit=o(" \u2014 "),Xde=a("a"),Nit=o("TFBertForSequenceClassification"),Dit=o(" (BERT model)"),Git=l(),u6=a("li"),hPe=a("strong"),qit=o("camembert"),jit=o(" \u2014 "),zde=a("a"),Oit=o("TFCamembertForSequenceClassification"),Vit=o(" (CamemBERT model)"),Xit=l(),p6=a("li"),uPe=a("strong"),zit=o("convbert"),Wit=o(" \u2014 "),Wde=a("a"),Qit=o("TFConvBertForSequenceClassification"),Uit=o(" (ConvBERT model)"),Hit=l(),_6=a("li"),pPe=a("strong"),Jit=o("ctrl"),Yit=o(" \u2014 "),Qde=a("a"),Zit=o("TFCTRLForSequenceClassification"),Kit=o(" (CTRL 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falling back to using pattern matching on `),qVe=a("code"),Tjt=o("pretrained_model_name_or_path"),Fjt=o(":"),Mjt=l(),wr=a("ul"),ux=a("li"),jVe=a("strong"),Ejt=o("conditional_detr"),Cjt=o(" \u2014 "),Upe=a("a"),wjt=o("ConditionalDetrForObjectDetection"),Ajt=o(" (Conditional DETR model)"),Ljt=l(),px=a("li"),OVe=a("strong"),yjt=o("deformable_detr"),xjt=o(" \u2014 "),Hpe=a("a"),kjt=o("DeformableDetrForObjectDetection"),$jt=o(" (Deformable DETR model)"),Sjt=l(),_x=a("li"),VVe=a("strong"),Rjt=o("deta"),Pjt=o(" \u2014 "),Jpe=a("a"),Ijt=o("DetaForObjectDetection"),Bjt=o(" (DETA model)"),Njt=l(),bx=a("li"),XVe=a("strong"),Djt=o("detr"),Gjt=o(" \u2014 "),Ype=a("a"),qjt=o("DetrForObjectDetection"),jjt=o(" (DETR model)"),Ojt=l(),vx=a("li"),zVe=a("strong"),Vjt=o("table-transformer"),Xjt=o(" \u2014 "),Zpe=a("a"),zjt=o("TableTransformerForObjectDetection"),Wjt=o(" (Table Transformer model)"),Qjt=l(),Tx=a("li"),WVe=a("strong"),Ujt=o("yolos"),Hjt=o(" \u2014 "),Kpe=a("a"),Jjt=o("YolosForObjectDetection"),Yjt=o(" (YOLOS model)"),Zjt=l(),Fx=a("p"),Kjt=o("The model is set in evaluation mode by default using "),QVe=a("code"),eOt=o("model.eval()"),oOt=o(` (so for instance, dropout modules are
deactivated). To train the model, you should first set it back in training mode with `),UVe=a("code"),rOt=o("model.train()"),tOt=l(),v(Mx.$$.fragment),GTo=l(),mf=a("h3"),Ex=a("a"),HVe=a("span"),v(ZD.$$.fragment),aOt=l(),JVe=a("span"),nOt=o("AutoModelForImageSegmentation"),qTo=l(),Ar=a("div"),v(KD.$$.fragment),sOt=l(),cf=a("p"),lOt=o(`This is a generic model class that will be instantiated as one of the model classes of the library (with a image segmentation head) when created
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method.`),fOt=l(),eG=a("p"),gOt=o("This class cannot be instantiated directly using "),YVe=a("code"),hOt=o("__init__()"),uOt=o(" (throws an error)."),pOt=l(),ga=a("div"),v(oG.$$.fragment),_Ot=l(),ZVe=a("p"),bOt=o("Instantiates one of the model classes of the library (with a image segmentation head) from a configuration."),vOt=l(),ff=a("p"),TOt=o(`Note:
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model\u2019s configuration. Use `),r_e=a("a"),EOt=o("from_pretrained()"),COt=o(" to load the model weights."),wOt=l(),v(Cx.$$.fragment),AOt=l(),Mo=a("div"),v(rG.$$.fragment),LOt=l(),eXe=a("p"),yOt=o("Instantiate one of the model classes of the library (with a image segmentation head) from a pretrained model."),xOt=l(),rs=a("p"),kOt=o("The model class to instantiate is selected based on the "),oXe=a("code"),$Ot=o("model_type"),SOt=o(` property of the config object (either
passed as an argument or loaded from `),rXe=a("code"),ROt=o("pretrained_model_name_or_path"),POt=o(` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),tXe=a("code"),IOt=o("pretrained_model_name_or_path"),BOt=o(":"),NOt=l(),aXe=a("ul"),wx=a("li"),nXe=a("strong"),DOt=o("detr"),GOt=o(" \u2014 "),t_e=a("a"),qOt=o("DetrForSegmentation"),jOt=o(" (DETR model)"),OOt=l(),Ax=a("p"),VOt=o("The model is set in evaluation mode by default using "),sXe=a("code"),XOt=o("model.eval()"),zOt=o(` (so for instance, dropout modules are
deactivated). To train the model, you should first set it back in training mode with `),lXe=a("code"),WOt=o("model.train()"),QOt=l(),v(Lx.$$.fragment),jTo=l(),gf=a("h3"),yx=a("a"),iXe=a("span"),v(tG.$$.fragment),UOt=l(),dXe=a("span"),HOt=o("AutoModelForSemanticSegmentation"),OTo=l(),Lr=a("div"),v(aG.$$.fragment),JOt=l(),hf=a("p"),YOt=o(`This is a generic model class that will be instantiated as one of the model classes of the library (with a semantic segmentation head) when created
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method.`),rVt=l(),nG=a("p"),tVt=o("This class cannot be instantiated directly using "),mXe=a("code"),aVt=o("__init__()"),nVt=o(" (throws an error)."),sVt=l(),ha=a("div"),v(sG.$$.fragment),lVt=l(),cXe=a("p"),iVt=o("Instantiates one of the model classes of the library (with a semantic segmentation head) from a configuration."),dVt=l(),uf=a("p"),mVt=o(`Note:
Loading a model from its configuration file does `),fXe=a("strong"),cVt=o("not"),fVt=o(` load the model weights. It only affects the
model\u2019s configuration. Use `),s_e=a("a"),gVt=o("from_pretrained()"),hVt=o(" to load the model weights."),uVt=l(),v(xx.$$.fragment),pVt=l(),Eo=a("div"),v(lG.$$.fragment),_Vt=l(),gXe=a("p"),bVt=o("Instantiate one of the model classes of the library (with a semantic segmentation head) from a pretrained model."),vVt=l(),ts=a("p"),TVt=o("The model class to instantiate is selected based on the "),hXe=a("code"),FVt=o("model_type"),MVt=o(` property of the config object (either
passed as an argument or loaded from `),uXe=a("code"),EVt=o("pretrained_model_name_or_path"),CVt=o(` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),pXe=a("code"),wVt=o("pretrained_model_name_or_path"),AVt=o(":"),LVt=l(),eo=a("ul"),kx=a("li"),_Xe=a("strong"),yVt=o("beit"),xVt=o(" \u2014 "),l_e=a("a"),kVt=o("BeitForSemanticSegmentation"),$Vt=o(" (BEiT model)"),SVt=l(),$x=a("li"),bXe=a("strong"),RVt=o("data2vec-vision"),PVt=o(" \u2014 "),i_e=a("a"),IVt=o("Data2VecVisionForSemanticSegmentation"),BVt=o(" (Data2VecVision model)"),NVt=l(),Sx=a("li"),vXe=a("strong"),DVt=o("dpt"),GVt=o(" \u2014 "),d_e=a("a"),qVt=o("DPTForSemanticSegmentation"),jVt=o(" (DPT model)"),OVt=l(),Rx=a("li"),TXe=a("strong"),VVt=o("mobilenet_v2"),XVt=o(" \u2014 "),m_e=a("a"),zVt=o("MobileNetV2ForSemanticSegmentation"),WVt=o(" (MobileNetV2 model)"),QVt=l(),Px=a("li"),FXe=a("strong"),UVt=o("mobilevit"),HVt=o(" \u2014 "),c_e=a("a"),JVt=o("MobileViTForSemanticSegmentation"),YVt=o(" (MobileViT model)"),ZVt=l(),Ix=a("li"),MXe=a("strong"),KVt=o("segformer"),eXt=o(" \u2014 "),f_e=a("a"),oXt=o("SegformerForSemanticSegmentation"),rXt=o(" (SegFormer model)"),tXt=l(),Bx=a("li"),EXe=a("strong"),aXt=o("upernet"),nXt=o(" \u2014 "),g_e=a("a"),sXt=o("UperNetForSemanticSegmentation"),lXt=o(" (UPerNet model)"),iXt=l(),Nx=a("p"),dXt=o("The model is set in evaluation mode by default using "),CXe=a("code"),mXt=o("model.eval()"),cXt=o(` (so for instance, dropout modules are
deactivated). To train the model, you should first set it back in training mode with `),wXe=a("code"),fXt=o("model.train()"),gXt=l(),v(Dx.$$.fragment),VTo=l(),pf=a("h3"),Gx=a("a"),AXe=a("span"),v(iG.$$.fragment),hXt=l(),LXe=a("span"),uXt=o("TFAutoModelForSemanticSegmentation"),XTo=l(),yr=a("div"),v(dG.$$.fragment),pXt=l(),_f=a("p"),_Xt=o(`This is a generic model class that will be instantiated as one of the model classes of the library (with a semantic segmentation head) when created
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method.`),MXt=l(),mG=a("p"),EXt=o("This class cannot be instantiated directly using "),yXe=a("code"),CXt=o("__init__()"),wXt=o(" (throws an error)."),AXt=l(),ua=a("div"),v(cG.$$.fragment),LXt=l(),xXe=a("p"),yXt=o("Instantiates one of the model classes of the library (with a semantic segmentation head) from a configuration."),xXt=l(),bf=a("p"),kXt=o(`Note:
Loading a model from its configuration file does `),kXe=a("strong"),$Xt=o("not"),SXt=o(` load the model weights. It only affects the
model\u2019s configuration. Use `),p_e=a("a"),RXt=o("from_pretrained()"),PXt=o(" to load the model weights."),IXt=l(),v(qx.$$.fragment),BXt=l(),pt=a("div"),v(fG.$$.fragment),NXt=l(),$Xe=a("p"),DXt=o("Instantiate one of the model classes of the library (with a semantic segmentation head) from a pretrained model."),GXt=l(),as=a("p"),qXt=o("The model class to instantiate is selected based on the "),SXe=a("code"),jXt=o("model_type"),OXt=o(` property of the config object (either
passed as an argument or loaded from `),RXe=a("code"),VXt=o("pretrained_model_name_or_path"),XXt=o(` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),PXe=a("code"),zXt=o("pretrained_model_name_or_path"),WXt=o(":"),QXt=l(),vf=a("ul"),jx=a("li"),IXe=a("strong"),UXt=o("data2vec-vision"),HXt=o(" \u2014 "),__e=a("a"),JXt=o("TFData2VecVisionForSemanticSegmentation"),YXt=o(" (Data2VecVision model)"),ZXt=l(),Ox=a("li"),BXe=a("strong"),KXt=o("mobilevit"),ezt=o(" \u2014 "),b_e=a("a"),ozt=o("TFMobileViTForSemanticSegmentation"),rzt=o(" (MobileViT model)"),tzt=l(),Vx=a("li"),NXe=a("strong"),azt=o("segformer"),nzt=o(" \u2014 "),v_e=a("a"),szt=o("TFSegformerForSemanticSegmentation"),lzt=o(" (SegFormer model)"),izt=l(),v(Xx.$$.fragment),zTo=l(),Tf=a("h3"),zx=a("a"),DXe=a("span"),v(gG.$$.fragment),dzt=l(),GXe=a("span"),mzt=o("AutoModelForInstanceSegmentation"),WTo=l(),xr=a("div"),v(hG.$$.fragment),czt=l(),Ff=a("p"),fzt=o(`This is a generic model class that will be instantiated as one of the model classes of the library (with a instance segmentation head) when created
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method.`),_zt=l(),uG=a("p"),bzt=o("This class cannot be instantiated directly using "),qXe=a("code"),vzt=o("__init__()"),Tzt=o(" (throws an error)."),Fzt=l(),pa=a("div"),v(pG.$$.fragment),Mzt=l(),jXe=a("p"),Ezt=o("Instantiates one of the model classes of the library (with a instance segmentation head) from a configuration."),Czt=l(),Mf=a("p"),wzt=o(`Note:
Loading a model from its configuration file does `),OXe=a("strong"),Azt=o("not"),Lzt=o(` load the model weights. It only affects the
model\u2019s configuration. Use `),M_e=a("a"),yzt=o("from_pretrained()"),xzt=o(" to load the model weights."),kzt=l(),v(Wx.$$.fragment),$zt=l(),Co=a("div"),v(_G.$$.fragment),Szt=l(),VXe=a("p"),Rzt=o("Instantiate one of the model classes of the library (with a instance segmentation head) from a pretrained model."),Pzt=l(),ns=a("p"),Izt=o("The model class to instantiate is selected based on the "),XXe=a("code"),Bzt=o("model_type"),Nzt=o(` property of the config object (either
passed as an argument or loaded from `),zXe=a("code"),Dzt=o("pretrained_model_name_or_path"),Gzt=o(` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),WXe=a("code"),qzt=o("pretrained_model_name_or_path"),jzt=o(":"),Ozt=l(),QXe=a("ul"),Qx=a("li"),UXe=a("strong"),Vzt=o("maskformer"),Xzt=o(" \u2014 "),E_e=a("a"),zzt=o("MaskFormerForInstanceSegmentation"),Wzt=o(" (MaskFormer model)"),Qzt=l(),Ux=a("p"),Uzt=o("The model is set in evaluation mode by default using "),HXe=a("code"),Hzt=o("model.eval()"),Jzt=o(` (so for instance, dropout modules are
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with the `),C_e=a("a"),tWt=o("from_pretrained()"),aWt=o(" class method or the "),w_e=a("a"),nWt=o("from_config()"),sWt=o(` class
method.`),lWt=l(),TG=a("p"),iWt=o("This class cannot be instantiated directly using "),KXe=a("code"),dWt=o("__init__()"),mWt=o(" (throws an error)."),cWt=l(),_a=a("div"),v(FG.$$.fragment),fWt=l(),eze=a("p"),gWt=o("Instantiates one of the model classes of the library (with a universal image segmentation head) from a configuration."),hWt=l(),wf=a("p"),uWt=o(`Note:
Loading a model from its configuration file does `),oze=a("strong"),pWt=o("not"),_Wt=o(` load the model weights. It only affects the
model\u2019s configuration. Use `),A_e=a("a"),bWt=o("from_pretrained()"),vWt=o(" to load the model weights."),TWt=l(),v(Yx.$$.fragment),FWt=l(),wo=a("div"),v(MG.$$.fragment),MWt=l(),rze=a("p"),EWt=o("Instantiate one of the model classes of the library (with a universal image segmentation head) from a pretrained model."),CWt=l(),ss=a("p"),wWt=o("The model class to instantiate is selected based on the "),tze=a("code"),AWt=o("model_type"),LWt=o(` property of the config object (either
passed as an argument or loaded from `),aze=a("code"),yWt=o("pretrained_model_name_or_path"),xWt=o(` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),nze=a("code"),kWt=o("pretrained_model_name_or_path"),$Wt=o(":"),SWt=l(),ls=a("ul"),Zx=a("li"),sze=a("strong"),RWt=o("detr"),PWt=o(" \u2014 "),L_e=a("a"),IWt=o("DetrForSegmentation"),BWt=o(" (DETR model)"),NWt=l(),Kx=a("li"),lze=a("strong"),DWt=o("mask2former"),GWt=o(" \u2014 "),y_e=a("a"),qWt=o("Mask2FormerForUniversalSegmentation"),jWt=o(" (Mask2Former model)"),OWt=l(),ek=a("li"),ize=a("strong"),VWt=o("maskformer"),XWt=o(" \u2014 "),x_e=a("a"),zWt=o("MaskFormerForInstanceSegmentation"),WWt=o(" (MaskFormer model)"),QWt=l(),ok=a("li"),dze=a("strong"),UWt=o("oneformer"),HWt=o(" \u2014 "),k_e=a("a"),JWt=o("OneFormerForUniversalSegmentation"),YWt=o(" (OneFormer model)"),ZWt=l(),rk=a("p"),KWt=o("The model is set in evaluation mode by default using "),mze=a("code"),eQt=o("model.eval()"),oQt=o(` (so for instance, dropout modules are
deactivated). To train the model, you should first set it back in training mode with `),cze=a("code"),rQt=o("model.train()"),tQt=l(),v(tk.$$.fragment),HTo=l(),Af=a("h3"),ak=a("a"),fze=a("span"),v(EG.$$.fragment),aQt=l(),gze=a("span"),nQt=o("AutoModelForZeroShotObjectDetection"),JTo=l(),$r=a("div"),v(CG.$$.fragment),sQt=l(),Lf=a("p"),lQt=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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method.`),fQt=l(),wG=a("p"),gQt=o("This class cannot be instantiated directly using "),hze=a("code"),hQt=o("__init__()"),uQt=o(" (throws an error)."),pQt=l(),ba=a("div"),v(AG.$$.fragment),_Qt=l(),uze=a("p"),bQt=o("Instantiates one of the model classes of the library (with a zero-shot object detection head) from a configuration."),vQt=l(),yf=a("p"),TQt=o(`Note:
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model\u2019s configuration. Use `),R_e=a("a"),EQt=o("from_pretrained()"),CQt=o(" to load the model weights."),wQt=l(),v(nk.$$.fragment),AQt=l(),Ao=a("div"),v(LG.$$.fragment),LQt=l(),_ze=a("p"),yQt=o("Instantiate one of the model classes of the library (with a zero-shot object detection head) from a pretrained model."),xQt=l(),is=a("p"),kQt=o("The model class to instantiate is selected based on the "),bze=a("code"),$Qt=o("model_type"),SQt=o(` property of the config object (either
passed as an argument or loaded from `),vze=a("code"),RQt=o("pretrained_model_name_or_path"),PQt=o(` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),Tze=a("code"),IQt=o("pretrained_model_name_or_path"),BQt=o(":"),NQt=l(),Fze=a("ul"),sk=a("li"),Mze=a("strong"),DQt=o("owlvit"),GQt=o(" \u2014 "),P_e=a("a"),qQt=o("OwlViTForObjectDetection"),jQt=o(" (OWL-ViT model)"),OQt=l(),lk=a("p"),VQt=o("The model is set in evaluation mode by default using "),Eze=a("code"),XQt=o("model.eval()"),zQt=o(` (so for instance, dropout modules are
deactivated). To train the model, you should first set it back in training mode with `),Cze=a("code"),WQt=o("model.train()"),QQt=l(),v(ik.$$.fragment),YTo=l(),xf=a("h2"),dk=a("a"),wze=a("span"),v(yG.$$.fragment),UQt=l(),Aze=a("span"),HQt=o("Audio"),ZTo=l(),I_e=a("p"),JQt=o("The following auto classes are available for the following audio tasks."),KTo=l(),kf=a("h3"),mk=a("a"),Lze=a("span"),v(xG.$$.fragment),YQt=l(),yze=a("span"),ZQt=o("AutoModelForAudioClassification"),eFo=l(),Sr=a("div"),v(kG.$$.fragment),KQt=l(),$f=a("p"),eUt=o(`This is a generic model class that will be instantiated as one of the model classes of the library (with a audio classification head) when created
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method.`),nUt=l(),$G=a("p"),sUt=o("This class cannot be instantiated directly using "),xze=a("code"),lUt=o("__init__()"),iUt=o(" (throws an error)."),dUt=l(),va=a("div"),v(SG.$$.fragment),mUt=l(),kze=a("p"),cUt=o("Instantiates one of the model classes of the library (with a audio classification head) from a configuration."),fUt=l(),Sf=a("p"),gUt=o(`Note:
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model\u2019s configuration. Use `),D_e=a("a"),pUt=o("from_pretrained()"),_Ut=o(" to load the model weights."),bUt=l(),v(ck.$$.fragment),vUt=l(),Lo=a("div"),v(RG.$$.fragment),TUt=l(),Sze=a("p"),FUt=o("Instantiate one of the model classes of the library (with a audio classification head) from a pretrained model."),MUt=l(),ds=a("p"),EUt=o("The model class to instantiate is selected based on the "),Rze=a("code"),CUt=o("model_type"),wUt=o(` property of the config object (either
passed as an argument or loaded from `),Pze=a("code"),AUt=o("pretrained_model_name_or_path"),LUt=o(` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),Ize=a("code"),yUt=o("pretrained_model_name_or_path"),xUt=o(":"),kUt=l(),Ne=a("ul"),fk=a("li"),Bze=a("strong"),$Ut=o("audio-spectrogram-transformer"),SUt=o(" \u2014 "),G_e=a("a"),RUt=o("ASTForAudioClassification"),PUt=o(" (Audio Spectrogram Transformer model)"),IUt=l(),gk=a("li"),Nze=a("strong"),BUt=o("data2vec-audio"),NUt=o(" \u2014 "),q_e=a("a"),DUt=o("Data2VecAudioForSequenceClassification"),GUt=o(" (Data2VecAudio model)"),qUt=l(),hk=a("li"),Dze=a("strong"),jUt=o("hubert"),OUt=o(" \u2014 "),j_e=a("a"),VUt=o("HubertForSequenceClassification"),XUt=o(" (Hubert model)"),zUt=l(),uk=a("li"),Gze=a("strong"),WUt=o("sew"),QUt=o(" \u2014 "),O_e=a("a"),UUt=o("SEWForSequenceClassification"),HUt=o(" (SEW model)"),JUt=l(),pk=a("li"),qze=a("strong"),YUt=o("sew-d"),ZUt=o(" \u2014 "),V_e=a("a"),KUt=o("SEWDForSequenceClassification"),eHt=o(" (SEW-D model)"),oHt=l(),_k=a("li"),jze=a("strong"),rHt=o("unispeech"),tHt=o(" \u2014 "),X_e=a("a"),aHt=o("UniSpeechForSequenceClassification"),nHt=o(" (UniSpeech model)"),sHt=l(),bk=a("li"),Oze=a("strong"),lHt=o("unispeech-sat"),iHt=o(" \u2014 "),z_e=a("a"),dHt=o("UniSpeechSatForSequenceClassification"),mHt=o(" (UniSpeechSat model)"),cHt=l(),vk=a("li"),Vze=a("strong"),fHt=o("wav2vec2"),gHt=o(" \u2014 "),W_e=a("a"),hHt=o("Wav2Vec2ForSequenceClassification"),uHt=o(" (Wav2Vec2 model)"),pHt=l(),Tk=a("li"),Xze=a("strong"),_Ht=o("wav2vec2-conformer"),bHt=o(" \u2014 "),Q_e=a("a"),vHt=o("Wav2Vec2ConformerForSequenceClassification"),THt=o(" (Wav2Vec2-Conformer model)"),FHt=l(),Fk=a("li"),zze=a("strong"),MHt=o("wavlm"),EHt=o(" \u2014 "),U_e=a("a"),CHt=o("WavLMForSequenceClassification"),wHt=o(" (WavLM model)"),AHt=l(),Mk=a("p"),LHt=o("The model is set in evaluation mode by default using "),Wze=a("code"),yHt=o("model.eval()"),xHt=o(` (so for instance, dropout modules are
deactivated). To train the model, you should first set it back in training mode with `),Qze=a("code"),kHt=o("model.train()"),$Ht=l(),v(Ek.$$.fragment),oFo=l(),Rf=a("h3"),Ck=a("a"),Uze=a("span"),v(PG.$$.fragment),SHt=l(),Hze=a("span"),RHt=o("AutoModelForAudioFrameClassification"),rFo=l(),Rr=a("div"),v(IG.$$.fragment),PHt=l(),Pf=a("p"),IHt=o(`This is a generic model class that will be instantiated as one of the model classes of the library (with a audio frame (token) classification head) when created
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method.`),qHt=l(),BG=a("p"),jHt=o("This class cannot be instantiated directly using "),Jze=a("code"),OHt=o("__init__()"),VHt=o(" (throws an error)."),XHt=l(),Ta=a("div"),v(NG.$$.fragment),zHt=l(),Yze=a("p"),WHt=o("Instantiates one of the model classes of the library (with a audio frame (token) classification head) from a configuration."),QHt=l(),If=a("p"),UHt=o(`Note:
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passed as an argument or loaded from `),oWe=a("code"),lJt=o("pretrained_model_name_or_path"),iJt=o(` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),rWe=a("code"),dJt=o("pretrained_model_name_or_path"),mJt=o(":"),cJt=l(),Ct=a("ul"),Ak=a("li"),tWe=a("strong"),fJt=o("data2vec-audio"),gJt=o(" \u2014 "),Z_e=a("a"),hJt=o("Data2VecAudioForAudioFrameClassification"),uJt=o(" (Data2VecAudio model)"),pJt=l(),Lk=a("li"),aWe=a("strong"),_Jt=o("unispeech-sat"),bJt=o(" \u2014 "),K_e=a("a"),vJt=o("UniSpeechSatForAudioFrameClassification"),TJt=o(" (UniSpeechSat model)"),FJt=l(),yk=a("li"),nWe=a("strong"),MJt=o("wav2vec2"),EJt=o(" \u2014 "),e1e=a("a"),CJt=o("Wav2Vec2ForAudioFrameClassification"),wJt=o(" (Wav2Vec2 model)"),AJt=l(),xk=a("li"),sWe=a("strong"),LJt=o("wav2vec2-conformer"),yJt=o(" \u2014 "),o1e=a("a"),xJt=o("Wav2Vec2ConformerForAudioFrameClassification"),kJt=o(" (Wav2Vec2-Conformer model)"),$Jt=l(),kk=a("li"),lWe=a("strong"),SJt=o("wavlm"),RJt=o(" \u2014 "),r1e=a("a"),PJt=o("WavLMForAudioFrameClassification"),IJt=o(" (WavLM model)"),BJt=l(),$k=a("p"),NJt=o("The model is set in evaluation mode by default using "),iWe=a("code"),DJt=o("model.eval()"),GJt=o(` (so for instance, dropout modules are
deactivated). To train the model, you should first set it back in training mode with `),dWe=a("code"),qJt=o("model.train()"),jJt=l(),v(Sk.$$.fragment),tFo=l(),Bf=a("h3"),Rk=a("a"),mWe=a("span"),v(GG.$$.fragment),OJt=l(),cWe=a("span"),VJt=o("AutoModelForCTC"),aFo=l(),Pr=a("div"),v(qG.$$.fragment),XJt=l(),Nf=a("p"),zJt=o(`This is a generic model class that will be instantiated as one of the model classes of the library (with a connectionist temporal classification head) when created
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method.`),JJt=l(),jG=a("p"),YJt=o("This class cannot be instantiated directly using "),fWe=a("code"),ZJt=o("__init__()"),KJt=o(" (throws an error)."),eYt=l(),Fa=a("div"),v(OG.$$.fragment),oYt=l(),gWe=a("p"),rYt=o("Instantiates one of the model classes of the library (with a connectionist temporal classification head) from a configuration."),tYt=l(),Df=a("p"),aYt=o(`Note:
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model\u2019s configuration. Use `),n1e=a("a"),lYt=o("from_pretrained()"),iYt=o(" to load the model weights."),dYt=l(),v(Pk.$$.fragment),mYt=l(),xo=a("div"),v(VG.$$.fragment),cYt=l(),uWe=a("p"),fYt=o("Instantiate one of the model classes of the library (with a connectionist temporal classification head) from a pretrained model."),gYt=l(),cs=a("p"),hYt=o("The model class to instantiate is selected based on the "),pWe=a("code"),uYt=o("model_type"),pYt=o(` property of the config object (either
passed as an argument or loaded from `),_We=a("code"),_Yt=o("pretrained_model_name_or_path"),bYt=o(` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),bWe=a("code"),vYt=o("pretrained_model_name_or_path"),TYt=o(":"),FYt=l(),De=a("ul"),Ik=a("li"),vWe=a("strong"),MYt=o("data2vec-audio"),EYt=o(" \u2014 "),s1e=a("a"),CYt=o("Data2VecAudioForCTC"),wYt=o(" (Data2VecAudio model)"),AYt=l(),Bk=a("li"),TWe=a("strong"),LYt=o("hubert"),yYt=o(" \u2014 "),l1e=a("a"),xYt=o("HubertForCTC"),kYt=o(" (Hubert model)"),$Yt=l(),Nk=a("li"),FWe=a("strong"),SYt=o("mctct"),RYt=o(" \u2014 "),i1e=a("a"),PYt=o("MCTCTForCTC"),IYt=o(" (M-CTC-T model)"),BYt=l(),Dk=a("li"),MWe=a("strong"),NYt=o("sew"),DYt=o(" \u2014 "),d1e=a("a"),GYt=o("SEWForCTC"),qYt=o(" (SEW model)"),jYt=l(),Gk=a("li"),EWe=a("strong"),OYt=o("sew-d"),VYt=o(" \u2014 "),m1e=a("a"),XYt=o("SEWDForCTC"),zYt=o(" (SEW-D model)"),WYt=l(),qk=a("li"),CWe=a("strong"),QYt=o("unispeech"),UYt=o(" \u2014 "),c1e=a("a"),HYt=o("UniSpeechForCTC"),JYt=o(" (UniSpeech model)"),YYt=l(),jk=a("li"),wWe=a("strong"),ZYt=o("unispeech-sat"),KYt=o(" \u2014 "),f1e=a("a"),eZt=o("UniSpeechSatForCTC"),oZt=o(" (UniSpeechSat model)"),rZt=l(),Ok=a("li"),AWe=a("strong"),tZt=o("wav2vec2"),aZt=o(" \u2014 "),g1e=a("a"),nZt=o("Wav2Vec2ForCTC"),sZt=o(" (Wav2Vec2 model)"),lZt=l(),Vk=a("li"),LWe=a("strong"),iZt=o("wav2vec2-conformer"),dZt=o(" \u2014 "),h1e=a("a"),mZt=o("Wav2Vec2ConformerForCTC"),cZt=o(" (Wav2Vec2-Conformer model)"),fZt=l(),Xk=a("li"),yWe=a("strong"),gZt=o("wavlm"),hZt=o(" \u2014 "),u1e=a("a"),uZt=o("WavLMForCTC"),pZt=o(" (WavLM model)"),_Zt=l(),zk=a("p"),bZt=o("The model is set in evaluation mode by default using "),xWe=a("code"),vZt=o("model.eval()"),TZt=o(` (so for instance, dropout modules are
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passed as an argument or loaded from `),DWe=a("code"),ZZt=o("pretrained_model_name_or_path"),KZt=o(` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),GWe=a("code"),eKt=o("pretrained_model_name_or_path"),oKt=o(":"),rKt=l(),Of=a("ul"),Hk=a("li"),qWe=a("strong"),tKt=o("speech-encoder-decoder"),aKt=o(" \u2014 "),v1e=a("a"),nKt=o("SpeechEncoderDecoderModel"),sKt=o(" (Speech Encoder decoder model)"),lKt=l(),Jk=a("li"),jWe=a("strong"),iKt=o("speech_to_text"),dKt=o(" \u2014 "),T1e=a("a"),mKt=o("Speech2TextForConditionalGeneration"),cKt=o(" (Speech2Text model)"),fKt=l(),Yk=a("li"),OWe=a("strong"),gKt=o("whisper"),hKt=o(" \u2014 "),F1e=a("a"),uKt=o("WhisperForConditionalGeneration"),pKt=o(" (Whisper model)"),_Kt=l(),Zk=a("p"),bKt=o("The model is set in evaluation mode by default using "),VWe=a("code"),vKt=o("model.eval()"),TKt=o(` (so for instance, dropout modules are
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method.`),$Kt=l(),YG=a("p"),SKt=o("This class cannot be instantiated directly using "),QWe=a("code"),RKt=o("__init__()"),PKt=o(" (throws an error)."),IKt=l(),Ea=a("div"),v(ZG.$$.fragment),BKt=l(),UWe=a("p"),NKt=o("Instantiates one of the model classes of the library (with a sequence-to-sequence speech-to-text modeling head) from a configuration."),DKt=l(),zf=a("p"),GKt=o(`Note:
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passed as an argument or loaded from `),ZWe=a("code"),ZKt=o("pretrained_model_name_or_path"),KKt=o(` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),KWe=a("code"),eea=o("pretrained_model_name_or_path"),oea=o(":"),rea=l(),eq=a("ul"),r$=a("li"),eQe=a("strong"),tea=o("speech_to_text"),aea=o(" \u2014 "),w1e=a("a"),nea=o("TFSpeech2TextForConditionalGeneration"),sea=o(" (Speech2Text model)"),lea=l(),t$=a("li"),oQe=a("strong"),iea=o("whisper"),dea=o(" \u2014 "),A1e=a("a"),mea=o("TFWhisperForConditionalGeneration"),cea=o(" (Whisper model)"),fea=l(),v(a$.$$.fragment),dFo=l(),Wf=a("h3"),n$=a("a"),rQe=a("span"),v(oq.$$.fragment),gea=l(),tQe=a("span"),hea=o("AutoModelForAudioXVector"),mFo=l(),Nr=a("div"),v(rq.$$.fragment),uea=l(),Qf=a("p"),pea=o(`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
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method.`),Fea=l(),tq=a("p"),Mea=o("This class cannot be instantiated directly using "),aQe=a("code"),Eea=o("__init__()"),Cea=o(" (throws an error)."),wea=l(),Ca=a("div"),v(aq.$$.fragment),Aea=l(),nQe=a("p"),Lea=o("Instantiates one of the model classes of the library (with a audio retrieval via x-vector head) from a configuration."),yea=l(),Uf=a("p"),xea=o(`Note:
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passed as an argument or loaded from `),dQe=a("code"),Oea=o("pretrained_model_name_or_path"),Vea=o(` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),mQe=a("code"),Xea=o("pretrained_model_name_or_path"),zea=o(":"),Wea=l(),wt=a("ul"),l$=a("li"),cQe=a("strong"),Qea=o("data2vec-audio"),Uea=o(" \u2014 "),k1e=a("a"),Hea=o("Data2VecAudioForXVector"),Jea=o(" (Data2VecAudio model)"),Yea=l(),i$=a("li"),fQe=a("strong"),Zea=o("unispeech-sat"),Kea=o(" \u2014 "),$1e=a("a"),eoa=o("UniSpeechSatForXVector"),ooa=o(" (UniSpeechSat model)"),roa=l(),d$=a("li"),gQe=a("strong"),toa=o("wav2vec2"),aoa=o(" \u2014 "),S1e=a("a"),noa=o("Wav2Vec2ForXVector"),soa=o(" (Wav2Vec2 model)"),loa=l(),m$=a("li"),hQe=a("strong"),ioa=o("wav2vec2-conformer"),doa=o(" \u2014 "),R1e=a("a"),moa=o("Wav2Vec2ConformerForXVector"),coa=o(" (Wav2Vec2-Conformer model)"),foa=l(),c$=a("li"),uQe=a("strong"),goa=o("wavlm"),hoa=o(" \u2014 "),P1e=a("a"),uoa=o("WavLMForXVector"),poa=o(" (WavLM model)"),_oa=l(),f$=a("p"),boa=o("The model is set in evaluation mode by default using "),pQe=a("code"),voa=o("model.eval()"),Toa=o(` (so for instance, dropout modules are
deactivated). To train the model, you should first set it back in training mode with `),_Qe=a("code"),Foa=o("model.train()"),Moa=l(),v(g$.$$.fragment),cFo=l(),Hf=a("h2"),h$=a("a"),bQe=a("span"),v(sq.$$.fragment),Eoa=l(),vQe=a("span"),Coa=o("Multimodal"),fFo=l(),I1e=a("p"),woa=o("The following auto classes are available for the following multimodal tasks."),gFo=l(),Jf=a("h3"),u$=a("a"),TQe=a("span"),v(lq.$$.fragment),Aoa=l(),FQe=a("span"),Loa=o("AutoModelForTableQuestionAnswering"),hFo=l(),Dr=a("div"),v(iq.$$.fragment),yoa=l(),Yf=a("p"),xoa=o(`This is a generic model class that will be instantiated as one of the model classes of the library (with a table question answering head) when created
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passed as an argument or loaded from `),LQe=a("code"),ora=o("pretrained_model_name_or_path"),rra=o(` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),yQe=a("code"),tra=o("pretrained_model_name_or_path"),ara=o(":"),nra=l(),xQe=a("ul"),_$=a("li"),kQe=a("strong"),sra=o("tapas"),lra=o(" \u2014 "),G1e=a("a"),ira=o("TapasForQuestionAnswering"),dra=o(" (TAPAS model)"),mra=l(),b$=a("p"),cra=o("The model is set in evaluation mode by default using "),$Qe=a("code"),fra=o("model.eval()"),gra=o(` (so for instance, dropout modules are
deactivated). To train the model, you should first set it back in training mode with `),SQe=a("code"),hra=o("model.train()"),ura=l(),v(v$.$$.fragment),uFo=l(),Kf=a("h3"),T$=a("a"),RQe=a("span"),v(fq.$$.fragment),pra=l(),PQe=a("span"),_ra=o("TFAutoModelForTableQuestionAnswering"),pFo=l(),Gr=a("div"),v(gq.$$.fragment),bra=l(),eg=a("p"),vra=o(`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 `),q1e=a("a"),Tra=o("from_pretrained()"),Fra=o(" class method or the "),j1e=a("a"),Mra=o("from_config()"),Era=o(` class
method.`),Cra=l(),hq=a("p"),wra=o("This class cannot be instantiated directly using "),IQe=a("code"),Ara=o("__init__()"),Lra=o(" (throws an error)."),yra=l(),Aa=a("div"),v(uq.$$.fragment),xra=l(),BQe=a("p"),kra=o("Instantiates one of the model classes of the library (with a table question answering head) from a configuration."),$ra=l(),og=a("p"),Sra=o(`Note:
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classes"),xMa.forEach(t),QFo.forEach(t),fvo=i(c),UY=n(c,"P",{});var kMa=s(UY);xsr=r(kMa,"The following auto classes are available for instantiating a base model class without a specific head."),kMa.forEach(t),gvo=i(c),tm=n(c,"H3",{class:!0});var UFo=s(tm);pb=n(UFo,"A",{id:!0,class:!0,href:!0});var $Ma=s(pb);i0e=n($Ma,"SPAN",{});var SMa=s(i0e);T(MI.$$.fragment,SMa),SMa.forEach(t),$Ma.forEach(t),ksr=i(UFo),d0e=n(UFo,"SPAN",{});var RMa=s(d0e);$sr=r(RMa,"AutoModel"),RMa.forEach(t),UFo.forEach(t),hvo=i(c),Oo=n(c,"DIV",{class:!0});var ii=s(Oo);T(EI.$$.fragment,ii),Ssr=i(ii),am=n(ii,"P",{});var _2e=s(am);Rsr=r(_2e,`This is a generic model class that will be instantiated as one of the base model classes of the library when created
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method.`),_2e.forEach(t),Dsr=i(ii),CI=n(ii,"P",{});var HFo=s(CI);Gsr=r(HFo,"This class cannot be instantiated directly using "),m0e=n(HFo,"CODE",{});var BMa=s(m0e);qsr=r(BMa,"__init__()"),BMa.forEach(t),jsr=r(HFo," (throws an error)."),HFo.forEach(t),Osr=i(ii),xt=n(ii,"DIV",{class:!0});var $S=s(xt);T(wI.$$.fragment,$S),Vsr=i($S),c0e=n($S,"P",{});var NMa=s(c0e);Xsr=r(NMa,"Instantiates one of the base model classes of the library from a configuration."),NMa.forEach(t),zsr=i($S),nm=n($S,"P",{});var b2e=s(nm);Wsr=r(b2e,`Note:
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uAa=s(xre);Q0r=r(uAa,"FlaxVisionTextDualEncoderModel"),uAa.forEach(t),U0r=r(Jso," (VisionTextDualEncoder model)"),Jso.forEach(t),H0r=i(de),UM=n(de,"LI",{});var Yso=s(UM);d7e=n(Yso,"STRONG",{});var pAa=s(d7e);J0r=r(pAa,"vit"),pAa.forEach(t),Y0r=r(Yso," \u2014 "),kre=n(Yso,"A",{href:!0});var _Aa=s(kre);Z0r=r(_Aa,"FlaxViTModel"),_Aa.forEach(t),K0r=r(Yso," (ViT model)"),Yso.forEach(t),ewr=i(de),HM=n(de,"LI",{});var Zso=s(HM);m7e=n(Zso,"STRONG",{});var bAa=s(m7e);owr=r(bAa,"wav2vec2"),bAa.forEach(t),rwr=r(Zso," \u2014 "),$re=n(Zso,"A",{href:!0});var vAa=s($re);twr=r(vAa,"FlaxWav2Vec2Model"),vAa.forEach(t),awr=r(Zso," (Wav2Vec2 model)"),Zso.forEach(t),nwr=i(de),JM=n(de,"LI",{});var Kso=s(JM);c7e=n(Kso,"STRONG",{});var TAa=s(c7e);swr=r(TAa,"xglm"),TAa.forEach(t),lwr=r(Kso," \u2014 "),Sre=n(Kso,"A",{href:!0});var FAa=s(Sre);iwr=r(FAa,"FlaxXGLMModel"),FAa.forEach(t),dwr=r(Kso," (XGLM model)"),Kso.forEach(t),mwr=i(de),YM=n(de,"LI",{});var elo=s(YM);f7e=n(elo,"STRONG",{});var MAa=s(f7e);cwr=r(MAa,"xlm-roberta"),MAa.forEach(t),fwr=r(elo," \u2014 "),Rre=n(elo,"A",{href:!0});var EAa=s(Rre);gwr=r(EAa,"FlaxXLMRobertaModel"),EAa.forEach(t),hwr=r(elo," (XLM-RoBERTa model)"),elo.forEach(t),de.forEach(t),uwr=i(fi),T(ZM.$$.fragment,fi),fi.forEach(t),ci.forEach(t),vvo=i(c),fm=n(c,"H2",{class:!0});var eMo=s(fm);KM=n(eMo,"A",{id:!0,class:!0,href:!0});var CAa=s(KM);g7e=n(CAa,"SPAN",{});var wAa=s(g7e);T(NI.$$.fragment,wAa),wAa.forEach(t),CAa.forEach(t),pwr=i(eMo),h7e=n(eMo,"SPAN",{});var AAa=s(h7e);_wr=r(AAa,"Generic pretraining classes"),AAa.forEach(t),eMo.forEach(t),Tvo=i(c),Pre=n(c,"P",{});var LAa=s(Pre);bwr=r(LAa,"The following auto classes are available for instantiating a model with a pretraining head."),LAa.forEach(t),Fvo=i(c),gm=n(c,"H3",{class:!0});var oMo=s(gm);eE=n(oMo,"A",{id:!0,class:!0,href:!0});var yAa=s(eE);u7e=n(yAa,"SPAN",{});var xAa=s(u7e);T(DI.$$.fragment,xAa),xAa.forEach(t),yAa.forEach(t),vwr=i(oMo),p7e=n(oMo,"SPAN",{});var kAa=s(p7e);Twr=r(kAa,"AutoModelForPreTraining"),kAa.forEach(t),oMo.forEach(t),Mvo=i(c),zo=n(c,"DIV",{class:!0});var gi=s(zo);T(GI.$$.fragment,gi),Fwr=i(gi),hm=n(gi,"P",{});var E2e=s(hm);Mwr=r(E2e,`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 `),Ire=n(E2e,"A",{href:!0});var $Aa=s(Ire);Ewr=r($Aa,"from_pretrained()"),$Aa.forEach(t),Cwr=r(E2e," class method or the "),Bre=n(E2e,"A",{href:!0});var SAa=s(Bre);wwr=r(SAa,"from_config()"),SAa.forEach(t),Awr=r(E2e,` class
method.`),E2e.forEach(t),Lwr=i(gi),qI=n(gi,"P",{});var rMo=s(qI);ywr=r(rMo,"This class cannot be instantiated directly using "),_7e=n(rMo,"CODE",{});var RAa=s(_7e);xwr=r(RAa,"__init__()"),RAa.forEach(t),kwr=r(rMo," (throws an error)."),rMo.forEach(t),$wr=i(gi),St=n(gi,"DIV",{class:!0});var NS=s(St);T(jI.$$.fragment,NS),Swr=i(NS),b7e=n(NS,"P",{});var PAa=s(b7e);Rwr=r(PAa,"Instantiates one of the model classes of the library (with a pretraining head) from a configuration."),PAa.forEach(t),Pwr=i(NS),um=n(NS,"P",{});var C2e=s(um);Iwr=r(C2e,`Note:
Loading a model from its configuration file does `),v7e=n(C2e,"STRONG",{});var IAa=s(v7e);Bwr=r(IAa,"not"),IAa.forEach(t),Nwr=r(C2e,` load the model weights. It only affects the
model\u2019s configuration. Use `),Nre=n(C2e,"A",{href:!0});var BAa=s(Nre);Dwr=r(BAa,"from_pretrained()"),BAa.forEach(t),Gwr=r(C2e," to load the model weights."),C2e.forEach(t),qwr=i(NS),T(oE.$$.fragment,NS),NS.forEach(t),jwr=i(gi),lo=n(gi,"DIV",{class:!0});var Na=s(lo);T(OI.$$.fragment,Na),Owr=i(Na),T7e=n(Na,"P",{});var NAa=s(T7e);Vwr=r(NAa,"Instantiate one of the model classes of the library (with a pretraining head) from a pretrained model."),NAa.forEach(t),Xwr=i(Na),Fn=n(Na,"P",{});var DS=s(Fn);zwr=r(DS,"The model class to instantiate is selected based on the "),F7e=n(DS,"CODE",{});var DAa=s(F7e);Wwr=r(DAa,"model_type"),DAa.forEach(t),Qwr=r(DS,` property of the config object (either
passed as an argument or loaded from `),M7e=n(DS,"CODE",{});var GAa=s(M7e);Uwr=r(GAa,"pretrained_model_name_or_path"),GAa.forEach(t),Hwr=r(DS,` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),E7e=n(DS,"CODE",{});var qAa=s(E7e);Jwr=r(qAa,"pretrained_model_name_or_path"),qAa.forEach(t),Ywr=r(DS,":"),DS.forEach(t),Zwr=i(Na),z=n(Na,"UL",{});var W=s(z);rE=n(W,"LI",{});var olo=s(rE);C7e=n(olo,"STRONG",{});var jAa=s(C7e);Kwr=r(jAa,"albert"),jAa.forEach(t),e6r=r(olo," \u2014 "),Dre=n(olo,"A",{href:!0});var OAa=s(Dre);o6r=r(OAa,"AlbertForPreTraining"),OAa.forEach(t),r6r=r(olo," (ALBERT model)"),olo.forEach(t),t6r=i(W),tE=n(W,"LI",{});var rlo=s(tE);w7e=n(rlo,"STRONG",{});var VAa=s(w7e);a6r=r(VAa,"bart"),VAa.forEach(t),n6r=r(rlo," \u2014 "),Gre=n(rlo,"A",{href:!0});var XAa=s(Gre);s6r=r(XAa,"BartForConditionalGeneration"),XAa.forEach(t),l6r=r(rlo," (BART model)"),rlo.forEach(t),i6r=i(W),aE=n(W,"LI",{});var tlo=s(aE);A7e=n(tlo,"STRONG",{});var zAa=s(A7e);d6r=r(zAa,"bert"),zAa.forEach(t),m6r=r(tlo," \u2014 "),qre=n(tlo,"A",{href:!0});var WAa=s(qre);c6r=r(WAa,"BertForPreTraining"),WAa.forEach(t),f6r=r(tlo," (BERT 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with the `),Bte=n(w2e,"A",{href:!0});var OLa=s(Bte);ZLr=r(OLa,"from_pretrained()"),OLa.forEach(t),KLr=r(w2e," class method or the "),Nte=n(w2e,"A",{href:!0});var VLa=s(Nte);e9r=r(VLa,"from_config()"),VLa.forEach(t),o9r=r(w2e,` class
method.`),w2e.forEach(t),r9r=i(hi),zI=n(hi,"P",{});var aMo=s(zI);t9r=r(aMo,"This class cannot be instantiated directly using "),ALe=n(aMo,"CODE",{});var XLa=s(ALe);a9r=r(XLa,"__init__()"),XLa.forEach(t),n9r=r(aMo," (throws an error)."),aMo.forEach(t),s9r=i(hi),Rt=n(hi,"DIV",{class:!0});var GS=s(Rt);T(WI.$$.fragment,GS),l9r=i(GS),LLe=n(GS,"P",{});var zLa=s(LLe);i9r=r(zLa,"Instantiates one of the model classes of the library (with a pretraining head) from a configuration."),zLa.forEach(t),d9r=i(GS),bm=n(GS,"P",{});var A2e=s(bm);m9r=r(A2e,`Note:
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model\u2019s configuration. Use `),Dte=n(A2e,"A",{href:!0});var QLa=s(Dte);g9r=r(QLa,"from_pretrained()"),QLa.forEach(t),h9r=r(A2e," to load the model weights."),A2e.forEach(t),u9r=i(GS),T(t3.$$.fragment,GS),GS.forEach(t),p9r=i(hi),Jr=n(hi,"DIV",{class:!0});var ui=s(Jr);T(QI.$$.fragment,ui),_9r=i(ui),xLe=n(ui,"P",{});var ULa=s(xLe);b9r=r(ULa,"Instantiate one of the model classes of the library (with a pretraining head) from a pretrained model."),ULa.forEach(t),v9r=i(ui),Mn=n(ui,"P",{});var qS=s(Mn);T9r=r(qS,"The model class to instantiate is selected based on the "),kLe=n(qS,"CODE",{});var HLa=s(kLe);F9r=r(HLa,"model_type"),HLa.forEach(t),M9r=r(qS,` property of the config object (either
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falling back to using pattern matching on `),SLe=n(qS,"CODE",{});var YLa=s(SLe);w9r=r(YLa,"pretrained_model_name_or_path"),YLa.forEach(t),A9r=r(qS,":"),qS.forEach(t),L9r=i(ui),me=n(ui,"UL",{});var fe=s(me);a3=n(fe,"LI",{});var eio=s(a3);RLe=n(eio,"STRONG",{});var ZLa=s(RLe);y9r=r(ZLa,"albert"),ZLa.forEach(t),x9r=r(eio," \u2014 "),Gte=n(eio,"A",{href:!0});var KLa=s(Gte);k9r=r(KLa,"TFAlbertForPreTraining"),KLa.forEach(t),$9r=r(eio," (ALBERT model)"),eio.forEach(t),S9r=i(fe),n3=n(fe,"LI",{});var oio=s(n3);PLe=n(oio,"STRONG",{});var e9a=s(PLe);R9r=r(e9a,"bart"),e9a.forEach(t),P9r=r(oio," \u2014 "),qte=n(oio,"A",{href:!0});var o9a=s(qte);I9r=r(o9a,"TFBartForConditionalGeneration"),o9a.forEach(t),B9r=r(oio," (BART model)"),oio.forEach(t),N9r=i(fe),s3=n(fe,"LI",{});var rio=s(s3);ILe=n(rio,"STRONG",{});var r9a=s(ILe);D9r=r(r9a,"bert"),r9a.forEach(t),G9r=r(rio," \u2014 "),jte=n(rio,"A",{href:!0});var t9a=s(jte);q9r=r(t9a,"TFBertForPreTraining"),t9a.forEach(t),j9r=r(rio," (BERT 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with the `),mae=n(L2e,"A",{href:!0});var Y9a=s(mae);Hxr=r(Y9a,"from_pretrained()"),Y9a.forEach(t),Jxr=r(L2e," class method or the "),cae=n(L2e,"A",{href:!0});var Z9a=s(cae);Yxr=r(Z9a,"from_config()"),Z9a.forEach(t),Zxr=r(L2e,` class
method.`),L2e.forEach(t),Kxr=i(pi),JI=n(pi,"P",{});var sMo=s(JI);ekr=r(sMo,"This class cannot be instantiated directly using "),s9e=n(sMo,"CODE",{});var K9a=s(s9e);okr=r(K9a,"__init__()"),K9a.forEach(t),rkr=r(sMo," (throws an error)."),sMo.forEach(t),tkr=i(pi),Pt=n(pi,"DIV",{class:!0});var jS=s(Pt);T(YI.$$.fragment,jS),akr=i(jS),l9e=n(jS,"P",{});var eya=s(l9e);nkr=r(eya,"Instantiates one of the model classes of the library (with a pretraining head) from a configuration."),eya.forEach(t),skr=i(jS),Fm=n(jS,"P",{});var y2e=s(Fm);lkr=r(y2e,`Note:
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model\u2019s configuration. Use `),fae=n(y2e,"A",{href:!0});var rya=s(fae);mkr=r(rya,"from_pretrained()"),rya.forEach(t),ckr=r(y2e," to load the model weights."),y2e.forEach(t),fkr=i(jS),T($3.$$.fragment,jS),jS.forEach(t),gkr=i(pi),Yr=n(pi,"DIV",{class:!0});var _i=s(Yr);T(ZI.$$.fragment,_i),hkr=i(_i),d9e=n(_i,"P",{});var tya=s(d9e);ukr=r(tya,"Instantiate one of the model classes of the library (with a pretraining head) from a pretrained model."),tya.forEach(t),pkr=i(_i),En=n(_i,"P",{});var OS=s(En);_kr=r(OS,"The model class to instantiate is selected based on the "),m9e=n(OS,"CODE",{});var aya=s(m9e);bkr=r(aya,"model_type"),aya.forEach(t),vkr=r(OS,` property of the config object (either
passed as an argument or loaded from `),c9e=n(OS,"CODE",{});var nya=s(c9e);Tkr=r(nya,"pretrained_model_name_or_path"),nya.forEach(t),Fkr=r(OS,` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),f9e=n(OS,"CODE",{});var sya=s(f9e);Mkr=r(sya,"pretrained_model_name_or_path"),sya.forEach(t),Ekr=r(OS,":"),OS.forEach(t),Ckr=i(_i),Ae=n(_i,"UL",{});var ye=s(Ae);S3=n(ye,"LI",{});var wio=s(S3);g9e=n(wio,"STRONG",{});var lya=s(g9e);wkr=r(lya,"albert"),lya.forEach(t),Akr=r(wio," \u2014 "),gae=n(wio,"A",{href:!0});var iya=s(gae);Lkr=r(iya,"FlaxAlbertForPreTraining"),iya.forEach(t),ykr=r(wio," (ALBERT model)"),wio.forEach(t),xkr=i(ye),R3=n(ye,"LI",{});var Aio=s(R3);h9e=n(Aio,"STRONG",{});var dya=s(h9e);kkr=r(dya,"bart"),dya.forEach(t),$kr=r(Aio," \u2014 "),hae=n(Aio,"A",{href:!0});var mya=s(hae);Skr=r(mya,"FlaxBartForConditionalGeneration"),mya.forEach(t),Rkr=r(Aio," (BART model)"),Aio.forEach(t),Pkr=i(ye),P3=n(ye,"LI",{});var Lio=s(P3);u9e=n(Lio,"STRONG",{});var cya=s(u9e);Ikr=r(cya,"bert"),cya.forEach(t),Bkr=r(Lio," \u2014 "),uae=n(Lio,"A",{href:!0});var fya=s(uae);Nkr=r(fya,"FlaxBertForPreTraining"),fya.forEach(t),Dkr=r(Lio," (BERT 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"),Aae=n(Dio,"A",{href:!0});var Pya=s(Aae);D$r=r(Pya,"FlaxXLMRobertaForMaskedLM"),Pya.forEach(t),G$r=r(Dio," (XLM-RoBERTa model)"),Dio.forEach(t),ye.forEach(t),q$r=i(_i),T(W3.$$.fragment,_i),_i.forEach(t),pi.forEach(t),Lvo=i(c),Mm=n(c,"H2",{class:!0});var lMo=s(Mm);Q3=n(lMo,"A",{id:!0,class:!0,href:!0});var Iya=s(Q3);L9e=n(Iya,"SPAN",{});var Bya=s(L9e);T(KI.$$.fragment,Bya),Bya.forEach(t),Iya.forEach(t),j$r=i(lMo),y9e=n(lMo,"SPAN",{});var Nya=s(y9e);O$r=r(Nya,"Natural Language Processing"),Nya.forEach(t),lMo.forEach(t),yvo=i(c),Lae=n(c,"P",{});var Dya=s(Lae);V$r=r(Dya,"The following auto classes are available for the following natural language processing tasks."),Dya.forEach(t),xvo=i(c),Em=n(c,"H3",{class:!0});var iMo=s(Em);U3=n(iMo,"A",{id:!0,class:!0,href:!0});var Gya=s(U3);x9e=n(Gya,"SPAN",{});var qya=s(x9e);T(eB.$$.fragment,qya),qya.forEach(t),Gya.forEach(t),X$r=i(iMo),k9e=n(iMo,"SPAN",{});var jya=s(k9e);z$r=r(jya,"AutoModelForCausalLM"),jya.forEach(t),iMo.forEach(t),kvo=i(c),Uo=n(c,"DIV",{class:!0});var bi=s(Uo);T(oB.$$.fragment,bi),W$r=i(bi),Cm=n(bi,"P",{});var x2e=s(Cm);Q$r=r(x2e,`This is a generic model class that will be instantiated as one of the model classes of the library (with a causal language modeling head) when created
with the `),yae=n(x2e,"A",{href:!0});var Oya=s(yae);U$r=r(Oya,"from_pretrained()"),Oya.forEach(t),H$r=r(x2e," class method or the "),xae=n(x2e,"A",{href:!0});var Vya=s(xae);J$r=r(Vya,"from_config()"),Vya.forEach(t),Y$r=r(x2e,` class
method.`),x2e.forEach(t),Z$r=i(bi),rB=n(bi,"P",{});var dMo=s(rB);K$r=r(dMo,"This class cannot be instantiated directly using "),$9e=n(dMo,"CODE",{});var Xya=s($9e);eSr=r(Xya,"__init__()"),Xya.forEach(t),oSr=r(dMo," (throws an error)."),dMo.forEach(t),rSr=i(bi),It=n(bi,"DIV",{class:!0});var VS=s(It);T(tB.$$.fragment,VS),tSr=i(VS),S9e=n(VS,"P",{});var zya=s(S9e);aSr=r(zya,"Instantiates one of the model classes of the library (with a causal language modeling head) from a configuration."),zya.forEach(t),nSr=i(VS),wm=n(VS,"P",{});var k2e=s(wm);sSr=r(k2e,`Note:
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model\u2019s configuration. Use `),kae=n(k2e,"A",{href:!0});var Qya=s(kae);dSr=r(Qya,"from_pretrained()"),Qya.forEach(t),mSr=r(k2e," to load the model weights."),k2e.forEach(t),cSr=i(VS),T(H3.$$.fragment,VS),VS.forEach(t),fSr=i(bi),io=n(bi,"DIV",{class:!0});var Da=s(io);T(aB.$$.fragment,Da),gSr=i(Da),P9e=n(Da,"P",{});var Uya=s(P9e);hSr=r(Uya,"Instantiate one of the model classes of the library (with a causal language modeling head) from a pretrained model."),Uya.forEach(t),uSr=i(Da),Cn=n(Da,"P",{});var XS=s(Cn);pSr=r(XS,"The model class to instantiate is selected based on the "),I9e=n(XS,"CODE",{});var Hya=s(I9e);_Sr=r(Hya,"model_type"),Hya.forEach(t),bSr=r(XS,` property of the config object (either
passed as an argument or loaded from `),B9e=n(XS,"CODE",{});var Jya=s(B9e);vSr=r(Jya,"pretrained_model_name_or_path"),Jya.forEach(t),TSr=r(XS,` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),N9e=n(XS,"CODE",{});var Yya=s(N9e);FSr=r(Yya,"pretrained_model_name_or_path"),Yya.forEach(t),MSr=r(XS,":"),XS.forEach(t),ESr=i(Da),H=n(Da,"UL",{});var J=s(H);J3=n(J,"LI",{});var Gio=s(J3);D9e=n(Gio,"STRONG",{});var Zya=s(D9e);CSr=r(Zya,"bart"),Zya.forEach(t),wSr=r(Gio," \u2014 "),$ae=n(Gio,"A",{href:!0});var Kya=s($ae);ASr=r(Kya,"BartForCausalLM"),Kya.forEach(t),LSr=r(Gio," (BART model)"),Gio.forEach(t),ySr=i(J),Y3=n(J,"LI",{});var qio=s(Y3);G9e=n(qio,"STRONG",{});var exa=s(G9e);xSr=r(exa,"bert"),exa.forEach(t),kSr=r(qio," \u2014 "),Sae=n(qio,"A",{href:!0});var oxa=s(Sae);$Sr=r(oxa,"BertLMHeadModel"),oxa.forEach(t),SSr=r(qio," (BERT model)"),qio.forEach(t),RSr=i(J),Z3=n(J,"LI",{});var jio=s(Z3);q9e=n(jio,"STRONG",{});var rxa=s(q9e);PSr=r(rxa,"bert-generation"),rxa.forEach(t),ISr=r(jio," \u2014 "),Rae=n(jio,"A",{href:!0});var txa=s(Rae);BSr=r(txa,"BertGenerationDecoder"),txa.forEach(t),NSr=r(jio," (Bert Generation 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Hio=s(s4);U9e=n(Hio,"STRONG",{});var bxa=s(U9e);TRr=r(bxa,"codegen"),bxa.forEach(t),FRr=r(Hio," \u2014 "),jae=n(Hio,"A",{href:!0});var vxa=s(jae);MRr=r(vxa,"CodeGenForCausalLM"),vxa.forEach(t),ERr=r(Hio," (CodeGen model)"),Hio.forEach(t),CRr=i(J),l4=n(J,"LI",{});var Jio=s(l4);H9e=n(Jio,"STRONG",{});var Txa=s(H9e);wRr=r(Txa,"ctrl"),Txa.forEach(t),ARr=r(Jio," \u2014 "),Oae=n(Jio,"A",{href:!0});var Fxa=s(Oae);LRr=r(Fxa,"CTRLLMHeadModel"),Fxa.forEach(t),yRr=r(Jio," (CTRL model)"),Jio.forEach(t),xRr=i(J),i4=n(J,"LI",{});var Yio=s(i4);J9e=n(Yio,"STRONG",{});var Mxa=s(J9e);kRr=r(Mxa,"data2vec-text"),Mxa.forEach(t),$Rr=r(Yio," \u2014 "),Vae=n(Yio,"A",{href:!0});var Exa=s(Vae);SRr=r(Exa,"Data2VecTextForCausalLM"),Exa.forEach(t),RRr=r(Yio," (Data2VecText model)"),Yio.forEach(t),PRr=i(J),d4=n(J,"LI",{});var Zio=s(d4);Y9e=n(Zio,"STRONG",{});var Cxa=s(Y9e);IRr=r(Cxa,"electra"),Cxa.forEach(t),BRr=r(Zio," \u2014 "),Xae=n(Zio,"A",{href:!0});var 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model)"),Hdo.forEach(t),we.forEach(t),aGr=i(Ti),T(mC.$$.fragment,Ti),Ti.forEach(t),vi.forEach(t),Rvo=i(c),xm=n(c,"H3",{class:!0});var fMo=s(xm);cC=n(fMo,"A",{id:!0,class:!0,href:!0});var R$a=s(cC);nxe=n(R$a,"SPAN",{});var P$a=s(nxe);T(mB.$$.fragment,P$a),P$a.forEach(t),R$a.forEach(t),nGr=i(fMo),sxe=n(fMo,"SPAN",{});var I$a=s(sxe);sGr=r(I$a,"FlaxAutoModelForCausalLM"),I$a.forEach(t),fMo.forEach(t),Pvo=i(c),Jo=n(c,"DIV",{class:!0});var Fi=s(Jo);T(cB.$$.fragment,Fi),lGr=i(Fi),km=n(Fi,"P",{});var R2e=s(km);iGr=r(R2e,`This is a generic model class that will be instantiated as one of the model classes of the library (with a causal language modeling head) when created
with the `),Xne=n(R2e,"A",{href:!0});var B$a=s(Xne);dGr=r(B$a,"from_pretrained()"),B$a.forEach(t),mGr=r(R2e," class method or the "),zne=n(R2e,"A",{href:!0});var N$a=s(zne);cGr=r(N$a,"from_config()"),N$a.forEach(t),fGr=r(R2e,` class
method.`),R2e.forEach(t),gGr=i(Fi),fB=n(Fi,"P",{});var gMo=s(fB);hGr=r(gMo,"This class cannot be instantiated directly using "),lxe=n(gMo,"CODE",{});var D$a=s(lxe);uGr=r(D$a,"__init__()"),D$a.forEach(t),pGr=r(gMo," (throws an error)."),gMo.forEach(t),_Gr=i(Fi),Nt=n(Fi,"DIV",{class:!0});var QS=s(Nt);T(gB.$$.fragment,QS),bGr=i(QS),ixe=n(QS,"P",{});var G$a=s(ixe);vGr=r(G$a,"Instantiates one of the model classes of the library (with a causal language modeling head) from a configuration."),G$a.forEach(t),TGr=i(QS),$m=n(QS,"P",{});var P2e=s($m);FGr=r(P2e,`Note:
Loading a model from its configuration file does `),dxe=n(P2e,"STRONG",{});var q$a=s(dxe);MGr=r(q$a,"not"),q$a.forEach(t),EGr=r(P2e,` load the model weights. It only affects the
model\u2019s configuration. Use `),Wne=n(P2e,"A",{href:!0});var j$a=s(Wne);CGr=r(j$a,"from_pretrained()"),j$a.forEach(t),wGr=r(P2e," to load the model weights."),P2e.forEach(t),AGr=i(QS),T(fC.$$.fragment,QS),QS.forEach(t),LGr=i(Fi),Kr=n(Fi,"DIV",{class:!0});var Mi=s(Kr);T(hB.$$.fragment,Mi),yGr=i(Mi),mxe=n(Mi,"P",{});var O$a=s(mxe);xGr=r(O$a,"Instantiate one of the model classes of the library (with a causal language modeling head) from a pretrained model."),O$a.forEach(t),kGr=i(Mi),An=n(Mi,"P",{});var US=s(An);$Gr=r(US,"The model class to instantiate is selected based on the "),cxe=n(US,"CODE",{});var V$a=s(cxe);SGr=r(V$a,"model_type"),V$a.forEach(t),RGr=r(US,` property of the config object (either
passed as an argument or loaded from `),fxe=n(US,"CODE",{});var X$a=s(fxe);PGr=r(X$a,"pretrained_model_name_or_path"),X$a.forEach(t),IGr=r(US,` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),gxe=n(US,"CODE",{});var z$a=s(gxe);BGr=r(z$a,"pretrained_model_name_or_path"),z$a.forEach(t),NGr=r(US,":"),US.forEach(t),DGr=i(Mi),Le=n(Mi,"UL",{});var xe=s(Le);gC=n(xe,"LI",{});var Jdo=s(gC);hxe=n(Jdo,"STRONG",{});var W$a=s(hxe);GGr=r(W$a,"bart"),W$a.forEach(t),qGr=r(Jdo," \u2014 "),Qne=n(Jdo,"A",{href:!0});var Q$a=s(Qne);jGr=r(Q$a,"FlaxBartForCausalLM"),Q$a.forEach(t),OGr=r(Jdo," (BART model)"),Jdo.forEach(t),VGr=i(xe),hC=n(xe,"LI",{});var Ydo=s(hC);uxe=n(Ydo,"STRONG",{});var U$a=s(uxe);XGr=r(U$a,"bert"),U$a.forEach(t),zGr=r(Ydo," \u2014 "),Une=n(Ydo,"A",{href:!0});var H$a=s(Une);WGr=r(H$a,"FlaxBertForCausalLM"),H$a.forEach(t),QGr=r(Ydo," (BERT model)"),Ydo.forEach(t),UGr=i(xe),uC=n(xe,"LI",{});var Zdo=s(uC);pxe=n(Zdo,"STRONG",{});var J$a=s(pxe);HGr=r(J$a,"big_bird"),J$a.forEach(t),JGr=r(Zdo," \u2014 "),Hne=n(Zdo,"A",{href:!0});var Y$a=s(Hne);YGr=r(Y$a,"FlaxBigBirdForCausalLM"),Y$a.forEach(t),ZGr=r(Zdo," (BigBird model)"),Zdo.forEach(t),KGr=i(xe),pC=n(xe,"LI",{});var Kdo=s(pC);_xe=n(Kdo,"STRONG",{});var Z$a=s(_xe);eqr=r(Z$a,"electra"),Z$a.forEach(t),oqr=r(Kdo," \u2014 "),Jne=n(Kdo,"A",{href:!0});var K$a=s(Jne);rqr=r(K$a,"FlaxElectraForCausalLM"),K$a.forEach(t),tqr=r(Kdo," (ELECTRA model)"),Kdo.forEach(t),aqr=i(xe),_C=n(xe,"LI",{});var emo=s(_C);bxe=n(emo,"STRONG",{});var eSa=s(bxe);nqr=r(eSa,"gpt-sw3"),eSa.forEach(t),sqr=r(emo," \u2014 "),Yne=n(emo,"A",{href:!0});var oSa=s(Yne);lqr=r(oSa,"FlaxGPT2LMHeadModel"),oSa.forEach(t),iqr=r(emo," (GPT-Sw3 model)"),emo.forEach(t),dqr=i(xe),bC=n(xe,"LI",{});var omo=s(bC);vxe=n(omo,"STRONG",{});var rSa=s(vxe);mqr=r(rSa,"gpt2"),rSa.forEach(t),cqr=r(omo," \u2014 "),Zne=n(omo,"A",{href:!0});var tSa=s(Zne);fqr=r(tSa,"FlaxGPT2LMHeadModel"),tSa.forEach(t),gqr=r(omo," (OpenAI GPT-2 model)"),omo.forEach(t),hqr=i(xe),vC=n(xe,"LI",{});var rmo=s(vC);Txe=n(rmo,"STRONG",{});var aSa=s(Txe);uqr=r(aSa,"gpt_neo"),aSa.forEach(t),pqr=r(rmo," \u2014 "),Kne=n(rmo,"A",{href:!0});var nSa=s(Kne);_qr=r(nSa,"FlaxGPTNeoForCausalLM"),nSa.forEach(t),bqr=r(rmo," (GPT Neo model)"),rmo.forEach(t),vqr=i(xe),TC=n(xe,"LI",{});var tmo=s(TC);Fxe=n(tmo,"STRONG",{});var sSa=s(Fxe);Tqr=r(sSa,"gptj"),sSa.forEach(t),Fqr=r(tmo," \u2014 "),ese=n(tmo,"A",{href:!0});var lSa=s(ese);Mqr=r(lSa,"FlaxGPTJForCausalLM"),lSa.forEach(t),Eqr=r(tmo," (GPT-J model)"),tmo.forEach(t),Cqr=i(xe),FC=n(xe,"LI",{});var amo=s(FC);Mxe=n(amo,"STRONG",{});var iSa=s(Mxe);wqr=r(iSa,"opt"),iSa.forEach(t),Aqr=r(amo," \u2014 "),ose=n(amo,"A",{href:!0});var dSa=s(ose);Lqr=r(dSa,"FlaxOPTForCausalLM"),dSa.forEach(t),yqr=r(amo," (OPT model)"),amo.forEach(t),xqr=i(xe),MC=n(xe,"LI",{});var nmo=s(MC);Exe=n(nmo,"STRONG",{});var mSa=s(Exe);kqr=r(mSa,"roberta"),mSa.forEach(t),$qr=r(nmo," \u2014 "),rse=n(nmo,"A",{href:!0});var cSa=s(rse);Sqr=r(cSa,"FlaxRobertaForCausalLM"),cSa.forEach(t),Rqr=r(nmo," (RoBERTa model)"),nmo.forEach(t),Pqr=i(xe),EC=n(xe,"LI",{});var smo=s(EC);Cxe=n(smo,"STRONG",{});var fSa=s(Cxe);Iqr=r(fSa,"roberta-prelayernorm"),fSa.forEach(t),Bqr=r(smo," \u2014 "),tse=n(smo,"A",{href:!0});var gSa=s(tse);Nqr=r(gSa,"FlaxRobertaPreLayerNormForCausalLM"),gSa.forEach(t),Dqr=r(smo," (RoBERTa-PreLayerNorm model)"),smo.forEach(t),Gqr=i(xe),CC=n(xe,"LI",{});var lmo=s(CC);wxe=n(lmo,"STRONG",{});var hSa=s(wxe);qqr=r(hSa,"xglm"),hSa.forEach(t),jqr=r(lmo," \u2014 "),ase=n(lmo,"A",{href:!0});var uSa=s(ase);Oqr=r(uSa,"FlaxXGLMForCausalLM"),uSa.forEach(t),Vqr=r(lmo," (XGLM model)"),lmo.forEach(t),Xqr=i(xe),wC=n(xe,"LI",{});var imo=s(wC);Axe=n(imo,"STRONG",{});var pSa=s(Axe);zqr=r(pSa,"xlm-roberta"),pSa.forEach(t),Wqr=r(imo," \u2014 "),nse=n(imo,"A",{href:!0});var _Sa=s(nse);Qqr=r(_Sa,"FlaxXLMRobertaForCausalLM"),_Sa.forEach(t),Uqr=r(imo," (XLM-RoBERTa model)"),imo.forEach(t),xe.forEach(t),Hqr=i(Mi),T(AC.$$.fragment,Mi),Mi.forEach(t),Fi.forEach(t),Ivo=i(c),Sm=n(c,"H3",{class:!0});var hMo=s(Sm);LC=n(hMo,"A",{id:!0,class:!0,href:!0});var bSa=s(LC);Lxe=n(bSa,"SPAN",{});var vSa=s(Lxe);T(uB.$$.fragment,vSa),vSa.forEach(t),bSa.forEach(t),Jqr=i(hMo),yxe=n(hMo,"SPAN",{});var TSa=s(yxe);Yqr=r(TSa,"AutoModelForMaskedLM"),TSa.forEach(t),hMo.forEach(t),Bvo=i(c),Yo=n(c,"DIV",{class:!0});var Ei=s(Yo);T(pB.$$.fragment,Ei),Zqr=i(Ei),Rm=n(Ei,"P",{});var I2e=s(Rm);Kqr=r(I2e,`This is a generic model class that will be instantiated as one of the model classes of the library (with a masked language modeling head) when created
with the `),sse=n(I2e,"A",{href:!0});var FSa=s(sse);ejr=r(FSa,"from_pretrained()"),FSa.forEach(t),ojr=r(I2e," class method or the "),lse=n(I2e,"A",{href:!0});var MSa=s(lse);rjr=r(MSa,"from_config()"),MSa.forEach(t),tjr=r(I2e,` class
method.`),I2e.forEach(t),ajr=i(Ei),_B=n(Ei,"P",{});var uMo=s(_B);njr=r(uMo,"This class cannot be instantiated directly using "),xxe=n(uMo,"CODE",{});var ESa=s(xxe);sjr=r(ESa,"__init__()"),ESa.forEach(t),ljr=r(uMo," (throws an error)."),uMo.forEach(t),ijr=i(Ei),Dt=n(Ei,"DIV",{class:!0});var HS=s(Dt);T(bB.$$.fragment,HS),djr=i(HS),kxe=n(HS,"P",{});var CSa=s(kxe);mjr=r(CSa,"Instantiates one of the model classes of the library (with a masked language modeling head) from a configuration."),CSa.forEach(t),cjr=i(HS),Pm=n(HS,"P",{});var B2e=s(Pm);fjr=r(B2e,`Note:
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model\u2019s configuration. Use `),ise=n(B2e,"A",{href:!0});var ASa=s(ise);ujr=r(ASa,"from_pretrained()"),ASa.forEach(t),pjr=r(B2e," to load the model weights."),B2e.forEach(t),_jr=i(HS),T(yC.$$.fragment,HS),HS.forEach(t),bjr=i(Ei),mo=n(Ei,"DIV",{class:!0});var Ga=s(mo);T(vB.$$.fragment,Ga),vjr=i(Ga),Sxe=n(Ga,"P",{});var LSa=s(Sxe);Tjr=r(LSa,"Instantiate one of the model classes of the library (with a masked language modeling head) from a pretrained model."),LSa.forEach(t),Fjr=i(Ga),Ln=n(Ga,"P",{});var JS=s(Ln);Mjr=r(JS,"The model class to instantiate is selected based on the "),Rxe=n(JS,"CODE",{});var ySa=s(Rxe);Ejr=r(ySa,"model_type"),ySa.forEach(t),Cjr=r(JS,` property of the config object (either
passed as an argument or loaded from `),Pxe=n(JS,"CODE",{});var xSa=s(Pxe);wjr=r(xSa,"pretrained_model_name_or_path"),xSa.forEach(t),Ajr=r(JS,` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),Ixe=n(JS,"CODE",{});var kSa=s(Ixe);Ljr=r(kSa,"pretrained_model_name_or_path"),kSa.forEach(t),yjr=r(JS,":"),JS.forEach(t),xjr=i(Ga),K=n(Ga,"UL",{});var ee=s(K);xC=n(ee,"LI",{});var dmo=s(xC);Bxe=n(dmo,"STRONG",{});var $Sa=s(Bxe);kjr=r($Sa,"albert"),$Sa.forEach(t),$jr=r(dmo," \u2014 "),dse=n(dmo,"A",{href:!0});var SSa=s(dse);Sjr=r(SSa,"AlbertForMaskedLM"),SSa.forEach(t),Rjr=r(dmo," (ALBERT model)"),dmo.forEach(t),Pjr=i(ee),kC=n(ee,"LI",{});var mmo=s(kC);Nxe=n(mmo,"STRONG",{});var RSa=s(Nxe);Ijr=r(RSa,"bart"),RSa.forEach(t),Bjr=r(mmo," \u2014 "),mse=n(mmo,"A",{href:!0});var PSa=s(mse);Njr=r(PSa,"BartForConditionalGeneration"),PSa.forEach(t),Djr=r(mmo," (BART model)"),mmo.forEach(t),Gjr=i(ee),$C=n(ee,"LI",{});var cmo=s($C);Dxe=n(cmo,"STRONG",{});var ISa=s(Dxe);qjr=r(ISa,"bert"),ISa.forEach(t),jjr=r(cmo," \u2014 "),cse=n(cmo,"A",{href:!0});var BSa=s(cse);Ojr=r(BSa,"BertForMaskedLM"),BSa.forEach(t),Vjr=r(cmo," (BERT model)"),cmo.forEach(t),Xjr=i(ee),SC=n(ee,"LI",{});var fmo=s(SC);Gxe=n(fmo,"STRONG",{});var NSa=s(Gxe);zjr=r(NSa,"big_bird"),NSa.forEach(t),Wjr=r(fmo," \u2014 "),fse=n(fmo,"A",{href:!0});var DSa=s(fse);Qjr=r(DSa,"BigBirdForMaskedLM"),DSa.forEach(t),Ujr=r(fmo," (BigBird model)"),fmo.forEach(t),Hjr=i(ee),RC=n(ee,"LI",{});var gmo=s(RC);qxe=n(gmo,"STRONG",{});var GSa=s(qxe);Jjr=r(GSa,"camembert"),GSa.forEach(t),Yjr=r(gmo," \u2014 "),gse=n(gmo,"A",{href:!0});var qSa=s(gse);Zjr=r(qSa,"CamembertForMaskedLM"),qSa.forEach(t),Kjr=r(gmo," (CamemBERT model)"),gmo.forEach(t),eOr=i(ee),PC=n(ee,"LI",{});var hmo=s(PC);jxe=n(hmo,"STRONG",{});var jSa=s(jxe);oOr=r(jSa,"convbert"),jSa.forEach(t),rOr=r(hmo," \u2014 "),hse=n(hmo,"A",{href:!0});var OSa=s(hse);tOr=r(OSa,"ConvBertForMaskedLM"),OSa.forEach(t),aOr=r(hmo," (ConvBERT model)"),hmo.forEach(t),nOr=i(ee),IC=n(ee,"LI",{});var umo=s(IC);Oxe=n(umo,"STRONG",{});var VSa=s(Oxe);sOr=r(VSa,"data2vec-text"),VSa.forEach(t),lOr=r(umo," \u2014 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model\u2019s configuration. Use `),Nle=n(O2e,"A",{href:!0});var oBa=s(Nle);cJr=r(oBa,"from_pretrained()"),oBa.forEach(t),fJr=r(O2e," to load the model weights."),O2e.forEach(t),gJr=i(oR),T(n0.$$.fragment,oR),oR.forEach(t),hJr=i(yi),co=n(yi,"DIV",{class:!0});var qa=s(co);T(PB.$$.fragment,qa),uJr=i(qa),x$e=n(qa,"P",{});var rBa=s(x$e);pJr=r(rBa,"Instantiate one of the model classes of the library (with a sequence-to-sequence language modeling head) from a pretrained model."),rBa.forEach(t),_Jr=i(qa),kn=n(qa,"P",{});var rR=s(kn);bJr=r(rR,"The model class to instantiate is selected based on the "),k$e=n(rR,"CODE",{});var tBa=s(k$e);vJr=r(tBa,"model_type"),tBa.forEach(t),TJr=r(rR,` property of the config object (either
passed as an argument or loaded from `),$$e=n(rR,"CODE",{});var aBa=s($$e);FJr=r(aBa,"pretrained_model_name_or_path"),aBa.forEach(t),MJr=r(rR,` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),S$e=n(rR,"CODE",{});var nBa=s(S$e);EJr=r(nBa,"pretrained_model_name_or_path"),nBa.forEach(t),CJr=r(rR,":"),rR.forEach(t),wJr=i(qa),be=n(qa,"UL",{});var Fe=s(be);s0=n(Fe,"LI",{});var $co=s(s0);R$e=n($co,"STRONG",{});var sBa=s(R$e);AJr=r(sBa,"bart"),sBa.forEach(t),LJr=r($co," \u2014 "),Dle=n($co,"A",{href:!0});var lBa=s(Dle);yJr=r(lBa,"BartForConditionalGeneration"),lBa.forEach(t),xJr=r($co," (BART model)"),$co.forEach(t),kJr=i(Fe),l0=n(Fe,"LI",{});var Sco=s(l0);P$e=n(Sco,"STRONG",{});var iBa=s(P$e);$Jr=r(iBa,"bigbird_pegasus"),iBa.forEach(t),SJr=r(Sco," \u2014 "),Gle=n(Sco,"A",{href:!0});var dBa=s(Gle);RJr=r(dBa,"BigBirdPegasusForConditionalGeneration"),dBa.forEach(t),PJr=r(Sco," (BigBird-Pegasus model)"),Sco.forEach(t),IJr=i(Fe),i0=n(Fe,"LI",{});var Rco=s(i0);I$e=n(Rco,"STRONG",{});var mBa=s(I$e);BJr=r(mBa,"blenderbot"),mBa.forEach(t),NJr=r(Rco," \u2014 "),qle=n(Rco,"A",{href:!0});var cBa=s(qle);DJr=r(cBa,"BlenderbotForConditionalGeneration"),cBa.forEach(t),GJr=r(Rco," (Blenderbot model)"),Rco.forEach(t),qJr=i(Fe),d0=n(Fe,"LI",{});var Pco=s(d0);B$e=n(Pco,"STRONG",{});var fBa=s(B$e);jJr=r(fBa,"blenderbot-small"),fBa.forEach(t),OJr=r(Pco," \u2014 "),jle=n(Pco,"A",{href:!0});var gBa=s(jle);VJr=r(gBa,"BlenderbotSmallForConditionalGeneration"),gBa.forEach(t),XJr=r(Pco," (BlenderbotSmall model)"),Pco.forEach(t),zJr=i(Fe),m0=n(Fe,"LI",{});var Ico=s(m0);N$e=n(Ico,"STRONG",{});var hBa=s(N$e);WJr=r(hBa,"encoder-decoder"),hBa.forEach(t),QJr=r(Ico," \u2014 "),Ole=n(Ico,"A",{href:!0});var uBa=s(Ole);UJr=r(uBa,"EncoderDecoderModel"),uBa.forEach(t),HJr=r(Ico," (Encoder decoder model)"),Ico.forEach(t),JJr=i(Fe),c0=n(Fe,"LI",{});var Bco=s(c0);D$e=n(Bco,"STRONG",{});var pBa=s(D$e);YJr=r(pBa,"fsmt"),pBa.forEach(t),ZJr=r(Bco," \u2014 "),Vle=n(Bco,"A",{href:!0});var _Ba=s(Vle);KJr=r(_Ba,"FSMTForConditionalGeneration"),_Ba.forEach(t),eYr=r(Bco," (FairSeq Machine-Translation model)"),Bco.forEach(t),oYr=i(Fe),f0=n(Fe,"LI",{});var Nco=s(f0);G$e=n(Nco,"STRONG",{});var bBa=s(G$e);rYr=r(bBa,"led"),bBa.forEach(t),tYr=r(Nco," \u2014 "),Xle=n(Nco,"A",{href:!0});var vBa=s(Xle);aYr=r(vBa,"LEDForConditionalGeneration"),vBa.forEach(t),nYr=r(Nco," (LED model)"),Nco.forEach(t),sYr=i(Fe),g0=n(Fe,"LI",{});var Dco=s(g0);q$e=n(Dco,"STRONG",{});var TBa=s(q$e);lYr=r(TBa,"longt5"),TBa.forEach(t),iYr=r(Dco," \u2014 "),zle=n(Dco,"A",{href:!0});var FBa=s(zle);dYr=r(FBa,"LongT5ForConditionalGeneration"),FBa.forEach(t),mYr=r(Dco," (LongT5 model)"),Dco.forEach(t),cYr=i(Fe),h0=n(Fe,"LI",{});var Gco=s(h0);j$e=n(Gco,"STRONG",{});var MBa=s(j$e);fYr=r(MBa,"m2m_100"),MBa.forEach(t),gYr=r(Gco," \u2014 "),Wle=n(Gco,"A",{href:!0});var EBa=s(Wle);hYr=r(EBa,"M2M100ForConditionalGeneration"),EBa.forEach(t),uYr=r(Gco," (M2M100 model)"),Gco.forEach(t),pYr=i(Fe),u0=n(Fe,"LI",{});var qco=s(u0);O$e=n(qco,"STRONG",{});var CBa=s(O$e);_Yr=r(CBa,"marian"),CBa.forEach(t),bYr=r(qco," \u2014 "),Qle=n(qco,"A",{href:!0});var wBa=s(Qle);vYr=r(wBa,"MarianMTModel"),wBa.forEach(t),TYr=r(qco," (Marian model)"),qco.forEach(t),FYr=i(Fe),p0=n(Fe,"LI",{});var jco=s(p0);V$e=n(jco,"STRONG",{});var ABa=s(V$e);MYr=r(ABa,"mbart"),ABa.forEach(t),EYr=r(jco," \u2014 "),Ule=n(jco,"A",{href:!0});var LBa=s(Ule);CYr=r(LBa,"MBartForConditionalGeneration"),LBa.forEach(t),wYr=r(jco," (mBART model)"),jco.forEach(t),AYr=i(Fe),_0=n(Fe,"LI",{});var Oco=s(_0);X$e=n(Oco,"STRONG",{});var yBa=s(X$e);LYr=r(yBa,"mt5"),yBa.forEach(t),yYr=r(Oco," \u2014 "),Hle=n(Oco,"A",{href:!0});var xBa=s(Hle);xYr=r(xBa,"MT5ForConditionalGeneration"),xBa.forEach(t),kYr=r(Oco," (MT5 model)"),Oco.forEach(t),$Yr=i(Fe),b0=n(Fe,"LI",{});var Vco=s(b0);z$e=n(Vco,"STRONG",{});var kBa=s(z$e);SYr=r(kBa,"mvp"),kBa.forEach(t),RYr=r(Vco," \u2014 "),Jle=n(Vco,"A",{href:!0});var $Ba=s(Jle);PYr=r($Ba,"MvpForConditionalGeneration"),$Ba.forEach(t),IYr=r(Vco," (MVP model)"),Vco.forEach(t),BYr=i(Fe),v0=n(Fe,"LI",{});var Xco=s(v0);W$e=n(Xco,"STRONG",{});var SBa=s(W$e);NYr=r(SBa,"nllb"),SBa.forEach(t),DYr=r(Xco," \u2014 "),Yle=n(Xco,"A",{href:!0});var RBa=s(Yle);GYr=r(RBa,"M2M100ForConditionalGeneration"),RBa.forEach(t),qYr=r(Xco," (NLLB model)"),Xco.forEach(t),jYr=i(Fe),T0=n(Fe,"LI",{});var zco=s(T0);Q$e=n(zco,"STRONG",{});var PBa=s(Q$e);OYr=r(PBa,"pegasus"),PBa.forEach(t),VYr=r(zco," \u2014 "),Zle=n(zco,"A",{href:!0});var IBa=s(Zle);XYr=r(IBa,"PegasusForConditionalGeneration"),IBa.forEach(t),zYr=r(zco," (Pegasus model)"),zco.forEach(t),WYr=i(Fe),F0=n(Fe,"LI",{});var Wco=s(F0);U$e=n(Wco,"STRONG",{});var BBa=s(U$e);QYr=r(BBa,"pegasus_x"),BBa.forEach(t),UYr=r(Wco," \u2014 "),Kle=n(Wco,"A",{href:!0});var NBa=s(Kle);HYr=r(NBa,"PegasusXForConditionalGeneration"),NBa.forEach(t),JYr=r(Wco," (PEGASUS-X model)"),Wco.forEach(t),YYr=i(Fe),M0=n(Fe,"LI",{});var Qco=s(M0);H$e=n(Qco,"STRONG",{});var DBa=s(H$e);ZYr=r(DBa,"plbart"),DBa.forEach(t),KYr=r(Qco," \u2014 "),eie=n(Qco,"A",{href:!0});var GBa=s(eie);eZr=r(GBa,"PLBartForConditionalGeneration"),GBa.forEach(t),oZr=r(Qco," (PLBart model)"),Qco.forEach(t),rZr=i(Fe),E0=n(Fe,"LI",{});var Uco=s(E0);J$e=n(Uco,"STRONG",{});var qBa=s(J$e);tZr=r(qBa,"prophetnet"),qBa.forEach(t),aZr=r(Uco," \u2014 "),oie=n(Uco,"A",{href:!0});var jBa=s(oie);nZr=r(jBa,"ProphetNetForConditionalGeneration"),jBa.forEach(t),sZr=r(Uco," (ProphetNet model)"),Uco.forEach(t),lZr=i(Fe),C0=n(Fe,"LI",{});var Hco=s(C0);Y$e=n(Hco,"STRONG",{});var OBa=s(Y$e);iZr=r(OBa,"switch_transformers"),OBa.forEach(t),dZr=r(Hco," \u2014 "),rie=n(Hco,"A",{href:!0});var VBa=s(rie);mZr=r(VBa,"SwitchTransformersForConditionalGeneration"),VBa.forEach(t),cZr=r(Hco," (SwitchTransformers model)"),Hco.forEach(t),fZr=i(Fe),w0=n(Fe,"LI",{});var Jco=s(w0);Z$e=n(Jco,"STRONG",{});var XBa=s(Z$e);gZr=r(XBa,"t5"),XBa.forEach(t),hZr=r(Jco," \u2014 "),tie=n(Jco,"A",{href:!0});var zBa=s(tie);uZr=r(zBa,"T5ForConditionalGeneration"),zBa.forEach(t),pZr=r(Jco," (T5 model)"),Jco.forEach(t),_Zr=i(Fe),A0=n(Fe,"LI",{});var Yco=s(A0);K$e=n(Yco,"STRONG",{});var WBa=s(K$e);bZr=r(WBa,"xlm-prophetnet"),WBa.forEach(t),vZr=r(Yco," \u2014 "),aie=n(Yco,"A",{href:!0});var QBa=s(aie);TZr=r(QBa,"XLMProphetNetForConditionalGeneration"),QBa.forEach(t),FZr=r(Yco," (XLM-ProphetNet model)"),Yco.forEach(t),Fe.forEach(t),MZr=i(qa),L0=n(qa,"P",{});var Zco=s(L0);EZr=r(Zco,"The model is set in evaluation mode by default using "),eSe=n(Zco,"CODE",{});var UBa=s(eSe);CZr=r(UBa,"model.eval()"),UBa.forEach(t),wZr=r(Zco,` (so for instance, dropout modules are
deactivated). To train the model, you should first set it back in training mode with `),oSe=n(Zco,"CODE",{});var HBa=s(oSe);AZr=r(HBa,"model.train()"),HBa.forEach(t),Zco.forEach(t),LZr=i(qa),T(y0.$$.fragment,qa),qa.forEach(t),yi.forEach(t),Vvo=i(c),Xm=n(c,"H3",{class:!0});var MMo=s(Xm);x0=n(MMo,"A",{id:!0,class:!0,href:!0});var JBa=s(x0);rSe=n(JBa,"SPAN",{});var YBa=s(rSe);T(IB.$$.fragment,YBa),YBa.forEach(t),JBa.forEach(t),yZr=i(MMo),tSe=n(MMo,"SPAN",{});var ZBa=s(tSe);xZr=r(ZBa,"TFAutoModelForSeq2SeqLM"),ZBa.forEach(t),MMo.forEach(t),Xvo=i(c),or=n(c,"DIV",{class:!0});var xi=s(or);T(BB.$$.fragment,xi),kZr=i(xi),zm=n(xi,"P",{});var V2e=s(zm);$Zr=r(V2e,`This is a generic model class that will be instantiated as one of the model classes of the library (with a sequence-to-sequence language modeling head) when created
with the `),nie=n(V2e,"A",{href:!0});var KBa=s(nie);SZr=r(KBa,"from_pretrained()"),KBa.forEach(t),RZr=r(V2e," class method or the "),sie=n(V2e,"A",{href:!0});var eNa=s(sie);PZr=r(eNa,"from_config()"),eNa.forEach(t),IZr=r(V2e,` class
method.`),V2e.forEach(t),BZr=i(xi),NB=n(xi,"P",{});var EMo=s(NB);NZr=r(EMo,"This class cannot be instantiated directly using "),aSe=n(EMo,"CODE",{});var oNa=s(aSe);DZr=r(oNa,"__init__()"),oNa.forEach(t),GZr=r(EMo," (throws an error)."),EMo.forEach(t),qZr=i(xi),Ot=n(xi,"DIV",{class:!0});var tR=s(Ot);T(DB.$$.fragment,tR),jZr=i(tR),nSe=n(tR,"P",{});var rNa=s(nSe);OZr=r(rNa,"Instantiates one of the model classes of the library (with a sequence-to-sequence language modeling head) from a configuration."),rNa.forEach(t),VZr=i(tR),Wm=n(tR,"P",{});var X2e=s(Wm);XZr=r(X2e,`Note:
Loading a model from its configuration file does `),sSe=n(X2e,"STRONG",{});var tNa=s(sSe);zZr=r(tNa,"not"),tNa.forEach(t),WZr=r(X2e,` load the model weights. It only affects the
model\u2019s configuration. Use `),lie=n(X2e,"A",{href:!0});var aNa=s(lie);QZr=r(aNa,"from_pretrained()"),aNa.forEach(t),UZr=r(X2e," to load the model weights."),X2e.forEach(t),HZr=i(tR),T(k0.$$.fragment,tR),tR.forEach(t),JZr=i(xi),rt=n(xi,"DIV",{class:!0});var ki=s(rt);T(GB.$$.fragment,ki),YZr=i(ki),lSe=n(ki,"P",{});var nNa=s(lSe);ZZr=r(nNa,"Instantiate one of the model classes of the library (with a sequence-to-sequence language modeling head) from a pretrained model."),nNa.forEach(t),KZr=i(ki),$n=n(ki,"P",{});var aR=s($n);eKr=r(aR,"The model class to instantiate is selected based on the "),iSe=n(aR,"CODE",{});var sNa=s(iSe);oKr=r(sNa,"model_type"),sNa.forEach(t),rKr=r(aR,` property of the config object (either
passed as an argument or loaded from `),dSe=n(aR,"CODE",{});var lNa=s(dSe);tKr=r(lNa,"pretrained_model_name_or_path"),lNa.forEach(t),aKr=r(aR,` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),mSe=n(aR,"CODE",{});var iNa=s(mSe);nKr=r(iNa,"pretrained_model_name_or_path"),iNa.forEach(t),sKr=r(aR,":"),aR.forEach(t),lKr=i(ki),Pe=n(ki,"UL",{});var Xe=s(Pe);$0=n(Xe,"LI",{});var Kco=s($0);cSe=n(Kco,"STRONG",{});var dNa=s(cSe);iKr=r(dNa,"bart"),dNa.forEach(t),dKr=r(Kco," \u2014 "),iie=n(Kco,"A",{href:!0});var mNa=s(iie);mKr=r(mNa,"TFBartForConditionalGeneration"),mNa.forEach(t),cKr=r(Kco," (BART model)"),Kco.forEach(t),fKr=i(Xe),S0=n(Xe,"LI",{});var efo=s(S0);fSe=n(efo,"STRONG",{});var cNa=s(fSe);gKr=r(cNa,"blenderbot"),cNa.forEach(t),hKr=r(efo," \u2014 "),die=n(efo,"A",{href:!0});var fNa=s(die);uKr=r(fNa,"TFBlenderbotForConditionalGeneration"),fNa.forEach(t),pKr=r(efo," (Blenderbot model)"),efo.forEach(t),_Kr=i(Xe),R0=n(Xe,"LI",{});var ofo=s(R0);gSe=n(ofo,"STRONG",{});var gNa=s(gSe);bKr=r(gNa,"blenderbot-small"),gNa.forEach(t),vKr=r(ofo," \u2014 "),mie=n(ofo,"A",{href:!0});var hNa=s(mie);TKr=r(hNa,"TFBlenderbotSmallForConditionalGeneration"),hNa.forEach(t),FKr=r(ofo," (BlenderbotSmall model)"),ofo.forEach(t),MKr=i(Xe),P0=n(Xe,"LI",{});var rfo=s(P0);hSe=n(rfo,"STRONG",{});var uNa=s(hSe);EKr=r(uNa,"encoder-decoder"),uNa.forEach(t),CKr=r(rfo," \u2014 "),cie=n(rfo,"A",{href:!0});var pNa=s(cie);wKr=r(pNa,"TFEncoderDecoderModel"),pNa.forEach(t),AKr=r(rfo," (Encoder decoder model)"),rfo.forEach(t),LKr=i(Xe),I0=n(Xe,"LI",{});var tfo=s(I0);uSe=n(tfo,"STRONG",{});var _Na=s(uSe);yKr=r(_Na,"led"),_Na.forEach(t),xKr=r(tfo," \u2014 "),fie=n(tfo,"A",{href:!0});var bNa=s(fie);kKr=r(bNa,"TFLEDForConditionalGeneration"),bNa.forEach(t),$Kr=r(tfo," (LED model)"),tfo.forEach(t),SKr=i(Xe),B0=n(Xe,"LI",{});var afo=s(B0);pSe=n(afo,"STRONG",{});var vNa=s(pSe);RKr=r(vNa,"marian"),vNa.forEach(t),PKr=r(afo," \u2014 "),gie=n(afo,"A",{href:!0});var TNa=s(gie);IKr=r(TNa,"TFMarianMTModel"),TNa.forEach(t),BKr=r(afo," (Marian model)"),afo.forEach(t),NKr=i(Xe),N0=n(Xe,"LI",{});var nfo=s(N0);_Se=n(nfo,"STRONG",{});var FNa=s(_Se);DKr=r(FNa,"mbart"),FNa.forEach(t),GKr=r(nfo," \u2014 "),hie=n(nfo,"A",{href:!0});var MNa=s(hie);qKr=r(MNa,"TFMBartForConditionalGeneration"),MNa.forEach(t),jKr=r(nfo," (mBART model)"),nfo.forEach(t),OKr=i(Xe),D0=n(Xe,"LI",{});var sfo=s(D0);bSe=n(sfo,"STRONG",{});var ENa=s(bSe);VKr=r(ENa,"mt5"),ENa.forEach(t),XKr=r(sfo," \u2014 "),uie=n(sfo,"A",{href:!0});var CNa=s(uie);zKr=r(CNa,"TFMT5ForConditionalGeneration"),CNa.forEach(t),WKr=r(sfo," (MT5 model)"),sfo.forEach(t),QKr=i(Xe),G0=n(Xe,"LI",{});var lfo=s(G0);vSe=n(lfo,"STRONG",{});var wNa=s(vSe);UKr=r(wNa,"pegasus"),wNa.forEach(t),HKr=r(lfo," \u2014 "),pie=n(lfo,"A",{href:!0});var ANa=s(pie);JKr=r(ANa,"TFPegasusForConditionalGeneration"),ANa.forEach(t),YKr=r(lfo," (Pegasus model)"),lfo.forEach(t),ZKr=i(Xe),q0=n(Xe,"LI",{});var ifo=s(q0);TSe=n(ifo,"STRONG",{});var LNa=s(TSe);KKr=r(LNa,"t5"),LNa.forEach(t),eet=r(ifo," \u2014 "),_ie=n(ifo,"A",{href:!0});var yNa=s(_ie);oet=r(yNa,"TFT5ForConditionalGeneration"),yNa.forEach(t),ret=r(ifo," (T5 model)"),ifo.forEach(t),Xe.forEach(t),tet=i(ki),T(j0.$$.fragment,ki),ki.forEach(t),xi.forEach(t),zvo=i(c),Qm=n(c,"H3",{class:!0});var CMo=s(Qm);O0=n(CMo,"A",{id:!0,class:!0,href:!0});var xNa=s(O0);FSe=n(xNa,"SPAN",{});var kNa=s(FSe);T(qB.$$.fragment,kNa),kNa.forEach(t),xNa.forEach(t),aet=i(CMo),MSe=n(CMo,"SPAN",{});var $Na=s(MSe);net=r($Na,"FlaxAutoModelForSeq2SeqLM"),$Na.forEach(t),CMo.forEach(t),Wvo=i(c),rr=n(c,"DIV",{class:!0});var $i=s(rr);T(jB.$$.fragment,$i),set=i($i),Um=n($i,"P",{});var z2e=s(Um);iet=r(z2e,`This is a generic model class that will be instantiated as one of the model classes of the library (with a sequence-to-sequence language modeling head) when created
with the `),bie=n(z2e,"A",{href:!0});var SNa=s(bie);det=r(SNa,"from_pretrained()"),SNa.forEach(t),met=r(z2e," class method or the "),vie=n(z2e,"A",{href:!0});var RNa=s(vie);cet=r(RNa,"from_config()"),RNa.forEach(t),fet=r(z2e,` class
method.`),z2e.forEach(t),get=i($i),OB=n($i,"P",{});var wMo=s(OB);het=r(wMo,"This class cannot be instantiated directly using "),ESe=n(wMo,"CODE",{});var PNa=s(ESe);uet=r(PNa,"__init__()"),PNa.forEach(t),pet=r(wMo," (throws an error)."),wMo.forEach(t),_et=i($i),Vt=n($i,"DIV",{class:!0});var nR=s(Vt);T(VB.$$.fragment,nR),bet=i(nR),CSe=n(nR,"P",{});var INa=s(CSe);vet=r(INa,"Instantiates one of the model classes of the library (with a sequence-to-sequence language modeling head) from a configuration."),INa.forEach(t),Tet=i(nR),Hm=n(nR,"P",{});var W2e=s(Hm);Fet=r(W2e,`Note:
Loading a model from its configuration file does `),wSe=n(W2e,"STRONG",{});var BNa=s(wSe);Met=r(BNa,"not"),BNa.forEach(t),Eet=r(W2e,` load the model weights. It only affects the
model\u2019s configuration. Use `),Tie=n(W2e,"A",{href:!0});var NNa=s(Tie);Cet=r(NNa,"from_pretrained()"),NNa.forEach(t),wet=r(W2e," to load the model weights."),W2e.forEach(t),Aet=i(nR),T(V0.$$.fragment,nR),nR.forEach(t),Let=i($i),tt=n($i,"DIV",{class:!0});var Si=s(tt);T(XB.$$.fragment,Si),yet=i(Si),ASe=n(Si,"P",{});var DNa=s(ASe);xet=r(DNa,"Instantiate one of the model classes of the library (with a sequence-to-sequence language modeling head) from a pretrained model."),DNa.forEach(t),ket=i(Si),Sn=n(Si,"P",{});var sR=s(Sn);$et=r(sR,"The model class to instantiate is selected based on the "),LSe=n(sR,"CODE",{});var GNa=s(LSe);Set=r(GNa,"model_type"),GNa.forEach(t),Ret=r(sR,` property of the config object (either
passed as an argument or loaded from `),ySe=n(sR,"CODE",{});var qNa=s(ySe);Pet=r(qNa,"pretrained_model_name_or_path"),qNa.forEach(t),Iet=r(sR,` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),xSe=n(sR,"CODE",{});var jNa=s(xSe);Bet=r(jNa,"pretrained_model_name_or_path"),jNa.forEach(t),Net=r(sR,":"),sR.forEach(t),Det=i(Si),Ie=n(Si,"UL",{});var ze=s(Ie);X0=n(ze,"LI",{});var dfo=s(X0);kSe=n(dfo,"STRONG",{});var ONa=s(kSe);Get=r(ONa,"bart"),ONa.forEach(t),qet=r(dfo," \u2014 "),Fie=n(dfo,"A",{href:!0});var VNa=s(Fie);jet=r(VNa,"FlaxBartForConditionalGeneration"),VNa.forEach(t),Oet=r(dfo," (BART model)"),dfo.forEach(t),Vet=i(ze),z0=n(ze,"LI",{});var mfo=s(z0);$Se=n(mfo,"STRONG",{});var XNa=s($Se);Xet=r(XNa,"blenderbot"),XNa.forEach(t),zet=r(mfo," \u2014 "),Mie=n(mfo,"A",{href:!0});var zNa=s(Mie);Wet=r(zNa,"FlaxBlenderbotForConditionalGeneration"),zNa.forEach(t),Qet=r(mfo," (Blenderbot model)"),mfo.forEach(t),Uet=i(ze),W0=n(ze,"LI",{});var cfo=s(W0);SSe=n(cfo,"STRONG",{});var WNa=s(SSe);Het=r(WNa,"blenderbot-small"),WNa.forEach(t),Jet=r(cfo," \u2014 "),Eie=n(cfo,"A",{href:!0});var QNa=s(Eie);Yet=r(QNa,"FlaxBlenderbotSmallForConditionalGeneration"),QNa.forEach(t),Zet=r(cfo," (BlenderbotSmall model)"),cfo.forEach(t),Ket=i(ze),Q0=n(ze,"LI",{});var ffo=s(Q0);RSe=n(ffo,"STRONG",{});var UNa=s(RSe);eot=r(UNa,"encoder-decoder"),UNa.forEach(t),oot=r(ffo," \u2014 "),Cie=n(ffo,"A",{href:!0});var HNa=s(Cie);rot=r(HNa,"FlaxEncoderDecoderModel"),HNa.forEach(t),tot=r(ffo," (Encoder decoder model)"),ffo.forEach(t),aot=i(ze),U0=n(ze,"LI",{});var gfo=s(U0);PSe=n(gfo,"STRONG",{});var JNa=s(PSe);not=r(JNa,"longt5"),JNa.forEach(t),sot=r(gfo," \u2014 "),wie=n(gfo,"A",{href:!0});var YNa=s(wie);lot=r(YNa,"FlaxLongT5ForConditionalGeneration"),YNa.forEach(t),iot=r(gfo," (LongT5 model)"),gfo.forEach(t),dot=i(ze),H0=n(ze,"LI",{});var hfo=s(H0);ISe=n(hfo,"STRONG",{});var ZNa=s(ISe);mot=r(ZNa,"marian"),ZNa.forEach(t),cot=r(hfo," \u2014 "),Aie=n(hfo,"A",{href:!0});var KNa=s(Aie);fot=r(KNa,"FlaxMarianMTModel"),KNa.forEach(t),got=r(hfo," (Marian model)"),hfo.forEach(t),hot=i(ze),J0=n(ze,"LI",{});var ufo=s(J0);BSe=n(ufo,"STRONG",{});var eDa=s(BSe);uot=r(eDa,"mbart"),eDa.forEach(t),pot=r(ufo," \u2014 "),Lie=n(ufo,"A",{href:!0});var oDa=s(Lie);_ot=r(oDa,"FlaxMBartForConditionalGeneration"),oDa.forEach(t),bot=r(ufo," (mBART model)"),ufo.forEach(t),vot=i(ze),Y0=n(ze,"LI",{});var pfo=s(Y0);NSe=n(pfo,"STRONG",{});var rDa=s(NSe);Tot=r(rDa,"mt5"),rDa.forEach(t),Fot=r(pfo," \u2014 "),yie=n(pfo,"A",{href:!0});var tDa=s(yie);Mot=r(tDa,"FlaxMT5ForConditionalGeneration"),tDa.forEach(t),Eot=r(pfo," (MT5 model)"),pfo.forEach(t),Cot=i(ze),Z0=n(ze,"LI",{});var _fo=s(Z0);DSe=n(_fo,"STRONG",{});var aDa=s(DSe);wot=r(aDa,"pegasus"),aDa.forEach(t),Aot=r(_fo," \u2014 "),xie=n(_fo,"A",{href:!0});var nDa=s(xie);Lot=r(nDa,"FlaxPegasusForConditionalGeneration"),nDa.forEach(t),yot=r(_fo," (Pegasus model)"),_fo.forEach(t),xot=i(ze),K0=n(ze,"LI",{});var bfo=s(K0);GSe=n(bfo,"STRONG",{});var sDa=s(GSe);kot=r(sDa,"t5"),sDa.forEach(t),$ot=r(bfo," \u2014 "),kie=n(bfo,"A",{href:!0});var lDa=s(kie);Sot=r(lDa,"FlaxT5ForConditionalGeneration"),lDa.forEach(t),Rot=r(bfo," (T5 model)"),bfo.forEach(t),ze.forEach(t),Pot=i(Si),T(ew.$$.fragment,Si),Si.forEach(t),$i.forEach(t),Qvo=i(c),Jm=n(c,"H3",{class:!0});var AMo=s(Jm);ow=n(AMo,"A",{id:!0,class:!0,href:!0});var iDa=s(ow);qSe=n(iDa,"SPAN",{});var dDa=s(qSe);T(zB.$$.fragment,dDa),dDa.forEach(t),iDa.forEach(t),Iot=i(AMo),jSe=n(AMo,"SPAN",{});var mDa=s(jSe);Bot=r(mDa,"AutoModelForSequenceClassification"),mDa.forEach(t),AMo.forEach(t),Uvo=i(c),tr=n(c,"DIV",{class:!0});var Ri=s(tr);T(WB.$$.fragment,Ri),Not=i(Ri),Ym=n(Ri,"P",{});var Q2e=s(Ym);Dot=r(Q2e,`This is a generic model class that will be instantiated as one of the model classes of the library (with a sequence classification head) when created
with the `),$ie=n(Q2e,"A",{href:!0});var cDa=s($ie);Got=r(cDa,"from_pretrained()"),cDa.forEach(t),qot=r(Q2e," class method or the "),Sie=n(Q2e,"A",{href:!0});var fDa=s(Sie);jot=r(fDa,"from_config()"),fDa.forEach(t),Oot=r(Q2e,` class
method.`),Q2e.forEach(t),Vot=i(Ri),QB=n(Ri,"P",{});var LMo=s(QB);Xot=r(LMo,"This class cannot be instantiated directly using "),OSe=n(LMo,"CODE",{});var gDa=s(OSe);zot=r(gDa,"__init__()"),gDa.forEach(t),Wot=r(LMo," (throws an error)."),LMo.forEach(t),Qot=i(Ri),Xt=n(Ri,"DIV",{class:!0});var lR=s(Xt);T(UB.$$.fragment,lR),Uot=i(lR),VSe=n(lR,"P",{});var hDa=s(VSe);Hot=r(hDa,"Instantiates one of the model classes of the library (with a sequence classification head) from a configuration."),hDa.forEach(t),Jot=i(lR),Zm=n(lR,"P",{});var U2e=s(Zm);Yot=r(U2e,`Note:
Loading a model from its configuration file does `),XSe=n(U2e,"STRONG",{});var uDa=s(XSe);Zot=r(uDa,"not"),uDa.forEach(t),Kot=r(U2e,` load the model weights. It only affects the
model\u2019s configuration. Use `),Rie=n(U2e,"A",{href:!0});var pDa=s(Rie);ert=r(pDa,"from_pretrained()"),pDa.forEach(t),ort=r(U2e," to load the model weights."),U2e.forEach(t),rrt=i(lR),T(rw.$$.fragment,lR),lR.forEach(t),trt=i(Ri),fo=n(Ri,"DIV",{class:!0});var ja=s(fo);T(HB.$$.fragment,ja),art=i(ja),zSe=n(ja,"P",{});var _Da=s(zSe);nrt=r(_Da,"Instantiate one of the model classes of the library (with a sequence classification head) from a pretrained model."),_Da.forEach(t),srt=i(ja),Rn=n(ja,"P",{});var iR=s(Rn);lrt=r(iR,"The model class to instantiate is selected based on the "),WSe=n(iR,"CODE",{});var bDa=s(WSe);irt=r(bDa,"model_type"),bDa.forEach(t),drt=r(iR,` property of the config object (either
passed as an argument or loaded from `),QSe=n(iR,"CODE",{});var vDa=s(QSe);mrt=r(vDa,"pretrained_model_name_or_path"),vDa.forEach(t),crt=r(iR,` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),USe=n(iR,"CODE",{});var TDa=s(USe);frt=r(TDa,"pretrained_model_name_or_path"),TDa.forEach(t),grt=r(iR,":"),iR.forEach(t),hrt=i(ja),I=n(ja,"UL",{});var q=s(I);tw=n(q,"LI",{});var vfo=s(tw);HSe=n(vfo,"STRONG",{});var FDa=s(HSe);urt=r(FDa,"albert"),FDa.forEach(t),prt=r(vfo," \u2014 "),Pie=n(vfo,"A",{href:!0});var MDa=s(Pie);_rt=r(MDa,"AlbertForSequenceClassification"),MDa.forEach(t),brt=r(vfo," (ALBERT model)"),vfo.forEach(t),vrt=i(q),aw=n(q,"LI",{});var Tfo=s(aw);JSe=n(Tfo,"STRONG",{});var EDa=s(JSe);Trt=r(EDa,"bart"),EDa.forEach(t),Frt=r(Tfo," \u2014 "),Iie=n(Tfo,"A",{href:!0});var CDa=s(Iie);Mrt=r(CDa,"BartForSequenceClassification"),CDa.forEach(t),Ert=r(Tfo," (BART model)"),Tfo.forEach(t),Crt=i(q),nw=n(q,"LI",{});var Ffo=s(nw);YSe=n(Ffo,"STRONG",{});var wDa=s(YSe);wrt=r(wDa,"bert"),wDa.forEach(t),Art=r(Ffo," \u2014 "),Bie=n(Ffo,"A",{href:!0});var ADa=s(Bie);Lrt=r(ADa,"BertForSequenceClassification"),ADa.forEach(t),yrt=r(Ffo," (BERT model)"),Ffo.forEach(t),xrt=i(q),sw=n(q,"LI",{});var Mfo=s(sw);ZSe=n(Mfo,"STRONG",{});var LDa=s(ZSe);krt=r(LDa,"big_bird"),LDa.forEach(t),$rt=r(Mfo," \u2014 "),Nie=n(Mfo,"A",{href:!0});var yDa=s(Nie);Srt=r(yDa,"BigBirdForSequenceClassification"),yDa.forEach(t),Rrt=r(Mfo," (BigBird model)"),Mfo.forEach(t),Prt=i(q),lw=n(q,"LI",{});var Efo=s(lw);KSe=n(Efo,"STRONG",{});var xDa=s(KSe);Irt=r(xDa,"bigbird_pegasus"),xDa.forEach(t),Brt=r(Efo," \u2014 "),Die=n(Efo,"A",{href:!0});var kDa=s(Die);Nrt=r(kDa,"BigBirdPegasusForSequenceClassification"),kDa.forEach(t),Drt=r(Efo," (BigBird-Pegasus model)"),Efo.forEach(t),Grt=i(q),iw=n(q,"LI",{});var Cfo=s(iw);eRe=n(Cfo,"STRONG",{});var $Da=s(eRe);qrt=r($Da,"bloom"),$Da.forEach(t),jrt=r(Cfo," \u2014 "),Gie=n(Cfo,"A",{href:!0});var SDa=s(Gie);Ort=r(SDa,"BloomForSequenceClassification"),SDa.forEach(t),Vrt=r(Cfo," (BLOOM model)"),Cfo.forEach(t),Xrt=i(q),dw=n(q,"LI",{});var wfo=s(dw);oRe=n(wfo,"STRONG",{});var RDa=s(oRe);zrt=r(RDa,"camembert"),RDa.forEach(t),Wrt=r(wfo," \u2014 "),qie=n(wfo,"A",{href:!0});var PDa=s(qie);Qrt=r(PDa,"CamembertForSequenceClassification"),PDa.forEach(t),Urt=r(wfo," (CamemBERT model)"),wfo.forEach(t),Hrt=i(q),mw=n(q,"LI",{});var Afo=s(mw);rRe=n(Afo,"STRONG",{});var IDa=s(rRe);Jrt=r(IDa,"canine"),IDa.forEach(t),Yrt=r(Afo," \u2014 "),jie=n(Afo,"A",{href:!0});var BDa=s(jie);Zrt=r(BDa,"CanineForSequenceClassification"),BDa.forEach(t),Krt=r(Afo," (CANINE model)"),Afo.forEach(t),ett=i(q),cw=n(q,"LI",{});var Lfo=s(cw);tRe=n(Lfo,"STRONG",{});var NDa=s(tRe);ott=r(NDa,"convbert"),NDa.forEach(t),rtt=r(Lfo," \u2014 "),Oie=n(Lfo,"A",{href:!0});var DDa=s(Oie);ttt=r(DDa,"ConvBertForSequenceClassification"),DDa.forEach(t),att=r(Lfo," (ConvBERT model)"),Lfo.forEach(t),ntt=i(q),fw=n(q,"LI",{});var yfo=s(fw);aRe=n(yfo,"STRONG",{});var GDa=s(aRe);stt=r(GDa,"ctrl"),GDa.forEach(t),ltt=r(yfo," \u2014 "),Vie=n(yfo,"A",{href:!0});var qDa=s(Vie);itt=r(qDa,"CTRLForSequenceClassification"),qDa.forEach(t),dtt=r(yfo," (CTRL model)"),yfo.forEach(t),mtt=i(q),gw=n(q,"LI",{});var xfo=s(gw);nRe=n(xfo,"STRONG",{});var jDa=s(nRe);ctt=r(jDa,"data2vec-text"),jDa.forEach(t),ftt=r(xfo," \u2014 "),Xie=n(xfo,"A",{href:!0});var ODa=s(Xie);gtt=r(ODa,"Data2VecTextForSequenceClassification"),ODa.forEach(t),htt=r(xfo," (Data2VecText model)"),xfo.forEach(t),utt=i(q),hw=n(q,"LI",{});var kfo=s(hw);sRe=n(kfo,"STRONG",{});var VDa=s(sRe);ptt=r(VDa,"deberta"),VDa.forEach(t),_tt=r(kfo," \u2014 "),zie=n(kfo,"A",{href:!0});var XDa=s(zie);btt=r(XDa,"DebertaForSequenceClassification"),XDa.forEach(t),vtt=r(kfo," (DeBERTa model)"),kfo.forEach(t),Ttt=i(q),uw=n(q,"LI",{});var $fo=s(uw);lRe=n($fo,"STRONG",{});var zDa=s(lRe);Ftt=r(zDa,"deberta-v2"),zDa.forEach(t),Mtt=r($fo," \u2014 "),Wie=n($fo,"A",{href:!0});var WDa=s(Wie);Ett=r(WDa,"DebertaV2ForSequenceClassification"),WDa.forEach(t),Ctt=r($fo," (DeBERTa-v2 model)"),$fo.forEach(t),wtt=i(q),pw=n(q,"LI",{});var Sfo=s(pw);iRe=n(Sfo,"STRONG",{});var QDa=s(iRe);Att=r(QDa,"distilbert"),QDa.forEach(t),Ltt=r(Sfo," \u2014 "),Qie=n(Sfo,"A",{href:!0});var UDa=s(Qie);ytt=r(UDa,"DistilBertForSequenceClassification"),UDa.forEach(t),xtt=r(Sfo," (DistilBERT model)"),Sfo.forEach(t),ktt=i(q),_w=n(q,"LI",{});var Rfo=s(_w);dRe=n(Rfo,"STRONG",{});var HDa=s(dRe);$tt=r(HDa,"electra"),HDa.forEach(t),Stt=r(Rfo," \u2014 "),Uie=n(Rfo,"A",{href:!0});var JDa=s(Uie);Rtt=r(JDa,"ElectraForSequenceClassification"),JDa.forEach(t),Ptt=r(Rfo," (ELECTRA model)"),Rfo.forEach(t),Itt=i(q),bw=n(q,"LI",{});var Pfo=s(bw);mRe=n(Pfo,"STRONG",{});var YDa=s(mRe);Btt=r(YDa,"ernie"),YDa.forEach(t),Ntt=r(Pfo," \u2014 "),Hie=n(Pfo,"A",{href:!0});var ZDa=s(Hie);Dtt=r(ZDa,"ErnieForSequenceClassification"),ZDa.forEach(t),Gtt=r(Pfo," (ERNIE model)"),Pfo.forEach(t),qtt=i(q),vw=n(q,"LI",{});var Ifo=s(vw);cRe=n(Ifo,"STRONG",{});var KDa=s(cRe);jtt=r(KDa,"esm"),KDa.forEach(t),Ott=r(Ifo," \u2014 "),Jie=n(Ifo,"A",{href:!0});var eGa=s(Jie);Vtt=r(eGa,"EsmForSequenceClassification"),eGa.forEach(t),Xtt=r(Ifo," (ESM model)"),Ifo.forEach(t),ztt=i(q),Tw=n(q,"LI",{});var Bfo=s(Tw);fRe=n(Bfo,"STRONG",{});var oGa=s(fRe);Wtt=r(oGa,"flaubert"),oGa.forEach(t),Qtt=r(Bfo," \u2014 "),Yie=n(Bfo,"A",{href:!0});var rGa=s(Yie);Utt=r(rGa,"FlaubertForSequenceClassification"),rGa.forEach(t),Htt=r(Bfo," (FlauBERT model)"),Bfo.forEach(t),Jtt=i(q),Fw=n(q,"LI",{});var Nfo=s(Fw);gRe=n(Nfo,"STRONG",{});var tGa=s(gRe);Ytt=r(tGa,"fnet"),tGa.forEach(t),Ztt=r(Nfo," \u2014 "),Zie=n(Nfo,"A",{href:!0});var aGa=s(Zie);Ktt=r(aGa,"FNetForSequenceClassification"),aGa.forEach(t),eat=r(Nfo," (FNet model)"),Nfo.forEach(t),oat=i(q),Mw=n(q,"LI",{});var Dfo=s(Mw);hRe=n(Dfo,"STRONG",{});var nGa=s(hRe);rat=r(nGa,"funnel"),nGa.forEach(t),tat=r(Dfo," \u2014 "),Kie=n(Dfo,"A",{href:!0});var sGa=s(Kie);aat=r(sGa,"FunnelForSequenceClassification"),sGa.forEach(t),nat=r(Dfo," (Funnel Transformer model)"),Dfo.forEach(t),sat=i(q),Ew=n(q,"LI",{});var Gfo=s(Ew);uRe=n(Gfo,"STRONG",{});var lGa=s(uRe);lat=r(lGa,"gpt-sw3"),lGa.forEach(t),iat=r(Gfo," \u2014 "),ede=n(Gfo,"A",{href:!0});var iGa=s(ede);dat=r(iGa,"GPT2ForSequenceClassification"),iGa.forEach(t),mat=r(Gfo," (GPT-Sw3 model)"),Gfo.forEach(t),cat=i(q),Cw=n(q,"LI",{});var qfo=s(Cw);pRe=n(qfo,"STRONG",{});var dGa=s(pRe);fat=r(dGa,"gpt2"),dGa.forEach(t),gat=r(qfo," \u2014 "),ode=n(qfo,"A",{href:!0});var mGa=s(ode);hat=r(mGa,"GPT2ForSequenceClassification"),mGa.forEach(t),uat=r(qfo," (OpenAI GPT-2 model)"),qfo.forEach(t),pat=i(q),ww=n(q,"LI",{});var jfo=s(ww);_Re=n(jfo,"STRONG",{});var cGa=s(_Re);_at=r(cGa,"gpt_neo"),cGa.forEach(t),bat=r(jfo," \u2014 "),rde=n(jfo,"A",{href:!0});var fGa=s(rde);vat=r(fGa,"GPTNeoForSequenceClassification"),fGa.forEach(t),Tat=r(jfo," (GPT Neo model)"),jfo.forEach(t),Fat=i(q),Aw=n(q,"LI",{});var Ofo=s(Aw);bRe=n(Ofo,"STRONG",{});var gGa=s(bRe);Mat=r(gGa,"gptj"),gGa.forEach(t),Eat=r(Ofo," \u2014 "),tde=n(Ofo,"A",{href:!0});var hGa=s(tde);Cat=r(hGa,"GPTJForSequenceClassification"),hGa.forEach(t),wat=r(Ofo," (GPT-J model)"),Ofo.forEach(t),Aat=i(q),Lw=n(q,"LI",{});var Vfo=s(Lw);vRe=n(Vfo,"STRONG",{});var uGa=s(vRe);Lat=r(uGa,"ibert"),uGa.forEach(t),yat=r(Vfo," \u2014 "),ade=n(Vfo,"A",{href:!0});var pGa=s(ade);xat=r(pGa,"IBertForSequenceClassification"),pGa.forEach(t),kat=r(Vfo," (I-BERT model)"),Vfo.forEach(t),$at=i(q),yw=n(q,"LI",{});var Xfo=s(yw);TRe=n(Xfo,"STRONG",{});var _Ga=s(TRe);Sat=r(_Ga,"layoutlm"),_Ga.forEach(t),Rat=r(Xfo," \u2014 "),nde=n(Xfo,"A",{href:!0});var bGa=s(nde);Pat=r(bGa,"LayoutLMForSequenceClassification"),bGa.forEach(t),Iat=r(Xfo," (LayoutLM model)"),Xfo.forEach(t),Bat=i(q),xw=n(q,"LI",{});var zfo=s(xw);FRe=n(zfo,"STRONG",{});var vGa=s(FRe);Nat=r(vGa,"layoutlmv2"),vGa.forEach(t),Dat=r(zfo," \u2014 "),sde=n(zfo,"A",{href:!0});var TGa=s(sde);Gat=r(TGa,"LayoutLMv2ForSequenceClassification"),TGa.forEach(t),qat=r(zfo," (LayoutLMv2 model)"),zfo.forEach(t),jat=i(q),kw=n(q,"LI",{});var Wfo=s(kw);MRe=n(Wfo,"STRONG",{});var FGa=s(MRe);Oat=r(FGa,"layoutlmv3"),FGa.forEach(t),Vat=r(Wfo," \u2014 "),lde=n(Wfo,"A",{href:!0});var MGa=s(lde);Xat=r(MGa,"LayoutLMv3ForSequenceClassification"),MGa.forEach(t),zat=r(Wfo," (LayoutLMv3 model)"),Wfo.forEach(t),Wat=i(q),$w=n(q,"LI",{});var Qfo=s($w);ERe=n(Qfo,"STRONG",{});var EGa=s(ERe);Qat=r(EGa,"led"),EGa.forEach(t),Uat=r(Qfo," \u2014 "),ide=n(Qfo,"A",{href:!0});var CGa=s(ide);Hat=r(CGa,"LEDForSequenceClassification"),CGa.forEach(t),Jat=r(Qfo," (LED model)"),Qfo.forEach(t),Yat=i(q),Sw=n(q,"LI",{});var Ufo=s(Sw);CRe=n(Ufo,"STRONG",{});var wGa=s(CRe);Zat=r(wGa,"lilt"),wGa.forEach(t),Kat=r(Ufo," \u2014 "),dde=n(Ufo,"A",{href:!0});var AGa=s(dde);ent=r(AGa,"LiltForSequenceClassification"),AGa.forEach(t),ont=r(Ufo," (LiLT model)"),Ufo.forEach(t),rnt=i(q),Rw=n(q,"LI",{});var Hfo=s(Rw);wRe=n(Hfo,"STRONG",{});var LGa=s(wRe);tnt=r(LGa,"longformer"),LGa.forEach(t),ant=r(Hfo," \u2014 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jGa=s(SRe);Dnt=r(jGa,"mvp"),jGa.forEach(t),Gnt=r(rgo," \u2014 "),_de=n(rgo,"A",{href:!0});var OGa=s(_de);qnt=r(OGa,"MvpForSequenceClassification"),OGa.forEach(t),jnt=r(rgo," (MVP model)"),rgo.forEach(t),Ont=i(q),jw=n(q,"LI",{});var tgo=s(jw);RRe=n(tgo,"STRONG",{});var VGa=s(RRe);Vnt=r(VGa,"nezha"),VGa.forEach(t),Xnt=r(tgo," \u2014 "),bde=n(tgo,"A",{href:!0});var XGa=s(bde);znt=r(XGa,"NezhaForSequenceClassification"),XGa.forEach(t),Wnt=r(tgo," (Nezha model)"),tgo.forEach(t),Qnt=i(q),Ow=n(q,"LI",{});var ago=s(Ow);PRe=n(ago,"STRONG",{});var zGa=s(PRe);Unt=r(zGa,"nystromformer"),zGa.forEach(t),Hnt=r(ago," \u2014 "),vde=n(ago,"A",{href:!0});var WGa=s(vde);Jnt=r(WGa,"NystromformerForSequenceClassification"),WGa.forEach(t),Ynt=r(ago," (Nystr\xF6mformer model)"),ago.forEach(t),Znt=i(q),Vw=n(q,"LI",{});var ngo=s(Vw);IRe=n(ngo,"STRONG",{});var QGa=s(IRe);Knt=r(QGa,"openai-gpt"),QGa.forEach(t),est=r(ngo," \u2014 "),Tde=n(ngo,"A",{href:!0});var UGa=s(Tde);ost=r(UGa,"OpenAIGPTForSequenceClassification"),UGa.forEach(t),rst=r(ngo," (OpenAI GPT model)"),ngo.forEach(t),tst=i(q),Xw=n(q,"LI",{});var sgo=s(Xw);BRe=n(sgo,"STRONG",{});var HGa=s(BRe);ast=r(HGa,"opt"),HGa.forEach(t),nst=r(sgo," \u2014 "),Fde=n(sgo,"A",{href:!0});var JGa=s(Fde);sst=r(JGa,"OPTForSequenceClassification"),JGa.forEach(t),lst=r(sgo," (OPT model)"),sgo.forEach(t),ist=i(q),zw=n(q,"LI",{});var lgo=s(zw);NRe=n(lgo,"STRONG",{});var YGa=s(NRe);dst=r(YGa,"perceiver"),YGa.forEach(t),mst=r(lgo," \u2014 "),Mde=n(lgo,"A",{href:!0});var ZGa=s(Mde);cst=r(ZGa,"PerceiverForSequenceClassification"),ZGa.forEach(t),fst=r(lgo," (Perceiver model)"),lgo.forEach(t),gst=i(q),Ww=n(q,"LI",{});var igo=s(Ww);DRe=n(igo,"STRONG",{});var KGa=s(DRe);hst=r(KGa,"plbart"),KGa.forEach(t),ust=r(igo," \u2014 "),Ede=n(igo,"A",{href:!0});var eqa=s(Ede);pst=r(eqa,"PLBartForSequenceClassification"),eqa.forEach(t),_st=r(igo," (PLBart model)"),igo.forEach(t),bst=i(q),Qw=n(q,"LI",{});var 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yqa=s(Nde);Slt=r(yqa,"XLNetForSequenceClassification"),yqa.forEach(t),Rlt=r(Mgo," (XLNet model)"),Mgo.forEach(t),Plt=i(q),l6=n(q,"LI",{});var Ego=s(l6);KRe=n(Ego,"STRONG",{});var xqa=s(KRe);Ilt=r(xqa,"yoso"),xqa.forEach(t),Blt=r(Ego," \u2014 "),Dde=n(Ego,"A",{href:!0});var kqa=s(Dde);Nlt=r(kqa,"YosoForSequenceClassification"),kqa.forEach(t),Dlt=r(Ego," (YOSO model)"),Ego.forEach(t),q.forEach(t),Glt=i(ja),i6=n(ja,"P",{});var Cgo=s(i6);qlt=r(Cgo,"The model is set in evaluation mode by default using "),ePe=n(Cgo,"CODE",{});var $qa=s(ePe);jlt=r($qa,"model.eval()"),$qa.forEach(t),Olt=r(Cgo,` (so for instance, dropout modules are
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with the `),Gde=n(H2e,"A",{href:!0});var Bqa=s(Gde);Hlt=r(Bqa,"from_pretrained()"),Bqa.forEach(t),Jlt=r(H2e," class method or the "),qde=n(H2e,"A",{href:!0});var Nqa=s(qde);Ylt=r(Nqa,"from_config()"),Nqa.forEach(t),Zlt=r(H2e,` class
method.`),H2e.forEach(t),Klt=i(Pi),ZB=n(Pi,"P",{});var xMo=s(ZB);eit=r(xMo,"This class cannot be instantiated directly using "),aPe=n(xMo,"CODE",{});var Dqa=s(aPe);oit=r(Dqa,"__init__()"),Dqa.forEach(t),rit=r(xMo," (throws an error)."),xMo.forEach(t),tit=i(Pi),zt=n(Pi,"DIV",{class:!0});var dR=s(zt);T(KB.$$.fragment,dR),ait=i(dR),nPe=n(dR,"P",{});var Gqa=s(nPe);nit=r(Gqa,"Instantiates one of the model classes of the library (with a sequence classification head) from a configuration."),Gqa.forEach(t),sit=i(dR),oc=n(dR,"P",{});var J2e=s(oc);lit=r(J2e,`Note:
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aja=s(_Pe);odt=r(aja,"deberta"),aja.forEach(t),rdt=r($go," \u2014 "),Ude=n($go,"A",{href:!0});var nja=s(Ude);tdt=r(nja,"TFDebertaForSequenceClassification"),nja.forEach(t),adt=r($go," (DeBERTa model)"),$go.forEach(t),ndt=i(se),v6=n(se,"LI",{});var Sgo=s(v6);bPe=n(Sgo,"STRONG",{});var sja=s(bPe);sdt=r(sja,"deberta-v2"),sja.forEach(t),ldt=r(Sgo," \u2014 "),Hde=n(Sgo,"A",{href:!0});var lja=s(Hde);idt=r(lja,"TFDebertaV2ForSequenceClassification"),lja.forEach(t),ddt=r(Sgo," (DeBERTa-v2 model)"),Sgo.forEach(t),mdt=i(se),T6=n(se,"LI",{});var Rgo=s(T6);vPe=n(Rgo,"STRONG",{});var ija=s(vPe);cdt=r(ija,"distilbert"),ija.forEach(t),fdt=r(Rgo," \u2014 "),Jde=n(Rgo,"A",{href:!0});var dja=s(Jde);gdt=r(dja,"TFDistilBertForSequenceClassification"),dja.forEach(t),hdt=r(Rgo," (DistilBERT model)"),Rgo.forEach(t),udt=i(se),F6=n(se,"LI",{});var Pgo=s(F6);TPe=n(Pgo,"STRONG",{});var mja=s(TPe);pdt=r(mja,"electra"),mja.forEach(t),_dt=r(Pgo," \u2014 "),Yde=n(Pgo,"A",{href:!0});var cja=s(Yde);bdt=r(cja,"TFElectraForSequenceClassification"),cja.forEach(t),vdt=r(Pgo," (ELECTRA model)"),Pgo.forEach(t),Tdt=i(se),M6=n(se,"LI",{});var Igo=s(M6);FPe=n(Igo,"STRONG",{});var fja=s(FPe);Fdt=r(fja,"esm"),fja.forEach(t),Mdt=r(Igo," \u2014 "),Zde=n(Igo,"A",{href:!0});var gja=s(Zde);Edt=r(gja,"TFEsmForSequenceClassification"),gja.forEach(t),Cdt=r(Igo," (ESM model)"),Igo.forEach(t),wdt=i(se),E6=n(se,"LI",{});var Bgo=s(E6);MPe=n(Bgo,"STRONG",{});var hja=s(MPe);Adt=r(hja,"flaubert"),hja.forEach(t),Ldt=r(Bgo," \u2014 "),Kde=n(Bgo,"A",{href:!0});var uja=s(Kde);ydt=r(uja,"TFFlaubertForSequenceClassification"),uja.forEach(t),xdt=r(Bgo," (FlauBERT model)"),Bgo.forEach(t),kdt=i(se),C6=n(se,"LI",{});var Ngo=s(C6);EPe=n(Ngo,"STRONG",{});var pja=s(EPe);$dt=r(pja,"funnel"),pja.forEach(t),Sdt=r(Ngo," \u2014 "),eme=n(Ngo,"A",{href:!0});var _ja=s(eme);Rdt=r(_ja,"TFFunnelForSequenceClassification"),_ja.forEach(t),Pdt=r(Ngo," (Funnel Transformer 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$ja=s(lme);hmt=r($ja,"TFMobileBertForSequenceClassification"),$ja.forEach(t),umt=r(Xgo," (MobileBERT model)"),Xgo.forEach(t),pmt=i(se),S6=n(se,"LI",{});var zgo=s(S6);$Pe=n(zgo,"STRONG",{});var Sja=s($Pe);_mt=r(Sja,"mpnet"),Sja.forEach(t),bmt=r(zgo," \u2014 "),ime=n(zgo,"A",{href:!0});var Rja=s(ime);vmt=r(Rja,"TFMPNetForSequenceClassification"),Rja.forEach(t),Tmt=r(zgo," (MPNet model)"),zgo.forEach(t),Fmt=i(se),R6=n(se,"LI",{});var Wgo=s(R6);SPe=n(Wgo,"STRONG",{});var Pja=s(SPe);Mmt=r(Pja,"openai-gpt"),Pja.forEach(t),Emt=r(Wgo," \u2014 "),dme=n(Wgo,"A",{href:!0});var Ija=s(dme);Cmt=r(Ija,"TFOpenAIGPTForSequenceClassification"),Ija.forEach(t),wmt=r(Wgo," (OpenAI GPT model)"),Wgo.forEach(t),Amt=i(se),P6=n(se,"LI",{});var Qgo=s(P6);RPe=n(Qgo,"STRONG",{});var Bja=s(RPe);Lmt=r(Bja,"rembert"),Bja.forEach(t),ymt=r(Qgo," \u2014 "),mme=n(Qgo,"A",{href:!0});var Nja=s(mme);xmt=r(Nja,"TFRemBertForSequenceClassification"),Nja.forEach(t),kmt=r(Qgo," (RemBERT 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Xja=s(NPe);Qmt=r(Xja,"tapas"),Xja.forEach(t),Umt=r(Ygo," \u2014 "),hme=n(Ygo,"A",{href:!0});var zja=s(hme);Hmt=r(zja,"TFTapasForSequenceClassification"),zja.forEach(t),Jmt=r(Ygo," (TAPAS model)"),Ygo.forEach(t),Ymt=i(se),G6=n(se,"LI",{});var Zgo=s(G6);DPe=n(Zgo,"STRONG",{});var Wja=s(DPe);Zmt=r(Wja,"transfo-xl"),Wja.forEach(t),Kmt=r(Zgo," \u2014 "),ume=n(Zgo,"A",{href:!0});var Qja=s(ume);ect=r(Qja,"TFTransfoXLForSequenceClassification"),Qja.forEach(t),oct=r(Zgo," (Transformer-XL model)"),Zgo.forEach(t),rct=i(se),q6=n(se,"LI",{});var Kgo=s(q6);GPe=n(Kgo,"STRONG",{});var Uja=s(GPe);tct=r(Uja,"xlm"),Uja.forEach(t),act=r(Kgo," \u2014 "),pme=n(Kgo,"A",{href:!0});var Hja=s(pme);nct=r(Hja,"TFXLMForSequenceClassification"),Hja.forEach(t),sct=r(Kgo," (XLM model)"),Kgo.forEach(t),lct=i(se),j6=n(se,"LI",{});var eho=s(j6);qPe=n(eho,"STRONG",{});var Jja=s(qPe);ict=r(Jja,"xlm-roberta"),Jja.forEach(t),dct=r(eho," \u2014 "),_me=n(eho,"A",{href:!0});var Yja=s(_me);mct=r(Yja,"TFXLMRobertaForSequenceClassification"),Yja.forEach(t),cct=r(eho," (XLM-RoBERTa model)"),eho.forEach(t),fct=i(se),O6=n(se,"LI",{});var oho=s(O6);jPe=n(oho,"STRONG",{});var Zja=s(jPe);gct=r(Zja,"xlnet"),Zja.forEach(t),hct=r(oho," \u2014 "),bme=n(oho,"A",{href:!0});var Kja=s(bme);uct=r(Kja,"TFXLNetForSequenceClassification"),Kja.forEach(t),pct=r(oho," (XLNet model)"),oho.forEach(t),se.forEach(t),_ct=i(Ii),T(V6.$$.fragment,Ii),Ii.forEach(t),Pi.forEach(t),Yvo=i(c),rc=n(c,"H3",{class:!0});var kMo=s(rc);X6=n(kMo,"A",{id:!0,class:!0,href:!0});var eOa=s(X6);OPe=n(eOa,"SPAN",{});var oOa=s(OPe);T(oN.$$.fragment,oOa),oOa.forEach(t),eOa.forEach(t),bct=i(kMo),VPe=n(kMo,"SPAN",{});var rOa=s(VPe);vct=r(rOa,"FlaxAutoModelForSequenceClassification"),rOa.forEach(t),kMo.forEach(t),Zvo=i(c),nr=n(c,"DIV",{class:!0});var Bi=s(nr);T(rN.$$.fragment,Bi),Tct=i(Bi),tc=n(Bi,"P",{});var Y2e=s(tc);Fct=r(Y2e,`This is a generic model class that will be instantiated as one of the model classes of 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with the `),vme=n(Y2e,"A",{href:!0});var tOa=s(vme);Mct=r(tOa,"from_pretrained()"),tOa.forEach(t),Ect=r(Y2e," class method or the "),Tme=n(Y2e,"A",{href:!0});var aOa=s(Tme);Cct=r(aOa,"from_config()"),aOa.forEach(t),wct=r(Y2e,` class
method.`),Y2e.forEach(t),Act=i(Bi),tN=n(Bi,"P",{});var $Mo=s(tN);Lct=r($Mo,"This class cannot be instantiated directly using "),XPe=n($Mo,"CODE",{});var nOa=s(XPe);yct=r(nOa,"__init__()"),nOa.forEach(t),xct=r($Mo," (throws an error)."),$Mo.forEach(t),kct=i(Bi),Wt=n(Bi,"DIV",{class:!0});var cR=s(Wt);T(aN.$$.fragment,cR),$ct=i(cR),zPe=n(cR,"P",{});var sOa=s(zPe);Sct=r(sOa,"Instantiates one of the model classes of the library (with a sequence classification head) from a configuration."),sOa.forEach(t),Rct=i(cR),ac=n(cR,"P",{});var Z2e=s(ac);Pct=r(Z2e,`Note:
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model\u2019s configuration. Use `),Fme=n(Z2e,"A",{href:!0});var iOa=s(Fme);Nct=r(iOa,"from_pretrained()"),iOa.forEach(t),Dct=r(Z2e," to load the model weights."),Z2e.forEach(t),Gct=i(cR),T(z6.$$.fragment,cR),cR.forEach(t),qct=i(Bi),nt=n(Bi,"DIV",{class:!0});var Ni=s(nt);T(nN.$$.fragment,Ni),jct=i(Ni),QPe=n(Ni,"P",{});var dOa=s(QPe);Oct=r(dOa,"Instantiate one of the model classes of the library (with a sequence classification head) from a pretrained model."),dOa.forEach(t),Vct=i(Ni),In=n(Ni,"P",{});var fR=s(In);Xct=r(fR,"The model class to instantiate is selected based on the "),UPe=n(fR,"CODE",{});var mOa=s(UPe);zct=r(mOa,"model_type"),mOa.forEach(t),Wct=r(fR,` property of the config object (either
passed as an argument or loaded from `),HPe=n(fR,"CODE",{});var cOa=s(HPe);Qct=r(cOa,"pretrained_model_name_or_path"),cOa.forEach(t),Uct=r(fR,` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),JPe=n(fR,"CODE",{});var fOa=s(JPe);Hct=r(fOa,"pretrained_model_name_or_path"),fOa.forEach(t),Jct=r(fR,":"),fR.forEach(t),Yct=i(Ni),$e=n(Ni,"UL",{});var qe=s($e);W6=n(qe,"LI",{});var rho=s(W6);YPe=n(rho,"STRONG",{});var gOa=s(YPe);Zct=r(gOa,"albert"),gOa.forEach(t),Kct=r(rho," \u2014 "),Mme=n(rho,"A",{href:!0});var hOa=s(Mme);eft=r(hOa,"FlaxAlbertForSequenceClassification"),hOa.forEach(t),oft=r(rho," (ALBERT model)"),rho.forEach(t),rft=i(qe),Q6=n(qe,"LI",{});var tho=s(Q6);ZPe=n(tho,"STRONG",{});var uOa=s(ZPe);tft=r(uOa,"bart"),uOa.forEach(t),aft=r(tho," \u2014 "),Eme=n(tho,"A",{href:!0});var pOa=s(Eme);nft=r(pOa,"FlaxBartForSequenceClassification"),pOa.forEach(t),sft=r(tho," (BART model)"),tho.forEach(t),lft=i(qe),U6=n(qe,"LI",{});var aho=s(U6);KPe=n(aho,"STRONG",{});var _Oa=s(KPe);ift=r(_Oa,"bert"),_Oa.forEach(t),dft=r(aho," \u2014 "),Cme=n(aho,"A",{href:!0});var bOa=s(Cme);mft=r(bOa,"FlaxBertForSequenceClassification"),bOa.forEach(t),cft=r(aho," (BERT model)"),aho.forEach(t),fft=i(qe),H6=n(qe,"LI",{});var nho=s(H6);eIe=n(nho,"STRONG",{});var vOa=s(eIe);gft=r(vOa,"big_bird"),vOa.forEach(t),hft=r(nho," \u2014 "),wme=n(nho,"A",{href:!0});var TOa=s(wme);uft=r(TOa,"FlaxBigBirdForSequenceClassification"),TOa.forEach(t),pft=r(nho," (BigBird model)"),nho.forEach(t),_ft=i(qe),J6=n(qe,"LI",{});var sho=s(J6);oIe=n(sho,"STRONG",{});var FOa=s(oIe);bft=r(FOa,"distilbert"),FOa.forEach(t),vft=r(sho," \u2014 "),Ame=n(sho,"A",{href:!0});var MOa=s(Ame);Tft=r(MOa,"FlaxDistilBertForSequenceClassification"),MOa.forEach(t),Fft=r(sho," (DistilBERT model)"),sho.forEach(t),Mft=i(qe),Y6=n(qe,"LI",{});var lho=s(Y6);rIe=n(lho,"STRONG",{});var EOa=s(rIe);Eft=r(EOa,"electra"),EOa.forEach(t),Cft=r(lho," \u2014 "),Lme=n(lho,"A",{href:!0});var COa=s(Lme);wft=r(COa,"FlaxElectraForSequenceClassification"),COa.forEach(t),Aft=r(lho," (ELECTRA model)"),lho.forEach(t),Lft=i(qe),Z6=n(qe,"LI",{});var iho=s(Z6);tIe=n(iho,"STRONG",{});var wOa=s(tIe);yft=r(wOa,"mbart"),wOa.forEach(t),xft=r(iho," \u2014 "),yme=n(iho,"A",{href:!0});var AOa=s(yme);kft=r(AOa,"FlaxMBartForSequenceClassification"),AOa.forEach(t),$ft=r(iho," (mBART model)"),iho.forEach(t),Sft=i(qe),K6=n(qe,"LI",{});var dho=s(K6);aIe=n(dho,"STRONG",{});var LOa=s(aIe);Rft=r(LOa,"roberta"),LOa.forEach(t),Pft=r(dho," \u2014 "),xme=n(dho,"A",{href:!0});var yOa=s(xme);Ift=r(yOa,"FlaxRobertaForSequenceClassification"),yOa.forEach(t),Bft=r(dho," (RoBERTa model)"),dho.forEach(t),Nft=i(qe),e8=n(qe,"LI",{});var mho=s(e8);nIe=n(mho,"STRONG",{});var xOa=s(nIe);Dft=r(xOa,"roberta-prelayernorm"),xOa.forEach(t),Gft=r(mho," \u2014 "),kme=n(mho,"A",{href:!0});var kOa=s(kme);qft=r(kOa,"FlaxRobertaPreLayerNormForSequenceClassification"),kOa.forEach(t),jft=r(mho," (RoBERTa-PreLayerNorm model)"),mho.forEach(t),Oft=i(qe),o8=n(qe,"LI",{});var cho=s(o8);sIe=n(cho,"STRONG",{});var $Oa=s(sIe);Vft=r($Oa,"roformer"),$Oa.forEach(t),Xft=r(cho," \u2014 "),$me=n(cho,"A",{href:!0});var SOa=s($me);zft=r(SOa,"FlaxRoFormerForSequenceClassification"),SOa.forEach(t),Wft=r(cho," (RoFormer model)"),cho.forEach(t),Qft=i(qe),r8=n(qe,"LI",{});var fho=s(r8);lIe=n(fho,"STRONG",{});var ROa=s(lIe);Uft=r(ROa,"xlm-roberta"),ROa.forEach(t),Hft=r(fho," \u2014 "),Sme=n(fho,"A",{href:!0});var POa=s(Sme);Jft=r(POa,"FlaxXLMRobertaForSequenceClassification"),POa.forEach(t),Yft=r(fho," (XLM-RoBERTa model)"),fho.forEach(t),qe.forEach(t),Zft=i(Ni),T(t8.$$.fragment,Ni),Ni.forEach(t),Bi.forEach(t),Kvo=i(c),nc=n(c,"H3",{class:!0});var SMo=s(nc);a8=n(SMo,"A",{id:!0,class:!0,href:!0});var IOa=s(a8);iIe=n(IOa,"SPAN",{});var BOa=s(iIe);T(sN.$$.fragment,BOa),BOa.forEach(t),IOa.forEach(t),Kft=i(SMo),dIe=n(SMo,"SPAN",{});var NOa=s(dIe);egt=r(NOa,"AutoModelForMultipleChoice"),NOa.forEach(t),SMo.forEach(t),eTo=i(c),sr=n(c,"DIV",{class:!0});var Di=s(sr);T(lN.$$.fragment,Di),ogt=i(Di),sc=n(Di,"P",{});var K2e=s(sc);rgt=r(K2e,`This is a generic model class that will be instantiated as one of the model classes of the library (with a multiple choice head) when created
with the `),Rme=n(K2e,"A",{href:!0});var DOa=s(Rme);tgt=r(DOa,"from_pretrained()"),DOa.forEach(t),agt=r(K2e," class method or the "),Pme=n(K2e,"A",{href:!0});var GOa=s(Pme);ngt=r(GOa,"from_config()"),GOa.forEach(t),sgt=r(K2e,` class
method.`),K2e.forEach(t),lgt=i(Di),iN=n(Di,"P",{});var RMo=s(iN);igt=r(RMo,"This class cannot be instantiated directly using "),mIe=n(RMo,"CODE",{});var qOa=s(mIe);dgt=r(qOa,"__init__()"),qOa.forEach(t),mgt=r(RMo," (throws an error)."),RMo.forEach(t),cgt=i(Di),Qt=n(Di,"DIV",{class:!0});var gR=s(Qt);T(dN.$$.fragment,gR),fgt=i(gR),cIe=n(gR,"P",{});var jOa=s(cIe);ggt=r(jOa,"Instantiates one of the model classes of the library (with a multiple choice head) from a configuration."),jOa.forEach(t),hgt=i(gR),lc=n(gR,"P",{});var ebe=s(lc);ugt=r(ebe,`Note:
Loading a model from its configuration file does `),fIe=n(ebe,"STRONG",{});var OOa=s(fIe);pgt=r(OOa,"not"),OOa.forEach(t),_gt=r(ebe,` load the model weights. It only affects the
model\u2019s configuration. Use `),Ime=n(ebe,"A",{href:!0});var VOa=s(Ime);bgt=r(VOa,"from_pretrained()"),VOa.forEach(t),vgt=r(ebe," to load the model weights."),ebe.forEach(t),Tgt=i(gR),T(n8.$$.fragment,gR),gR.forEach(t),Fgt=i(Di),go=n(Di,"DIV",{class:!0});var Oa=s(go);T(mN.$$.fragment,Oa),Mgt=i(Oa),gIe=n(Oa,"P",{});var XOa=s(gIe);Egt=r(XOa,"Instantiate one of the model classes of the library (with a multiple choice head) from a pretrained model."),XOa.forEach(t),Cgt=i(Oa),Bn=n(Oa,"P",{});var hR=s(Bn);wgt=r(hR,"The model class to instantiate is selected based on the "),hIe=n(hR,"CODE",{});var zOa=s(hIe);Agt=r(zOa,"model_type"),zOa.forEach(t),Lgt=r(hR,` property of the config object (either
passed as an argument or loaded from `),uIe=n(hR,"CODE",{});var WOa=s(uIe);ygt=r(WOa,"pretrained_model_name_or_path"),WOa.forEach(t),xgt=r(hR,` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),pIe=n(hR,"CODE",{});var QOa=s(pIe);kgt=r(QOa,"pretrained_model_name_or_path"),QOa.forEach(t),$gt=r(hR,":"),hR.forEach(t),Sgt=i(Oa),oe=n(Oa,"UL",{});var te=s(oe);s8=n(te,"LI",{});var gho=s(s8);_Ie=n(gho,"STRONG",{});var UOa=s(_Ie);Rgt=r(UOa,"albert"),UOa.forEach(t),Pgt=r(gho," \u2014 "),Bme=n(gho,"A",{href:!0});var HOa=s(Bme);Igt=r(HOa,"AlbertForMultipleChoice"),HOa.forEach(t),Bgt=r(gho," (ALBERT model)"),gho.forEach(t),Ngt=i(te),l8=n(te,"LI",{});var hho=s(l8);bIe=n(hho,"STRONG",{});var JOa=s(bIe);Dgt=r(JOa,"bert"),JOa.forEach(t),Ggt=r(hho," \u2014 "),Nme=n(hho,"A",{href:!0});var YOa=s(Nme);qgt=r(YOa,"BertForMultipleChoice"),YOa.forEach(t),jgt=r(hho," (BERT model)"),hho.forEach(t),Ogt=i(te),i8=n(te,"LI",{});var uho=s(i8);vIe=n(uho,"STRONG",{});var ZOa=s(vIe);Vgt=r(ZOa,"big_bird"),ZOa.forEach(t),Xgt=r(uho," \u2014 "),Dme=n(uho,"A",{href:!0});var KOa=s(Dme);zgt=r(KOa,"BigBirdForMultipleChoice"),KOa.forEach(t),Wgt=r(uho," (BigBird model)"),uho.forEach(t),Qgt=i(te),d8=n(te,"LI",{});var pho=s(d8);TIe=n(pho,"STRONG",{});var eVa=s(TIe);Ugt=r(eVa,"camembert"),eVa.forEach(t),Hgt=r(pho," \u2014 "),Gme=n(pho,"A",{href:!0});var oVa=s(Gme);Jgt=r(oVa,"CamembertForMultipleChoice"),oVa.forEach(t),Ygt=r(pho," (CamemBERT model)"),pho.forEach(t),Zgt=i(te),m8=n(te,"LI",{});var _ho=s(m8);FIe=n(_ho,"STRONG",{});var rVa=s(FIe);Kgt=r(rVa,"canine"),rVa.forEach(t),eht=r(_ho," \u2014 "),qme=n(_ho,"A",{href:!0});var tVa=s(qme);oht=r(tVa,"CanineForMultipleChoice"),tVa.forEach(t),rht=r(_ho," (CANINE model)"),_ho.forEach(t),tht=i(te),c8=n(te,"LI",{});var bho=s(c8);MIe=n(bho,"STRONG",{});var aVa=s(MIe);aht=r(aVa,"convbert"),aVa.forEach(t),nht=r(bho," \u2014 "),jme=n(bho,"A",{href:!0});var nVa=s(jme);sht=r(nVa,"ConvBertForMultipleChoice"),nVa.forEach(t),lht=r(bho," (ConvBERT model)"),bho.forEach(t),iht=i(te),f8=n(te,"LI",{});var vho=s(f8);EIe=n(vho,"STRONG",{});var sVa=s(EIe);dht=r(sVa,"data2vec-text"),sVa.forEach(t),mht=r(vho," \u2014 "),Ome=n(vho,"A",{href:!0});var lVa=s(Ome);cht=r(lVa,"Data2VecTextForMultipleChoice"),lVa.forEach(t),fht=r(vho," (Data2VecText model)"),vho.forEach(t),ght=i(te),g8=n(te,"LI",{});var Tho=s(g8);CIe=n(Tho,"STRONG",{});var iVa=s(CIe);hht=r(iVa,"deberta-v2"),iVa.forEach(t),uht=r(Tho," \u2014 "),Vme=n(Tho,"A",{href:!0});var dVa=s(Vme);pht=r(dVa,"DebertaV2ForMultipleChoice"),dVa.forEach(t),_ht=r(Tho," (DeBERTa-v2 model)"),Tho.forEach(t),bht=i(te),h8=n(te,"LI",{});var Fho=s(h8);wIe=n(Fho,"STRONG",{});var mVa=s(wIe);vht=r(mVa,"distilbert"),mVa.forEach(t),Tht=r(Fho," \u2014 "),Xme=n(Fho,"A",{href:!0});var cVa=s(Xme);Fht=r(cVa,"DistilBertForMultipleChoice"),cVa.forEach(t),Mht=r(Fho," (DistilBERT model)"),Fho.forEach(t),Eht=i(te),u8=n(te,"LI",{});var Mho=s(u8);AIe=n(Mho,"STRONG",{});var fVa=s(AIe);Cht=r(fVa,"electra"),fVa.forEach(t),wht=r(Mho," \u2014 "),zme=n(Mho,"A",{href:!0});var gVa=s(zme);Aht=r(gVa,"ElectraForMultipleChoice"),gVa.forEach(t),Lht=r(Mho," (ELECTRA model)"),Mho.forEach(t),yht=i(te),p8=n(te,"LI",{});var Eho=s(p8);LIe=n(Eho,"STRONG",{});var hVa=s(LIe);xht=r(hVa,"ernie"),hVa.forEach(t),kht=r(Eho," \u2014 "),Wme=n(Eho,"A",{href:!0});var uVa=s(Wme);$ht=r(uVa,"ErnieForMultipleChoice"),uVa.forEach(t),Sht=r(Eho," (ERNIE model)"),Eho.forEach(t),Rht=i(te),_8=n(te,"LI",{});var Cho=s(_8);yIe=n(Cho,"STRONG",{});var pVa=s(yIe);Pht=r(pVa,"flaubert"),pVa.forEach(t),Iht=r(Cho," \u2014 "),Qme=n(Cho,"A",{href:!0});var _Va=s(Qme);Bht=r(_Va,"FlaubertForMultipleChoice"),_Va.forEach(t),Nht=r(Cho," (FlauBERT model)"),Cho.forEach(t),Dht=i(te),b8=n(te,"LI",{});var who=s(b8);xIe=n(who,"STRONG",{});var bVa=s(xIe);Ght=r(bVa,"fnet"),bVa.forEach(t),qht=r(who," \u2014 "),Ume=n(who,"A",{href:!0});var vVa=s(Ume);jht=r(vVa,"FNetForMultipleChoice"),vVa.forEach(t),Oht=r(who," (FNet model)"),who.forEach(t),Vht=i(te),v8=n(te,"LI",{});var Aho=s(v8);kIe=n(Aho,"STRONG",{});var TVa=s(kIe);Xht=r(TVa,"funnel"),TVa.forEach(t),zht=r(Aho," \u2014 "),Hme=n(Aho,"A",{href:!0});var FVa=s(Hme);Wht=r(FVa,"FunnelForMultipleChoice"),FVa.forEach(t),Qht=r(Aho," (Funnel Transformer model)"),Aho.forEach(t),Uht=i(te),T8=n(te,"LI",{});var Lho=s(T8);$Ie=n(Lho,"STRONG",{});var MVa=s($Ie);Hht=r(MVa,"ibert"),MVa.forEach(t),Jht=r(Lho," \u2014 "),Jme=n(Lho,"A",{href:!0});var EVa=s(Jme);Yht=r(EVa,"IBertForMultipleChoice"),EVa.forEach(t),Zht=r(Lho," (I-BERT model)"),Lho.forEach(t),Kht=i(te),F8=n(te,"LI",{});var yho=s(F8);SIe=n(yho,"STRONG",{});var CVa=s(SIe);eut=r(CVa,"longformer"),CVa.forEach(t),out=r(yho," \u2014 "),Yme=n(yho,"A",{href:!0});var wVa=s(Yme);rut=r(wVa,"LongformerForMultipleChoice"),wVa.forEach(t),tut=r(yho," (Longformer model)"),yho.forEach(t),aut=i(te),M8=n(te,"LI",{});var xho=s(M8);RIe=n(xho,"STRONG",{});var AVa=s(RIe);nut=r(AVa,"luke"),AVa.forEach(t),sut=r(xho," \u2014 "),Zme=n(xho,"A",{href:!0});var LVa=s(Zme);lut=r(LVa,"LukeForMultipleChoice"),LVa.forEach(t),iut=r(xho," (LUKE 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"),rce=n(Rho,"A",{href:!0});var IVa=s(rce);Lut=r(IVa,"NezhaForMultipleChoice"),IVa.forEach(t),yut=r(Rho," (Nezha model)"),Rho.forEach(t),xut=i(te),L8=n(te,"LI",{});var Pho=s(L8);DIe=n(Pho,"STRONG",{});var BVa=s(DIe);kut=r(BVa,"nystromformer"),BVa.forEach(t),$ut=r(Pho," \u2014 "),tce=n(Pho,"A",{href:!0});var NVa=s(tce);Sut=r(NVa,"NystromformerForMultipleChoice"),NVa.forEach(t),Rut=r(Pho," (Nystr\xF6mformer model)"),Pho.forEach(t),Put=i(te),y8=n(te,"LI",{});var Iho=s(y8);GIe=n(Iho,"STRONG",{});var DVa=s(GIe);Iut=r(DVa,"qdqbert"),DVa.forEach(t),But=r(Iho," \u2014 "),ace=n(Iho,"A",{href:!0});var GVa=s(ace);Nut=r(GVa,"QDQBertForMultipleChoice"),GVa.forEach(t),Dut=r(Iho," (QDQBert model)"),Iho.forEach(t),Gut=i(te),x8=n(te,"LI",{});var Bho=s(x8);qIe=n(Bho,"STRONG",{});var qVa=s(qIe);qut=r(qVa,"rembert"),qVa.forEach(t),jut=r(Bho," \u2014 "),nce=n(Bho,"A",{href:!0});var jVa=s(nce);Out=r(jVa,"RemBertForMultipleChoice"),jVa.forEach(t),Vut=r(Bho," (RemBERT model)"),Bho.forEach(t),Xut=i(te),k8=n(te,"LI",{});var Nho=s(k8);jIe=n(Nho,"STRONG",{});var OVa=s(jIe);zut=r(OVa,"roberta"),OVa.forEach(t),Wut=r(Nho," \u2014 "),sce=n(Nho,"A",{href:!0});var VVa=s(sce);Qut=r(VVa,"RobertaForMultipleChoice"),VVa.forEach(t),Uut=r(Nho," (RoBERTa model)"),Nho.forEach(t),Hut=i(te),$8=n(te,"LI",{});var Dho=s($8);OIe=n(Dho,"STRONG",{});var XVa=s(OIe);Jut=r(XVa,"roberta-prelayernorm"),XVa.forEach(t),Yut=r(Dho," \u2014 "),lce=n(Dho,"A",{href:!0});var zVa=s(lce);Zut=r(zVa,"RobertaPreLayerNormForMultipleChoice"),zVa.forEach(t),Kut=r(Dho," (RoBERTa-PreLayerNorm model)"),Dho.forEach(t),ept=i(te),S8=n(te,"LI",{});var Gho=s(S8);VIe=n(Gho,"STRONG",{});var WVa=s(VIe);opt=r(WVa,"roc_bert"),WVa.forEach(t),rpt=r(Gho," \u2014 "),ice=n(Gho,"A",{href:!0});var QVa=s(ice);tpt=r(QVa,"RoCBertForMultipleChoice"),QVa.forEach(t),apt=r(Gho," (RoCBert model)"),Gho.forEach(t),npt=i(te),R8=n(te,"LI",{});var qho=s(R8);XIe=n(qho,"STRONG",{});var UVa=s(XIe);spt=r(UVa,"roformer"),UVa.forEach(t),lpt=r(qho," \u2014 "),dce=n(qho,"A",{href:!0});var HVa=s(dce);ipt=r(HVa,"RoFormerForMultipleChoice"),HVa.forEach(t),dpt=r(qho," (RoFormer model)"),qho.forEach(t),mpt=i(te),P8=n(te,"LI",{});var jho=s(P8);zIe=n(jho,"STRONG",{});var JVa=s(zIe);cpt=r(JVa,"squeezebert"),JVa.forEach(t),fpt=r(jho," \u2014 "),mce=n(jho,"A",{href:!0});var YVa=s(mce);gpt=r(YVa,"SqueezeBertForMultipleChoice"),YVa.forEach(t),hpt=r(jho," (SqueezeBERT model)"),jho.forEach(t),upt=i(te),I8=n(te,"LI",{});var Oho=s(I8);WIe=n(Oho,"STRONG",{});var ZVa=s(WIe);ppt=r(ZVa,"xlm"),ZVa.forEach(t),_pt=r(Oho," \u2014 "),cce=n(Oho,"A",{href:!0});var KVa=s(cce);bpt=r(KVa,"XLMForMultipleChoice"),KVa.forEach(t),vpt=r(Oho," (XLM model)"),Oho.forEach(t),Tpt=i(te),B8=n(te,"LI",{});var Vho=s(B8);QIe=n(Vho,"STRONG",{});var eXa=s(QIe);Fpt=r(eXa,"xlm-roberta"),eXa.forEach(t),Mpt=r(Vho," \u2014 "),fce=n(Vho,"A",{href:!0});var oXa=s(fce);Ept=r(oXa,"XLMRobertaForMultipleChoice"),oXa.forEach(t),Cpt=r(Vho," (XLM-RoBERTa model)"),Vho.forEach(t),wpt=i(te),N8=n(te,"LI",{});var Xho=s(N8);UIe=n(Xho,"STRONG",{});var rXa=s(UIe);Apt=r(rXa,"xlm-roberta-xl"),rXa.forEach(t),Lpt=r(Xho," \u2014 "),gce=n(Xho,"A",{href:!0});var tXa=s(gce);ypt=r(tXa,"XLMRobertaXLForMultipleChoice"),tXa.forEach(t),xpt=r(Xho," (XLM-RoBERTa-XL model)"),Xho.forEach(t),kpt=i(te),D8=n(te,"LI",{});var zho=s(D8);HIe=n(zho,"STRONG",{});var aXa=s(HIe);$pt=r(aXa,"xlnet"),aXa.forEach(t),Spt=r(zho," \u2014 "),hce=n(zho,"A",{href:!0});var nXa=s(hce);Rpt=r(nXa,"XLNetForMultipleChoice"),nXa.forEach(t),Ppt=r(zho," (XLNet model)"),zho.forEach(t),Ipt=i(te),G8=n(te,"LI",{});var Who=s(G8);JIe=n(Who,"STRONG",{});var sXa=s(JIe);Bpt=r(sXa,"yoso"),sXa.forEach(t),Npt=r(Who," \u2014 "),uce=n(Who,"A",{href:!0});var lXa=s(uce);Dpt=r(lXa,"YosoForMultipleChoice"),lXa.forEach(t),Gpt=r(Who," (YOSO model)"),Who.forEach(t),te.forEach(t),qpt=i(Oa),q8=n(Oa,"P",{});var Qho=s(q8);jpt=r(Qho,"The model is set in evaluation mode by default using "),YIe=n(Qho,"CODE",{});var iXa=s(YIe);Opt=r(iXa,"model.eval()"),iXa.forEach(t),Vpt=r(Qho,` (so for instance, dropout modules are
deactivated). To train the model, you should first set it back in training mode with `),ZIe=n(Qho,"CODE",{});var dXa=s(ZIe);Xpt=r(dXa,"model.train()"),dXa.forEach(t),Qho.forEach(t),zpt=i(Oa),T(j8.$$.fragment,Oa),Oa.forEach(t),Di.forEach(t),oTo=i(c),ic=n(c,"H3",{class:!0});var PMo=s(ic);O8=n(PMo,"A",{id:!0,class:!0,href:!0});var mXa=s(O8);KIe=n(mXa,"SPAN",{});var cXa=s(KIe);T(cN.$$.fragment,cXa),cXa.forEach(t),mXa.forEach(t),Wpt=i(PMo),eBe=n(PMo,"SPAN",{});var fXa=s(eBe);Qpt=r(fXa,"TFAutoModelForMultipleChoice"),fXa.forEach(t),PMo.forEach(t),rTo=i(c),lr=n(c,"DIV",{class:!0});var Gi=s(lr);T(fN.$$.fragment,Gi),Upt=i(Gi),dc=n(Gi,"P",{});var obe=s(dc);Hpt=r(obe,`This is a generic model class that will be instantiated as one of the model classes of the library (with a multiple choice head) when created
with the `),pce=n(obe,"A",{href:!0});var gXa=s(pce);Jpt=r(gXa,"from_pretrained()"),gXa.forEach(t),Ypt=r(obe," class method or the "),_ce=n(obe,"A",{href:!0});var hXa=s(_ce);Zpt=r(hXa,"from_config()"),hXa.forEach(t),Kpt=r(obe,` class
method.`),obe.forEach(t),e_t=i(Gi),gN=n(Gi,"P",{});var IMo=s(gN);o_t=r(IMo,"This class cannot be instantiated directly using "),oBe=n(IMo,"CODE",{});var uXa=s(oBe);r_t=r(uXa,"__init__()"),uXa.forEach(t),t_t=r(IMo," (throws an error)."),IMo.forEach(t),a_t=i(Gi),Ut=n(Gi,"DIV",{class:!0});var uR=s(Ut);T(hN.$$.fragment,uR),n_t=i(uR),rBe=n(uR,"P",{});var pXa=s(rBe);s_t=r(pXa,"Instantiates one of the model classes of the library (with a multiple choice head) from a configuration."),pXa.forEach(t),l_t=i(uR),mc=n(uR,"P",{});var rbe=s(mc);i_t=r(rbe,`Note:
Loading a model from its configuration file does `),tBe=n(rbe,"STRONG",{});var _Xa=s(tBe);d_t=r(_Xa,"not"),_Xa.forEach(t),m_t=r(rbe,` load the model weights. It only affects the
model\u2019s configuration. Use `),bce=n(rbe,"A",{href:!0});var bXa=s(bce);c_t=r(bXa,"from_pretrained()"),bXa.forEach(t),f_t=r(rbe," to load the model weights."),rbe.forEach(t),g_t=i(uR),T(V8.$$.fragment,uR),uR.forEach(t),h_t=i(Gi),st=n(Gi,"DIV",{class:!0});var qi=s(st);T(uN.$$.fragment,qi),u_t=i(qi),aBe=n(qi,"P",{});var vXa=s(aBe);p_t=r(vXa,"Instantiate one of the model classes of the library (with a multiple choice head) from a pretrained model."),vXa.forEach(t),__t=i(qi),Nn=n(qi,"P",{});var pR=s(Nn);b_t=r(pR,"The model class to instantiate is selected based on the "),nBe=n(pR,"CODE",{});var TXa=s(nBe);v_t=r(TXa,"model_type"),TXa.forEach(t),T_t=r(pR,` property of the config object (either
passed as an argument or loaded from `),sBe=n(pR,"CODE",{});var FXa=s(sBe);F_t=r(FXa,"pretrained_model_name_or_path"),FXa.forEach(t),M_t=r(pR,` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),lBe=n(pR,"CODE",{});var MXa=s(lBe);E_t=r(MXa,"pretrained_model_name_or_path"),MXa.forEach(t),C_t=r(pR,":"),pR.forEach(t),w_t=i(qi),Me=n(qi,"UL",{});var Ce=s(Me);X8=n(Ce,"LI",{});var Uho=s(X8);iBe=n(Uho,"STRONG",{});var EXa=s(iBe);A_t=r(EXa,"albert"),EXa.forEach(t),L_t=r(Uho," \u2014 "),vce=n(Uho,"A",{href:!0});var CXa=s(vce);y_t=r(CXa,"TFAlbertForMultipleChoice"),CXa.forEach(t),x_t=r(Uho," (ALBERT model)"),Uho.forEach(t),k_t=i(Ce),z8=n(Ce,"LI",{});var Hho=s(z8);dBe=n(Hho,"STRONG",{});var wXa=s(dBe);$_t=r(wXa,"bert"),wXa.forEach(t),S_t=r(Hho," \u2014 "),Tce=n(Hho,"A",{href:!0});var AXa=s(Tce);R_t=r(AXa,"TFBertForMultipleChoice"),AXa.forEach(t),P_t=r(Hho," (BERT model)"),Hho.forEach(t),I_t=i(Ce),W8=n(Ce,"LI",{});var Jho=s(W8);mBe=n(Jho,"STRONG",{});var LXa=s(mBe);B_t=r(LXa,"camembert"),LXa.forEach(t),N_t=r(Jho," \u2014 "),Fce=n(Jho,"A",{href:!0});var yXa=s(Fce);D_t=r(yXa,"TFCamembertForMultipleChoice"),yXa.forEach(t),G_t=r(Jho," (CamemBERT 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with the `),Nce=n(tbe,"A",{href:!0});var iza=s(Nce);f2t=r(iza,"from_pretrained()"),iza.forEach(t),g2t=r(tbe," class method or the "),Dce=n(tbe,"A",{href:!0});var dza=s(Dce);h2t=r(dza,"from_config()"),dza.forEach(t),u2t=r(tbe,` class
method.`),tbe.forEach(t),p2t=i(ji),bN=n(ji,"P",{});var NMo=s(bN);_2t=r(NMo,"This class cannot be instantiated directly using "),yBe=n(NMo,"CODE",{});var mza=s(yBe);b2t=r(mza,"__init__()"),mza.forEach(t),v2t=r(NMo," (throws an error)."),NMo.forEach(t),T2t=i(ji),Ht=n(ji,"DIV",{class:!0});var _R=s(Ht);T(vN.$$.fragment,_R),F2t=i(_R),xBe=n(_R,"P",{});var cza=s(xBe);M2t=r(cza,"Instantiates one of the model classes of the library (with a multiple choice head) from a configuration."),cza.forEach(t),E2t=i(_R),gc=n(_R,"P",{});var abe=s(gc);C2t=r(abe,`Note:
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model\u2019s configuration. Use `),Gce=n(abe,"A",{href:!0});var gza=s(Gce);L2t=r(gza,"from_pretrained()"),gza.forEach(t),y2t=r(abe," to load the model weights."),abe.forEach(t),x2t=i(_R),T(mA.$$.fragment,_R),_R.forEach(t),k2t=i(ji),lt=n(ji,"DIV",{class:!0});var Oi=s(lt);T(TN.$$.fragment,Oi),$2t=i(Oi),$Be=n(Oi,"P",{});var hza=s($Be);S2t=r(hza,"Instantiate one of the model classes of the library (with a multiple choice head) from a pretrained model."),hza.forEach(t),R2t=i(Oi),Dn=n(Oi,"P",{});var bR=s(Dn);P2t=r(bR,"The model class to instantiate is selected based on the "),SBe=n(bR,"CODE",{});var uza=s(SBe);I2t=r(uza,"model_type"),uza.forEach(t),B2t=r(bR,` property of the config object (either
passed as an argument or loaded from `),RBe=n(bR,"CODE",{});var pza=s(RBe);N2t=r(pza,"pretrained_model_name_or_path"),pza.forEach(t),D2t=r(bR,` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),PBe=n(bR,"CODE",{});var _za=s(PBe);G2t=r(_za,"pretrained_model_name_or_path"),_za.forEach(t),q2t=r(bR,":"),bR.forEach(t),j2t=i(Oi),Oe=n(Oi,"UL",{});var Ye=s(Oe);cA=n(Ye,"LI",{});var fuo=s(cA);IBe=n(fuo,"STRONG",{});var bza=s(IBe);O2t=r(bza,"albert"),bza.forEach(t),V2t=r(fuo," \u2014 "),qce=n(fuo,"A",{href:!0});var vza=s(qce);X2t=r(vza,"FlaxAlbertForMultipleChoice"),vza.forEach(t),z2t=r(fuo," (ALBERT model)"),fuo.forEach(t),W2t=i(Ye),fA=n(Ye,"LI",{});var guo=s(fA);BBe=n(guo,"STRONG",{});var Tza=s(BBe);Q2t=r(Tza,"bert"),Tza.forEach(t),U2t=r(guo," \u2014 "),jce=n(guo,"A",{href:!0});var Fza=s(jce);H2t=r(Fza,"FlaxBertForMultipleChoice"),Fza.forEach(t),J2t=r(guo," (BERT model)"),guo.forEach(t),Y2t=i(Ye),gA=n(Ye,"LI",{});var huo=s(gA);NBe=n(huo,"STRONG",{});var Mza=s(NBe);Z2t=r(Mza,"big_bird"),Mza.forEach(t),K2t=r(huo," \u2014 "),Oce=n(huo,"A",{href:!0});var Eza=s(Oce);ebt=r(Eza,"FlaxBigBirdForMultipleChoice"),Eza.forEach(t),obt=r(huo," (BigBird model)"),huo.forEach(t),rbt=i(Ye),hA=n(Ye,"LI",{});var uuo=s(hA);DBe=n(uuo,"STRONG",{});var Cza=s(DBe);tbt=r(Cza,"distilbert"),Cza.forEach(t),abt=r(uuo," \u2014 "),Vce=n(uuo,"A",{href:!0});var wza=s(Vce);nbt=r(wza,"FlaxDistilBertForMultipleChoice"),wza.forEach(t),sbt=r(uuo," (DistilBERT model)"),uuo.forEach(t),lbt=i(Ye),uA=n(Ye,"LI",{});var puo=s(uA);GBe=n(puo,"STRONG",{});var Aza=s(GBe);ibt=r(Aza,"electra"),Aza.forEach(t),dbt=r(puo," \u2014 "),Xce=n(puo,"A",{href:!0});var Lza=s(Xce);mbt=r(Lza,"FlaxElectraForMultipleChoice"),Lza.forEach(t),cbt=r(puo," (ELECTRA model)"),puo.forEach(t),fbt=i(Ye),pA=n(Ye,"LI",{});var _uo=s(pA);qBe=n(_uo,"STRONG",{});var yza=s(qBe);gbt=r(yza,"roberta"),yza.forEach(t),hbt=r(_uo," \u2014 "),zce=n(_uo,"A",{href:!0});var xza=s(zce);ubt=r(xza,"FlaxRobertaForMultipleChoice"),xza.forEach(t),pbt=r(_uo," (RoBERTa model)"),_uo.forEach(t),_bt=i(Ye),_A=n(Ye,"LI",{});var buo=s(_A);jBe=n(buo,"STRONG",{});var kza=s(jBe);bbt=r(kza,"roberta-prelayernorm"),kza.forEach(t),vbt=r(buo," \u2014 "),Wce=n(buo,"A",{href:!0});var $za=s(Wce);Tbt=r($za,"FlaxRobertaPreLayerNormForMultipleChoice"),$za.forEach(t),Fbt=r(buo," (RoBERTa-PreLayerNorm model)"),buo.forEach(t),Mbt=i(Ye),bA=n(Ye,"LI",{});var vuo=s(bA);OBe=n(vuo,"STRONG",{});var Sza=s(OBe);Ebt=r(Sza,"roformer"),Sza.forEach(t),Cbt=r(vuo," \u2014 "),Qce=n(vuo,"A",{href:!0});var Rza=s(Qce);wbt=r(Rza,"FlaxRoFormerForMultipleChoice"),Rza.forEach(t),Abt=r(vuo," (RoFormer model)"),vuo.forEach(t),Lbt=i(Ye),vA=n(Ye,"LI",{});var Tuo=s(vA);VBe=n(Tuo,"STRONG",{});var Pza=s(VBe);ybt=r(Pza,"xlm-roberta"),Pza.forEach(t),xbt=r(Tuo," \u2014 "),Uce=n(Tuo,"A",{href:!0});var Iza=s(Uce);kbt=r(Iza,"FlaxXLMRobertaForMultipleChoice"),Iza.forEach(t),$bt=r(Tuo," (XLM-RoBERTa model)"),Tuo.forEach(t),Ye.forEach(t),Sbt=i(Oi),T(TA.$$.fragment,Oi),Oi.forEach(t),ji.forEach(t),nTo=i(c),hc=n(c,"H3",{class:!0});var DMo=s(hc);FA=n(DMo,"A",{id:!0,class:!0,href:!0});var Bza=s(FA);XBe=n(Bza,"SPAN",{});var Nza=s(XBe);T(FN.$$.fragment,Nza),Nza.forEach(t),Bza.forEach(t),Rbt=i(DMo),zBe=n(DMo,"SPAN",{});var Dza=s(zBe);Pbt=r(Dza,"AutoModelForNextSentencePrediction"),Dza.forEach(t),DMo.forEach(t),sTo=i(c),dr=n(c,"DIV",{class:!0});var Vi=s(dr);T(MN.$$.fragment,Vi),Ibt=i(Vi),uc=n(Vi,"P",{});var nbe=s(uc);Bbt=r(nbe,`This is a generic model class that will be instantiated as one of the model classes of the library (with a next sentence prediction head) when created
with the `),Hce=n(nbe,"A",{href:!0});var Gza=s(Hce);Nbt=r(Gza,"from_pretrained()"),Gza.forEach(t),Dbt=r(nbe," class method or the "),Jce=n(nbe,"A",{href:!0});var qza=s(Jce);Gbt=r(qza,"from_config()"),qza.forEach(t),qbt=r(nbe,` class
method.`),nbe.forEach(t),jbt=i(Vi),EN=n(Vi,"P",{});var GMo=s(EN);Obt=r(GMo,"This class cannot be instantiated directly using "),WBe=n(GMo,"CODE",{});var jza=s(WBe);Vbt=r(jza,"__init__()"),jza.forEach(t),Xbt=r(GMo," (throws an error)."),GMo.forEach(t),zbt=i(Vi),Jt=n(Vi,"DIV",{class:!0});var vR=s(Jt);T(CN.$$.fragment,vR),Wbt=i(vR),QBe=n(vR,"P",{});var Oza=s(QBe);Qbt=r(Oza,"Instantiates one of the model classes of the library (with a next sentence prediction head) from a configuration."),Oza.forEach(t),Ubt=i(vR),pc=n(vR,"P",{});var sbe=s(pc);Hbt=r(sbe,`Note:
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model\u2019s configuration. Use `),Yce=n(sbe,"A",{href:!0});var Xza=s(Yce);Zbt=r(Xza,"from_pretrained()"),Xza.forEach(t),Kbt=r(sbe," to load the model weights."),sbe.forEach(t),evt=i(vR),T(MA.$$.fragment,vR),vR.forEach(t),ovt=i(Vi),ho=n(Vi,"DIV",{class:!0});var Va=s(ho);T(wN.$$.fragment,Va),rvt=i(Va),HBe=n(Va,"P",{});var zza=s(HBe);tvt=r(zza,"Instantiate one of the model classes of the library (with a next sentence prediction head) from a pretrained model."),zza.forEach(t),avt=i(Va),Gn=n(Va,"P",{});var TR=s(Gn);nvt=r(TR,"The model class to instantiate is selected based on the "),JBe=n(TR,"CODE",{});var Wza=s(JBe);svt=r(Wza,"model_type"),Wza.forEach(t),lvt=r(TR,` property of the config object (either
passed as an argument or loaded from `),YBe=n(TR,"CODE",{});var Qza=s(YBe);ivt=r(Qza,"pretrained_model_name_or_path"),Qza.forEach(t),dvt=r(TR,` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),ZBe=n(TR,"CODE",{});var Uza=s(ZBe);mvt=r(Uza,"pretrained_model_name_or_path"),Uza.forEach(t),cvt=r(TR,":"),TR.forEach(t),fvt=i(Va),Ke=n(Va,"UL",{});var Mt=s(Ke);EA=n(Mt,"LI",{});var Fuo=s(EA);KBe=n(Fuo,"STRONG",{});var Hza=s(KBe);gvt=r(Hza,"bert"),Hza.forEach(t),hvt=r(Fuo," \u2014 "),Zce=n(Fuo,"A",{href:!0});var Jza=s(Zce);uvt=r(Jza,"BertForNextSentencePrediction"),Jza.forEach(t),pvt=r(Fuo," (BERT model)"),Fuo.forEach(t),_vt=i(Mt),CA=n(Mt,"LI",{});var Muo=s(CA);eNe=n(Muo,"STRONG",{});var Yza=s(eNe);bvt=r(Yza,"ernie"),Yza.forEach(t),vvt=r(Muo," \u2014 "),Kce=n(Muo,"A",{href:!0});var Zza=s(Kce);Tvt=r(Zza,"ErnieForNextSentencePrediction"),Zza.forEach(t),Fvt=r(Muo," (ERNIE model)"),Muo.forEach(t),Mvt=i(Mt),wA=n(Mt,"LI",{});var Euo=s(wA);oNe=n(Euo,"STRONG",{});var Kza=s(oNe);Evt=r(Kza,"fnet"),Kza.forEach(t),Cvt=r(Euo," \u2014 "),efe=n(Euo,"A",{href:!0});var eWa=s(efe);wvt=r(eWa,"FNetForNextSentencePrediction"),eWa.forEach(t),Avt=r(Euo," (FNet model)"),Euo.forEach(t),Lvt=i(Mt),AA=n(Mt,"LI",{});var Cuo=s(AA);rNe=n(Cuo,"STRONG",{});var oWa=s(rNe);yvt=r(oWa,"megatron-bert"),oWa.forEach(t),xvt=r(Cuo," \u2014 "),ofe=n(Cuo,"A",{href:!0});var rWa=s(ofe);kvt=r(rWa,"MegatronBertForNextSentencePrediction"),rWa.forEach(t),$vt=r(Cuo," (Megatron-BERT model)"),Cuo.forEach(t),Svt=i(Mt),LA=n(Mt,"LI",{});var wuo=s(LA);tNe=n(wuo,"STRONG",{});var tWa=s(tNe);Rvt=r(tWa,"mobilebert"),tWa.forEach(t),Pvt=r(wuo," \u2014 "),rfe=n(wuo,"A",{href:!0});var aWa=s(rfe);Ivt=r(aWa,"MobileBertForNextSentencePrediction"),aWa.forEach(t),Bvt=r(wuo," (MobileBERT model)"),wuo.forEach(t),Nvt=i(Mt),yA=n(Mt,"LI",{});var Auo=s(yA);aNe=n(Auo,"STRONG",{});var nWa=s(aNe);Dvt=r(nWa,"nezha"),nWa.forEach(t),Gvt=r(Auo," \u2014 "),tfe=n(Auo,"A",{href:!0});var sWa=s(tfe);qvt=r(sWa,"NezhaForNextSentencePrediction"),sWa.forEach(t),jvt=r(Auo," (Nezha model)"),Auo.forEach(t),Ovt=i(Mt),xA=n(Mt,"LI",{});var Luo=s(xA);nNe=n(Luo,"STRONG",{});var lWa=s(nNe);Vvt=r(lWa,"qdqbert"),lWa.forEach(t),Xvt=r(Luo," \u2014 "),afe=n(Luo,"A",{href:!0});var iWa=s(afe);zvt=r(iWa,"QDQBertForNextSentencePrediction"),iWa.forEach(t),Wvt=r(Luo," (QDQBert model)"),Luo.forEach(t),Mt.forEach(t),Qvt=i(Va),kA=n(Va,"P",{});var yuo=s(kA);Uvt=r(yuo,"The model is set in evaluation mode by default using "),sNe=n(yuo,"CODE",{});var dWa=s(sNe);Hvt=r(dWa,"model.eval()"),dWa.forEach(t),Jvt=r(yuo,` (so for instance, dropout modules are
deactivated). To train the model, you should first set it back in training mode with `),lNe=n(yuo,"CODE",{});var mWa=s(lNe);Yvt=r(mWa,"model.train()"),mWa.forEach(t),yuo.forEach(t),Zvt=i(Va),T($A.$$.fragment,Va),Va.forEach(t),Vi.forEach(t),lTo=i(c),_c=n(c,"H3",{class:!0});var qMo=s(_c);SA=n(qMo,"A",{id:!0,class:!0,href:!0});var cWa=s(SA);iNe=n(cWa,"SPAN",{});var fWa=s(iNe);T(AN.$$.fragment,fWa),fWa.forEach(t),cWa.forEach(t),Kvt=i(qMo),dNe=n(qMo,"SPAN",{});var gWa=s(dNe);eTt=r(gWa,"TFAutoModelForNextSentencePrediction"),gWa.forEach(t),qMo.forEach(t),iTo=i(c),mr=n(c,"DIV",{class:!0});var Xi=s(mr);T(LN.$$.fragment,Xi),oTt=i(Xi),bc=n(Xi,"P",{});var lbe=s(bc);rTt=r(lbe,`This is a generic model class that will be instantiated as one of the model classes of the library (with a next sentence prediction head) when created
with the `),nfe=n(lbe,"A",{href:!0});var hWa=s(nfe);tTt=r(hWa,"from_pretrained()"),hWa.forEach(t),aTt=r(lbe," class method or the "),sfe=n(lbe,"A",{href:!0});var uWa=s(sfe);nTt=r(uWa,"from_config()"),uWa.forEach(t),sTt=r(lbe,` class
method.`),lbe.forEach(t),lTt=i(Xi),yN=n(Xi,"P",{});var jMo=s(yN);iTt=r(jMo,"This class cannot be instantiated directly using "),mNe=n(jMo,"CODE",{});var pWa=s(mNe);dTt=r(pWa,"__init__()"),pWa.forEach(t),mTt=r(jMo," (throws an error)."),jMo.forEach(t),cTt=i(Xi),Yt=n(Xi,"DIV",{class:!0});var FR=s(Yt);T(xN.$$.fragment,FR),fTt=i(FR),cNe=n(FR,"P",{});var _Wa=s(cNe);gTt=r(_Wa,"Instantiates one of the model classes of the library (with a next sentence prediction head) from a configuration."),_Wa.forEach(t),hTt=i(FR),vc=n(FR,"P",{});var ibe=s(vc);uTt=r(ibe,`Note:
Loading a model from its configuration file does `),fNe=n(ibe,"STRONG",{});var bWa=s(fNe);pTt=r(bWa,"not"),bWa.forEach(t),_Tt=r(ibe,` load the model weights. It only affects the
model\u2019s configuration. Use `),lfe=n(ibe,"A",{href:!0});var vWa=s(lfe);bTt=r(vWa,"from_pretrained()"),vWa.forEach(t),vTt=r(ibe," to load the model weights."),ibe.forEach(t),TTt=i(FR),T(RA.$$.fragment,FR),FR.forEach(t),FTt=i(Xi),it=n(Xi,"DIV",{class:!0});var zi=s(it);T(kN.$$.fragment,zi),MTt=i(zi),gNe=n(zi,"P",{});var TWa=s(gNe);ETt=r(TWa,"Instantiate one of the model classes of the library (with a next sentence prediction head) from a pretrained model."),TWa.forEach(t),CTt=i(zi),qn=n(zi,"P",{});var MR=s(qn);wTt=r(MR,"The model class to instantiate is selected based on the "),hNe=n(MR,"CODE",{});var FWa=s(hNe);ATt=r(FWa,"model_type"),FWa.forEach(t),LTt=r(MR,` property of the config object (either
passed as an argument or loaded from `),uNe=n(MR,"CODE",{});var MWa=s(uNe);yTt=r(MWa,"pretrained_model_name_or_path"),MWa.forEach(t),xTt=r(MR,` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),pNe=n(MR,"CODE",{});var EWa=s(pNe);kTt=r(EWa,"pretrained_model_name_or_path"),EWa.forEach(t),$Tt=r(MR,":"),MR.forEach(t),STt=i(zi),$N=n(zi,"UL",{});var OMo=s($N);PA=n(OMo,"LI",{});var xuo=s(PA);_Ne=n(xuo,"STRONG",{});var CWa=s(_Ne);RTt=r(CWa,"bert"),CWa.forEach(t),PTt=r(xuo," \u2014 "),ife=n(xuo,"A",{href:!0});var wWa=s(ife);ITt=r(wWa,"TFBertForNextSentencePrediction"),wWa.forEach(t),BTt=r(xuo," (BERT model)"),xuo.forEach(t),NTt=i(OMo),IA=n(OMo,"LI",{});var kuo=s(IA);bNe=n(kuo,"STRONG",{});var AWa=s(bNe);DTt=r(AWa,"mobilebert"),AWa.forEach(t),GTt=r(kuo," \u2014 "),dfe=n(kuo,"A",{href:!0});var LWa=s(dfe);qTt=r(LWa,"TFMobileBertForNextSentencePrediction"),LWa.forEach(t),jTt=r(kuo," (MobileBERT model)"),kuo.forEach(t),OMo.forEach(t),OTt=i(zi),T(BA.$$.fragment,zi),zi.forEach(t),Xi.forEach(t),dTo=i(c),Tc=n(c,"H3",{class:!0});var VMo=s(Tc);NA=n(VMo,"A",{id:!0,class:!0,href:!0});var yWa=s(NA);vNe=n(yWa,"SPAN",{});var xWa=s(vNe);T(SN.$$.fragment,xWa),xWa.forEach(t),yWa.forEach(t),VTt=i(VMo),TNe=n(VMo,"SPAN",{});var kWa=s(TNe);XTt=r(kWa,"FlaxAutoModelForNextSentencePrediction"),kWa.forEach(t),VMo.forEach(t),mTo=i(c),cr=n(c,"DIV",{class:!0});var Wi=s(cr);T(RN.$$.fragment,Wi),zTt=i(Wi),Fc=n(Wi,"P",{});var dbe=s(Fc);WTt=r(dbe,`This is a generic model class that will be instantiated as one of the model classes of the library (with a next sentence prediction head) when created
with the `),mfe=n(dbe,"A",{href:!0});var $Wa=s(mfe);QTt=r($Wa,"from_pretrained()"),$Wa.forEach(t),UTt=r(dbe," class method or the "),cfe=n(dbe,"A",{href:!0});var SWa=s(cfe);HTt=r(SWa,"from_config()"),SWa.forEach(t),JTt=r(dbe,` class
method.`),dbe.forEach(t),YTt=i(Wi),PN=n(Wi,"P",{});var XMo=s(PN);ZTt=r(XMo,"This class cannot be instantiated directly using "),FNe=n(XMo,"CODE",{});var RWa=s(FNe);KTt=r(RWa,"__init__()"),RWa.forEach(t),eFt=r(XMo," (throws an error)."),XMo.forEach(t),oFt=i(Wi),Zt=n(Wi,"DIV",{class:!0});var ER=s(Zt);T(IN.$$.fragment,ER),rFt=i(ER),MNe=n(ER,"P",{});var PWa=s(MNe);tFt=r(PWa,"Instantiates one of the model classes of the library (with a next sentence prediction head) from a configuration."),PWa.forEach(t),aFt=i(ER),Mc=n(ER,"P",{});var mbe=s(Mc);nFt=r(mbe,`Note:
Loading a model from its configuration file does `),ENe=n(mbe,"STRONG",{});var IWa=s(ENe);sFt=r(IWa,"not"),IWa.forEach(t),lFt=r(mbe,` load the model weights. It only affects the
model\u2019s configuration. Use `),ffe=n(mbe,"A",{href:!0});var BWa=s(ffe);iFt=r(BWa,"from_pretrained()"),BWa.forEach(t),dFt=r(mbe," to load the model weights."),mbe.forEach(t),mFt=i(ER),T(DA.$$.fragment,ER),ER.forEach(t),cFt=i(Wi),dt=n(Wi,"DIV",{class:!0});var Qi=s(dt);T(BN.$$.fragment,Qi),fFt=i(Qi),CNe=n(Qi,"P",{});var NWa=s(CNe);gFt=r(NWa,"Instantiate one of the model classes of the library (with a next sentence prediction head) from a pretrained model."),NWa.forEach(t),hFt=i(Qi),jn=n(Qi,"P",{});var CR=s(jn);uFt=r(CR,"The model class to instantiate is selected based on the "),wNe=n(CR,"CODE",{});var DWa=s(wNe);pFt=r(DWa,"model_type"),DWa.forEach(t),_Ft=r(CR,` property of the config object (either
passed as an argument or loaded from `),ANe=n(CR,"CODE",{});var GWa=s(ANe);bFt=r(GWa,"pretrained_model_name_or_path"),GWa.forEach(t),vFt=r(CR,` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),LNe=n(CR,"CODE",{});var qWa=s(LNe);TFt=r(qWa,"pretrained_model_name_or_path"),qWa.forEach(t),FFt=r(CR,":"),CR.forEach(t),MFt=i(Qi),yNe=n(Qi,"UL",{});var jWa=s(yNe);GA=n(jWa,"LI",{});var $uo=s(GA);xNe=n($uo,"STRONG",{});var OWa=s(xNe);EFt=r(OWa,"bert"),OWa.forEach(t),CFt=r($uo," \u2014 "),gfe=n($uo,"A",{href:!0});var VWa=s(gfe);wFt=r(VWa,"FlaxBertForNextSentencePrediction"),VWa.forEach(t),AFt=r($uo," (BERT model)"),$uo.forEach(t),jWa.forEach(t),LFt=i(Qi),T(qA.$$.fragment,Qi),Qi.forEach(t),Wi.forEach(t),cTo=i(c),Ec=n(c,"H3",{class:!0});var zMo=s(Ec);jA=n(zMo,"A",{id:!0,class:!0,href:!0});var XWa=s(jA);kNe=n(XWa,"SPAN",{});var zWa=s(kNe);T(NN.$$.fragment,zWa),zWa.forEach(t),XWa.forEach(t),yFt=i(zMo),$Ne=n(zMo,"SPAN",{});var WWa=s($Ne);xFt=r(WWa,"AutoModelForTokenClassification"),WWa.forEach(t),zMo.forEach(t),fTo=i(c),fr=n(c,"DIV",{class:!0});var Ui=s(fr);T(DN.$$.fragment,Ui),kFt=i(Ui),Cc=n(Ui,"P",{});var cbe=s(Cc);$Ft=r(cbe,`This is a generic model class that will be instantiated as one of the model classes of the library (with a token classification head) when created
with the `),hfe=n(cbe,"A",{href:!0});var QWa=s(hfe);SFt=r(QWa,"from_pretrained()"),QWa.forEach(t),RFt=r(cbe," class method or the "),ufe=n(cbe,"A",{href:!0});var UWa=s(ufe);PFt=r(UWa,"from_config()"),UWa.forEach(t),IFt=r(cbe,` class
method.`),cbe.forEach(t),BFt=i(Ui),GN=n(Ui,"P",{});var WMo=s(GN);NFt=r(WMo,"This class cannot be instantiated directly using "),SNe=n(WMo,"CODE",{});var HWa=s(SNe);DFt=r(HWa,"__init__()"),HWa.forEach(t),GFt=r(WMo," (throws an error)."),WMo.forEach(t),qFt=i(Ui),Kt=n(Ui,"DIV",{class:!0});var wR=s(Kt);T(qN.$$.fragment,wR),jFt=i(wR),RNe=n(wR,"P",{});var JWa=s(RNe);OFt=r(JWa,"Instantiates one of the model classes of the library (with a token classification head) from a configuration."),JWa.forEach(t),VFt=i(wR),wc=n(wR,"P",{});var fbe=s(wc);XFt=r(fbe,`Note:
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model\u2019s configuration. Use `),pfe=n(fbe,"A",{href:!0});var ZWa=s(pfe);QFt=r(ZWa,"from_pretrained()"),ZWa.forEach(t),UFt=r(fbe," to load the model weights."),fbe.forEach(t),HFt=i(wR),T(OA.$$.fragment,wR),wR.forEach(t),JFt=i(Ui),uo=n(Ui,"DIV",{class:!0});var Xa=s(uo);T(jN.$$.fragment,Xa),YFt=i(Xa),INe=n(Xa,"P",{});var KWa=s(INe);ZFt=r(KWa,"Instantiate one of the model classes of the library (with a token classification head) from a pretrained model."),KWa.forEach(t),KFt=i(Xa),On=n(Xa,"P",{});var AR=s(On);eMt=r(AR,"The model class to instantiate is selected based on the "),BNe=n(AR,"CODE",{});var eQa=s(BNe);oMt=r(eQa,"model_type"),eQa.forEach(t),rMt=r(AR,` property of the config object (either
passed as an argument or loaded from `),NNe=n(AR,"CODE",{});var oQa=s(NNe);tMt=r(oQa,"pretrained_model_name_or_path"),oQa.forEach(t),aMt=r(AR,` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),DNe=n(AR,"CODE",{});var rQa=s(DNe);nMt=r(rQa,"pretrained_model_name_or_path"),rQa.forEach(t),sMt=r(AR,":"),AR.forEach(t),lMt=i(Xa),Y=n(Xa,"UL",{});var Z=s(Y);VA=n(Z,"LI",{});var Suo=s(VA);GNe=n(Suo,"STRONG",{});var tQa=s(GNe);iMt=r(tQa,"albert"),tQa.forEach(t),dMt=r(Suo," \u2014 "),_fe=n(Suo,"A",{href:!0});var aQa=s(_fe);mMt=r(aQa,"AlbertForTokenClassification"),aQa.forEach(t),cMt=r(Suo," (ALBERT model)"),Suo.forEach(t),fMt=i(Z),XA=n(Z,"LI",{});var Ruo=s(XA);qNe=n(Ruo,"STRONG",{});var nQa=s(qNe);gMt=r(nQa,"bert"),nQa.forEach(t),hMt=r(Ruo," \u2014 "),bfe=n(Ruo,"A",{href:!0});var sQa=s(bfe);uMt=r(sQa,"BertForTokenClassification"),sQa.forEach(t),pMt=r(Ruo," (BERT model)"),Ruo.forEach(t),_Mt=i(Z),zA=n(Z,"LI",{});var Puo=s(zA);jNe=n(Puo,"STRONG",{});var lQa=s(jNe);bMt=r(lQa,"big_bird"),lQa.forEach(t),vMt=r(Puo," \u2014 "),vfe=n(Puo,"A",{href:!0});var iQa=s(vfe);TMt=r(iQa,"BigBirdForTokenClassification"),iQa.forEach(t),FMt=r(Puo," (BigBird 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with the `),lge=n(gbe,"A",{href:!0});var OUa=s(lge);FCt=r(OUa,"from_pretrained()"),OUa.forEach(t),MCt=r(gbe," class method or the "),ige=n(gbe,"A",{href:!0});var VUa=s(ige);ECt=r(VUa,"from_config()"),VUa.forEach(t),CCt=r(gbe,` class
method.`),gbe.forEach(t),wCt=i(Hi),XN=n(Hi,"P",{});var UMo=s(XN);ACt=r(UMo,"This class cannot be instantiated directly using "),RDe=n(UMo,"CODE",{});var XUa=s(RDe);LCt=r(XUa,"__init__()"),XUa.forEach(t),yCt=r(UMo," (throws an error)."),UMo.forEach(t),xCt=i(Hi),ea=n(Hi,"DIV",{class:!0});var LR=s(ea);T(zN.$$.fragment,LR),kCt=i(LR),PDe=n(LR,"P",{});var zUa=s(PDe);$Ct=r(zUa,"Instantiates one of the model classes of the library (with a token classification head) from a configuration."),zUa.forEach(t),SCt=i(LR),yc=n(LR,"P",{});var hbe=s(yc);RCt=r(hbe,`Note:
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model\u2019s configuration. Use `),dge=n(hbe,"A",{href:!0});var QUa=s(dge);BCt=r(QUa,"from_pretrained()"),QUa.forEach(t),NCt=r(hbe," to load the model weights."),hbe.forEach(t),DCt=i(LR),T(B7.$$.fragment,LR),LR.forEach(t),GCt=i(Hi),mt=n(Hi,"DIV",{class:!0});var Ji=s(mt);T(WN.$$.fragment,Ji),qCt=i(Ji),BDe=n(Ji,"P",{});var UUa=s(BDe);jCt=r(UUa,"Instantiate one of the model classes of the library (with a token classification head) from a pretrained model."),UUa.forEach(t),OCt=i(Ji),Vn=n(Ji,"P",{});var yR=s(Vn);VCt=r(yR,"The model class to instantiate is selected based on the "),NDe=n(yR,"CODE",{});var HUa=s(NDe);XCt=r(HUa,"model_type"),HUa.forEach(t),zCt=r(yR,` property of the config object (either
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falling back to using pattern matching on `),GDe=n(yR,"CODE",{});var YUa=s(GDe);UCt=r(YUa,"pretrained_model_name_or_path"),YUa.forEach(t),HCt=r(yR,":"),yR.forEach(t),JCt=i(Ji),he=n(Ji,"UL",{});var _e=s(he);N7=n(_e,"LI",{});var Epo=s(N7);qDe=n(Epo,"STRONG",{});var ZUa=s(qDe);YCt=r(ZUa,"albert"),ZUa.forEach(t),ZCt=r(Epo," \u2014 "),mge=n(Epo,"A",{href:!0});var KUa=s(mge);KCt=r(KUa,"TFAlbertForTokenClassification"),KUa.forEach(t),e5t=r(Epo," (ALBERT model)"),Epo.forEach(t),o5t=i(_e),D7=n(_e,"LI",{});var Cpo=s(D7);jDe=n(Cpo,"STRONG",{});var eHa=s(jDe);r5t=r(eHa,"bert"),eHa.forEach(t),t5t=r(Cpo," \u2014 "),cge=n(Cpo,"A",{href:!0});var oHa=s(cge);a5t=r(oHa,"TFBertForTokenClassification"),oHa.forEach(t),n5t=r(Cpo," (BERT model)"),Cpo.forEach(t),s5t=i(_e),G7=n(_e,"LI",{});var wpo=s(G7);ODe=n(wpo,"STRONG",{});var rHa=s(ODe);l5t=r(rHa,"camembert"),rHa.forEach(t),i5t=r(wpo," \u2014 "),fge=n(wpo,"A",{href:!0});var tHa=s(fge);d5t=r(tHa,"TFCamembertForTokenClassification"),tHa.forEach(t),m5t=r(wpo," 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kHa=s(rGe);E0t=r(kHa,"rembert"),kHa.forEach(t),C0t=r(Gpo," \u2014 "),Age=n(Gpo,"A",{href:!0});var $Ha=s(Age);w0t=r($Ha,"TFRemBertForTokenClassification"),$Ha.forEach(t),A0t=r(Gpo," (RemBERT model)"),Gpo.forEach(t),L0t=i(_e),eL=n(_e,"LI",{});var qpo=s(eL);tGe=n(qpo,"STRONG",{});var SHa=s(tGe);y0t=r(SHa,"roberta"),SHa.forEach(t),x0t=r(qpo," \u2014 "),Lge=n(qpo,"A",{href:!0});var RHa=s(Lge);k0t=r(RHa,"TFRobertaForTokenClassification"),RHa.forEach(t),$0t=r(qpo," (RoBERTa model)"),qpo.forEach(t),S0t=i(_e),oL=n(_e,"LI",{});var jpo=s(oL);aGe=n(jpo,"STRONG",{});var PHa=s(aGe);R0t=r(PHa,"roberta-prelayernorm"),PHa.forEach(t),P0t=r(jpo," \u2014 "),yge=n(jpo,"A",{href:!0});var IHa=s(yge);I0t=r(IHa,"TFRobertaPreLayerNormForTokenClassification"),IHa.forEach(t),B0t=r(jpo," (RoBERTa-PreLayerNorm model)"),jpo.forEach(t),N0t=i(_e),rL=n(_e,"LI",{});var Opo=s(rL);nGe=n(Opo,"STRONG",{});var BHa=s(nGe);D0t=r(BHa,"roformer"),BHa.forEach(t),G0t=r(Opo," \u2014 "),xge=n(Opo,"A",{href:!0});var 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with the `),Rge=n(ube,"A",{href:!0});var QHa=s(Rge);iwt=r(QHa,"from_pretrained()"),QHa.forEach(t),dwt=r(ube," class method or the "),Pge=n(ube,"A",{href:!0});var UHa=s(Pge);mwt=r(UHa,"from_config()"),UHa.forEach(t),cwt=r(ube,` class
method.`),ube.forEach(t),fwt=i(Yi),HN=n(Yi,"P",{});var JMo=s(HN);gwt=r(JMo,"This class cannot be instantiated directly using "),cGe=n(JMo,"CODE",{});var HHa=s(cGe);hwt=r(HHa,"__init__()"),HHa.forEach(t),uwt=r(JMo," (throws an error)."),JMo.forEach(t),pwt=i(Yi),oa=n(Yi,"DIV",{class:!0});var xR=s(oa);T(JN.$$.fragment,xR),_wt=i(xR),fGe=n(xR,"P",{});var JHa=s(fGe);bwt=r(JHa,"Instantiates one of the model classes of the library (with a token classification head) from a configuration."),JHa.forEach(t),vwt=i(xR),$c=n(xR,"P",{});var pbe=s($c);Twt=r(pbe,`Note:
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model\u2019s configuration. Use `),Ige=n(pbe,"A",{href:!0});var ZHa=s(Ige);Ewt=r(ZHa,"from_pretrained()"),ZHa.forEach(t),Cwt=r(pbe," to load the model weights."),pbe.forEach(t),wwt=i(xR),T(iL.$$.fragment,xR),xR.forEach(t),Awt=i(Yi),ct=n(Yi,"DIV",{class:!0});var Zi=s(ct);T(YN.$$.fragment,Zi),Lwt=i(Zi),hGe=n(Zi,"P",{});var KHa=s(hGe);ywt=r(KHa,"Instantiate one of the model classes of the library (with a token classification head) from a pretrained model."),KHa.forEach(t),xwt=i(Zi),Xn=n(Zi,"P",{});var kR=s(Xn);kwt=r(kR,"The model class to instantiate is selected based on the "),uGe=n(kR,"CODE",{});var eJa=s(uGe);$wt=r(eJa,"model_type"),eJa.forEach(t),Swt=r(kR,` property of the config object (either
passed as an argument or loaded from `),pGe=n(kR,"CODE",{});var oJa=s(pGe);Rwt=r(oJa,"pretrained_model_name_or_path"),oJa.forEach(t),Pwt=r(kR,` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),_Ge=n(kR,"CODE",{});var rJa=s(_Ge);Iwt=r(rJa,"pretrained_model_name_or_path"),rJa.forEach(t),Bwt=r(kR,":"),kR.forEach(t),Nwt=i(Zi),Ve=n(Zi,"UL",{});var Ze=s(Ve);dL=n(Ze,"LI",{});var Wpo=s(dL);bGe=n(Wpo,"STRONG",{});var tJa=s(bGe);Dwt=r(tJa,"albert"),tJa.forEach(t),Gwt=r(Wpo," \u2014 "),Bge=n(Wpo,"A",{href:!0});var aJa=s(Bge);qwt=r(aJa,"FlaxAlbertForTokenClassification"),aJa.forEach(t),jwt=r(Wpo," (ALBERT model)"),Wpo.forEach(t),Owt=i(Ze),mL=n(Ze,"LI",{});var Qpo=s(mL);vGe=n(Qpo,"STRONG",{});var nJa=s(vGe);Vwt=r(nJa,"bert"),nJa.forEach(t),Xwt=r(Qpo," \u2014 "),Nge=n(Qpo,"A",{href:!0});var sJa=s(Nge);zwt=r(sJa,"FlaxBertForTokenClassification"),sJa.forEach(t),Wwt=r(Qpo," (BERT model)"),Qpo.forEach(t),Qwt=i(Ze),cL=n(Ze,"LI",{});var Upo=s(cL);TGe=n(Upo,"STRONG",{});var lJa=s(TGe);Uwt=r(lJa,"big_bird"),lJa.forEach(t),Hwt=r(Upo," \u2014 "),Dge=n(Upo,"A",{href:!0});var iJa=s(Dge);Jwt=r(iJa,"FlaxBigBirdForTokenClassification"),iJa.forEach(t),Ywt=r(Upo," (BigBird model)"),Upo.forEach(t),Zwt=i(Ze),fL=n(Ze,"LI",{});var Hpo=s(fL);FGe=n(Hpo,"STRONG",{});var dJa=s(FGe);Kwt=r(dJa,"distilbert"),dJa.forEach(t),e6t=r(Hpo," \u2014 "),Gge=n(Hpo,"A",{href:!0});var mJa=s(Gge);o6t=r(mJa,"FlaxDistilBertForTokenClassification"),mJa.forEach(t),r6t=r(Hpo," (DistilBERT model)"),Hpo.forEach(t),t6t=i(Ze),gL=n(Ze,"LI",{});var Jpo=s(gL);MGe=n(Jpo,"STRONG",{});var cJa=s(MGe);a6t=r(cJa,"electra"),cJa.forEach(t),n6t=r(Jpo," \u2014 "),qge=n(Jpo,"A",{href:!0});var fJa=s(qge);s6t=r(fJa,"FlaxElectraForTokenClassification"),fJa.forEach(t),l6t=r(Jpo," (ELECTRA model)"),Jpo.forEach(t),i6t=i(Ze),hL=n(Ze,"LI",{});var Ypo=s(hL);EGe=n(Ypo,"STRONG",{});var gJa=s(EGe);d6t=r(gJa,"roberta"),gJa.forEach(t),m6t=r(Ypo," \u2014 "),jge=n(Ypo,"A",{href:!0});var hJa=s(jge);c6t=r(hJa,"FlaxRobertaForTokenClassification"),hJa.forEach(t),f6t=r(Ypo," (RoBERTa model)"),Ypo.forEach(t),g6t=i(Ze),uL=n(Ze,"LI",{});var Zpo=s(uL);CGe=n(Zpo,"STRONG",{});var uJa=s(CGe);h6t=r(uJa,"roberta-prelayernorm"),uJa.forEach(t),u6t=r(Zpo," \u2014 "),Oge=n(Zpo,"A",{href:!0});var pJa=s(Oge);p6t=r(pJa,"FlaxRobertaPreLayerNormForTokenClassification"),pJa.forEach(t),_6t=r(Zpo," (RoBERTa-PreLayerNorm model)"),Zpo.forEach(t),b6t=i(Ze),pL=n(Ze,"LI",{});var Kpo=s(pL);wGe=n(Kpo,"STRONG",{});var _Ja=s(wGe);v6t=r(_Ja,"roformer"),_Ja.forEach(t),T6t=r(Kpo," \u2014 "),Vge=n(Kpo,"A",{href:!0});var bJa=s(Vge);F6t=r(bJa,"FlaxRoFormerForTokenClassification"),bJa.forEach(t),M6t=r(Kpo," (RoFormer model)"),Kpo.forEach(t),E6t=i(Ze),_L=n(Ze,"LI",{});var e_o=s(_L);AGe=n(e_o,"STRONG",{});var vJa=s(AGe);C6t=r(vJa,"xlm-roberta"),vJa.forEach(t),w6t=r(e_o," \u2014 "),Xge=n(e_o,"A",{href:!0});var TJa=s(Xge);A6t=r(TJa,"FlaxXLMRobertaForTokenClassification"),TJa.forEach(t),L6t=r(e_o," (XLM-RoBERTa model)"),e_o.forEach(t),Ze.forEach(t),y6t=i(Zi),T(bL.$$.fragment,Zi),Zi.forEach(t),Yi.forEach(t),_To=i(c),Sc=n(c,"H3",{class:!0});var YMo=s(Sc);vL=n(YMo,"A",{id:!0,class:!0,href:!0});var FJa=s(vL);LGe=n(FJa,"SPAN",{});var MJa=s(LGe);T(ZN.$$.fragment,MJa),MJa.forEach(t),FJa.forEach(t),x6t=i(YMo),yGe=n(YMo,"SPAN",{});var EJa=s(yGe);k6t=r(EJa,"AutoModelForQuestionAnswering"),EJa.forEach(t),YMo.forEach(t),bTo=i(c),ur=n(c,"DIV",{class:!0});var Ki=s(ur);T(KN.$$.fragment,Ki),$6t=i(Ki),Rc=n(Ki,"P",{});var _be=s(Rc);S6t=r(_be,`This is a generic model class that will be instantiated as one of the model classes of the library (with a question answering head) when created
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method.`),_be.forEach(t),N6t=i(Ki),eD=n(Ki,"P",{});var ZMo=s(eD);D6t=r(ZMo,"This class cannot be instantiated directly using "),xGe=n(ZMo,"CODE",{});var AJa=s(xGe);G6t=r(AJa,"__init__()"),AJa.forEach(t),q6t=r(ZMo," (throws an error)."),ZMo.forEach(t),j6t=i(Ki),ra=n(Ki,"DIV",{class:!0});var $R=s(ra);T(oD.$$.fragment,$R),O6t=i($R),kGe=n($R,"P",{});var LJa=s(kGe);V6t=r(LJa,"Instantiates one of the model classes of the library (with a question answering head) from a configuration."),LJa.forEach(t),X6t=i($R),Pc=n($R,"P",{});var bbe=s(Pc);z6t=r(bbe,`Note:
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model\u2019s configuration. Use `),Whe=n(Tbe,"A",{href:!0});var BZa=s(Whe);gyt=r(BZa,"from_pretrained()"),BZa.forEach(t),hyt=r(Tbe," to load the model weights."),Tbe.forEach(t),uyt=i(RR),T(T9.$$.fragment,RR),RR.forEach(t),pyt=i(ed),ft=n(ed,"DIV",{class:!0});var od=s(ft);T(lD.$$.fragment,od),_yt=i(od),Gqe=n(od,"P",{});var NZa=s(Gqe);byt=r(NZa,"Instantiate one of the model classes of the library (with a question answering head) from a pretrained model."),NZa.forEach(t),vyt=i(od),Wn=n(od,"P",{});var PR=s(Wn);Tyt=r(PR,"The model class to instantiate is selected based on the "),qqe=n(PR,"CODE",{});var DZa=s(qqe);Fyt=r(DZa,"model_type"),DZa.forEach(t),Myt=r(PR,` property of the config object (either
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falling back to using pattern matching on `),Oqe=n(PR,"CODE",{});var qZa=s(Oqe);wyt=r(qZa,"pretrained_model_name_or_path"),qZa.forEach(t),Ayt=r(PR,":"),PR.forEach(t),Lyt=i(od),pe=n(od,"UL",{});var Te=s(pe);F9=n(Te,"LI",{});var Z_o=s(F9);Vqe=n(Z_o,"STRONG",{});var jZa=s(Vqe);yyt=r(jZa,"albert"),jZa.forEach(t),xyt=r(Z_o," \u2014 "),Qhe=n(Z_o,"A",{href:!0});var OZa=s(Qhe);kyt=r(OZa,"TFAlbertForQuestionAnswering"),OZa.forEach(t),$yt=r(Z_o," (ALBERT model)"),Z_o.forEach(t),Syt=i(Te),M9=n(Te,"LI",{});var K_o=s(M9);Xqe=n(K_o,"STRONG",{});var VZa=s(Xqe);Ryt=r(VZa,"bert"),VZa.forEach(t),Pyt=r(K_o," \u2014 "),Uhe=n(K_o,"A",{href:!0});var XZa=s(Uhe);Iyt=r(XZa,"TFBertForQuestionAnswering"),XZa.forEach(t),Byt=r(K_o," (BERT model)"),K_o.forEach(t),Nyt=i(Te),E9=n(Te,"LI",{});var e1o=s(E9);zqe=n(e1o,"STRONG",{});var zZa=s(zqe);Dyt=r(zZa,"camembert"),zZa.forEach(t),Gyt=r(e1o," \u2014 "),Hhe=n(e1o,"A",{href:!0});var WZa=s(Hhe);qyt=r(WZa,"TFCamembertForQuestionAnswering"),WZa.forEach(t),jyt=r(e1o," 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model\u2019s configuration. Use `),_ue=n(Mbe,"A",{href:!0});var GKa=s(_ue);Hkt=r(GKa,"from_pretrained()"),GKa.forEach(t),Jkt=r(Mbe," to load the model weights."),Mbe.forEach(t),Ykt=i(IR),T(z9.$$.fragment,IR),IR.forEach(t),Zkt=i(rd),gt=n(rd,"DIV",{class:!0});var td=s(gt);T(fD.$$.fragment,td),Kkt=i(td),pje=n(td,"P",{});var qKa=s(pje);e$t=r(qKa,"Instantiate one of the model classes of the library (with a question answering head) from a pretrained model."),qKa.forEach(t),o$t=i(td),Qn=n(td,"P",{});var BR=s(Qn);r$t=r(BR,"The model class to instantiate is selected based on the "),_je=n(BR,"CODE",{});var jKa=s(_je);t$t=r(jKa,"model_type"),jKa.forEach(t),a$t=r(BR,` property of the config object (either
passed as an argument or loaded from `),bje=n(BR,"CODE",{});var OKa=s(bje);n$t=r(OKa,"pretrained_model_name_or_path"),OKa.forEach(t),s$t=r(BR,` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),vje=n(BR,"CODE",{});var VKa=s(vje);l$t=r(VKa,"pretrained_model_name_or_path"),VKa.forEach(t),i$t=r(BR,":"),BR.forEach(t),d$t=i(td),Se=n(td,"UL",{});var je=s(Se);W9=n(je,"LI",{});var T1o=s(W9);Tje=n(T1o,"STRONG",{});var XKa=s(Tje);m$t=r(XKa,"albert"),XKa.forEach(t),c$t=r(T1o," \u2014 "),bue=n(T1o,"A",{href:!0});var zKa=s(bue);f$t=r(zKa,"FlaxAlbertForQuestionAnswering"),zKa.forEach(t),g$t=r(T1o," (ALBERT model)"),T1o.forEach(t),h$t=i(je),Q9=n(je,"LI",{});var F1o=s(Q9);Fje=n(F1o,"STRONG",{});var WKa=s(Fje);u$t=r(WKa,"bart"),WKa.forEach(t),p$t=r(F1o," \u2014 "),vue=n(F1o,"A",{href:!0});var QKa=s(vue);_$t=r(QKa,"FlaxBartForQuestionAnswering"),QKa.forEach(t),b$t=r(F1o," (BART model)"),F1o.forEach(t),v$t=i(je),U9=n(je,"LI",{});var M1o=s(U9);Mje=n(M1o,"STRONG",{});var UKa=s(Mje);T$t=r(UKa,"bert"),UKa.forEach(t),F$t=r(M1o," \u2014 "),Tue=n(M1o,"A",{href:!0});var HKa=s(Tue);M$t=r(HKa,"FlaxBertForQuestionAnswering"),HKa.forEach(t),E$t=r(M1o," (BERT model)"),M1o.forEach(t),C$t=i(je),H9=n(je,"LI",{});var E1o=s(H9);Eje=n(E1o,"STRONG",{});var JKa=s(Eje);w$t=r(JKa,"big_bird"),JKa.forEach(t),A$t=r(E1o," \u2014 "),Fue=n(E1o,"A",{href:!0});var YKa=s(Fue);L$t=r(YKa,"FlaxBigBirdForQuestionAnswering"),YKa.forEach(t),y$t=r(E1o," (BigBird model)"),E1o.forEach(t),x$t=i(je),J9=n(je,"LI",{});var C1o=s(J9);Cje=n(C1o,"STRONG",{});var ZKa=s(Cje);k$t=r(ZKa,"distilbert"),ZKa.forEach(t),$$t=r(C1o," \u2014 "),Mue=n(C1o,"A",{href:!0});var KKa=s(Mue);S$t=r(KKa,"FlaxDistilBertForQuestionAnswering"),KKa.forEach(t),R$t=r(C1o," (DistilBERT model)"),C1o.forEach(t),P$t=i(je),Y9=n(je,"LI",{});var w1o=s(Y9);wje=n(w1o,"STRONG",{});var een=s(wje);I$t=r(een,"electra"),een.forEach(t),B$t=r(w1o," \u2014 "),Eue=n(w1o,"A",{href:!0});var oen=s(Eue);N$t=r(oen,"FlaxElectraForQuestionAnswering"),oen.forEach(t),D$t=r(w1o," (ELECTRA model)"),w1o.forEach(t),G$t=i(je),Z9=n(je,"LI",{});var A1o=s(Z9);Aje=n(A1o,"STRONG",{});var ren=s(Aje);q$t=r(ren,"mbart"),ren.forEach(t),j$t=r(A1o," \u2014 "),Cue=n(A1o,"A",{href:!0});var ten=s(Cue);O$t=r(ten,"FlaxMBartForQuestionAnswering"),ten.forEach(t),V$t=r(A1o," (mBART model)"),A1o.forEach(t),X$t=i(je),K9=n(je,"LI",{});var L1o=s(K9);Lje=n(L1o,"STRONG",{});var aen=s(Lje);z$t=r(aen,"roberta"),aen.forEach(t),W$t=r(L1o," \u2014 "),wue=n(L1o,"A",{href:!0});var nen=s(wue);Q$t=r(nen,"FlaxRobertaForQuestionAnswering"),nen.forEach(t),U$t=r(L1o," (RoBERTa model)"),L1o.forEach(t),H$t=i(je),ey=n(je,"LI",{});var y1o=s(ey);yje=n(y1o,"STRONG",{});var sen=s(yje);J$t=r(sen,"roberta-prelayernorm"),sen.forEach(t),Y$t=r(y1o," \u2014 "),Aue=n(y1o,"A",{href:!0});var len=s(Aue);Z$t=r(len,"FlaxRobertaPreLayerNormForQuestionAnswering"),len.forEach(t),K$t=r(y1o," (RoBERTa-PreLayerNorm model)"),y1o.forEach(t),eSt=i(je),oy=n(je,"LI",{});var x1o=s(oy);xje=n(x1o,"STRONG",{});var ien=s(xje);oSt=r(ien,"roformer"),ien.forEach(t),rSt=r(x1o," \u2014 "),Lue=n(x1o,"A",{href:!0});var den=s(Lue);tSt=r(den,"FlaxRoFormerForQuestionAnswering"),den.forEach(t),aSt=r(x1o," (RoFormer model)"),x1o.forEach(t),nSt=i(je),ry=n(je,"LI",{});var k1o=s(ry);kje=n(k1o,"STRONG",{});var men=s(kje);sSt=r(men,"xlm-roberta"),men.forEach(t),lSt=r(k1o," \u2014 "),yue=n(k1o,"A",{href:!0});var cen=s(yue);iSt=r(cen,"FlaxXLMRobertaForQuestionAnswering"),cen.forEach(t),dSt=r(k1o," (XLM-RoBERTa model)"),k1o.forEach(t),je.forEach(t),mSt=i(td),T(ty.$$.fragment,td),td.forEach(t),rd.forEach(t),ETo=i(c),jc=n(c,"H2",{class:!0});var tEo=s(jc);ay=n(tEo,"A",{id:!0,class:!0,href:!0});var fen=s(ay);$je=n(fen,"SPAN",{});var gen=s($je);T(gD.$$.fragment,gen),gen.forEach(t),fen.forEach(t),cSt=i(tEo),Sje=n(tEo,"SPAN",{});var hen=s(Sje);fSt=r(hen,"Computer vision"),hen.forEach(t),tEo.forEach(t),CTo=i(c),xue=n(c,"P",{});var uen=s(xue);gSt=r(uen,"The following auto classes are available for the following computer vision tasks."),uen.forEach(t),wTo=i(c),Oc=n(c,"H3",{class:!0});var aEo=s(Oc);ny=n(aEo,"A",{id:!0,class:!0,href:!0});var pen=s(ny);Rje=n(pen,"SPAN",{});var _en=s(Rje);T(hD.$$.fragment,_en),_en.forEach(t),pen.forEach(t),hSt=i(aEo),Pje=n(aEo,"SPAN",{});var ben=s(Pje);uSt=r(ben,"AutoModelForDepthEstimation"),ben.forEach(t),aEo.forEach(t),ATo=i(c),br=n(c,"DIV",{class:!0});var ad=s(br);T(uD.$$.fragment,ad),pSt=i(ad),Vc=n(ad,"P",{});var Ebe=s(Vc);_St=r(Ebe,`This is a generic model class that will be instantiated as one of the model classes of the library (with a depth estimation head) when created
with the `),kue=n(Ebe,"A",{href:!0});var ven=s(kue);bSt=r(ven,"from_pretrained()"),ven.forEach(t),vSt=r(Ebe," class method or the "),$ue=n(Ebe,"A",{href:!0});var Ten=s($ue);TSt=r(Ten,"from_config()"),Ten.forEach(t),FSt=r(Ebe,` class
method.`),Ebe.forEach(t),MSt=i(ad),pD=n(ad,"P",{});var nEo=s(pD);ESt=r(nEo,"This class cannot be instantiated directly using "),Ije=n(nEo,"CODE",{});var Fen=s(Ije);CSt=r(Fen,"__init__()"),Fen.forEach(t),wSt=r(nEo," (throws an error)."),nEo.forEach(t),ASt=i(ad),na=n(ad,"DIV",{class:!0});var NR=s(na);T(_D.$$.fragment,NR),LSt=i(NR),Bje=n(NR,"P",{});var Men=s(Bje);ySt=r(Men,"Instantiates one of the model classes of the library (with a depth estimation head) from a configuration."),Men.forEach(t),xSt=i(NR),Xc=n(NR,"P",{});var Cbe=s(Xc);kSt=r(Cbe,`Note:
Loading a model from its configuration file does `),Nje=n(Cbe,"STRONG",{});var Een=s(Nje);$St=r(Een,"not"),Een.forEach(t),SSt=r(Cbe,` load the model weights. It only affects the
model\u2019s configuration. Use `),Sue=n(Cbe,"A",{href:!0});var Cen=s(Sue);RSt=r(Cen,"from_pretrained()"),Cen.forEach(t),PSt=r(Cbe," to load the model weights."),Cbe.forEach(t),ISt=i(NR),T(sy.$$.fragment,NR),NR.forEach(t),BSt=i(ad),_o=n(ad,"DIV",{class:!0});var Wa=s(_o);T(bD.$$.fragment,Wa),NSt=i(Wa),Dje=n(Wa,"P",{});var wen=s(Dje);DSt=r(wen,"Instantiate one of the model classes of the library (with a depth estimation head) from a pretrained model."),wen.forEach(t),GSt=i(Wa),Un=n(Wa,"P",{});var DR=s(Un);qSt=r(DR,"The model class to instantiate is selected based on the "),Gje=n(DR,"CODE",{});var Aen=s(Gje);jSt=r(Aen,"model_type"),Aen.forEach(t),OSt=r(DR,` property of the config object (either
passed as an argument or loaded from `),qje=n(DR,"CODE",{});var Len=s(qje);VSt=r(Len,"pretrained_model_name_or_path"),Len.forEach(t),XSt=r(DR,` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),jje=n(DR,"CODE",{});var yen=s(jje);zSt=r(yen,"pretrained_model_name_or_path"),yen.forEach(t),WSt=r(DR,":"),DR.forEach(t),QSt=i(Wa),vD=n(Wa,"UL",{});var sEo=s(vD);ly=n(sEo,"LI",{});var $1o=s(ly);Oje=n($1o,"STRONG",{});var xen=s(Oje);USt=r(xen,"dpt"),xen.forEach(t),HSt=r($1o," \u2014 "),Rue=n($1o,"A",{href:!0});var ken=s(Rue);JSt=r(ken,"DPTForDepthEstimation"),ken.forEach(t),YSt=r($1o," (DPT model)"),$1o.forEach(t),ZSt=i(sEo),iy=n(sEo,"LI",{});var S1o=s(iy);Vje=n(S1o,"STRONG",{});var $en=s(Vje);KSt=r($en,"glpn"),$en.forEach(t),eRt=r(S1o," \u2014 "),Pue=n(S1o,"A",{href:!0});var Sen=s(Pue);oRt=r(Sen,"GLPNForDepthEstimation"),Sen.forEach(t),rRt=r(S1o," (GLPN model)"),S1o.forEach(t),sEo.forEach(t),tRt=i(Wa),dy=n(Wa,"P",{});var R1o=s(dy);aRt=r(R1o,"The model is set in evaluation mode by default using "),Xje=n(R1o,"CODE",{});var Ren=s(Xje);nRt=r(Ren,"model.eval()"),Ren.forEach(t),sRt=r(R1o,` (so for instance, dropout modules are
deactivated). To train the model, you should first set it back in training mode with `),zje=n(R1o,"CODE",{});var Pen=s(zje);lRt=r(Pen,"model.train()"),Pen.forEach(t),R1o.forEach(t),iRt=i(Wa),T(my.$$.fragment,Wa),Wa.forEach(t),ad.forEach(t),LTo=i(c),zc=n(c,"H3",{class:!0});var lEo=s(zc);cy=n(lEo,"A",{id:!0,class:!0,href:!0});var Ien=s(cy);Wje=n(Ien,"SPAN",{});var Ben=s(Wje);T(TD.$$.fragment,Ben),Ben.forEach(t),Ien.forEach(t),dRt=i(lEo),Qje=n(lEo,"SPAN",{});var Nen=s(Qje);mRt=r(Nen,"AutoModelForImageClassification"),Nen.forEach(t),lEo.forEach(t),yTo=i(c),vr=n(c,"DIV",{class:!0});var nd=s(vr);T(FD.$$.fragment,nd),cRt=i(nd),Wc=n(nd,"P",{});var wbe=s(Wc);fRt=r(wbe,`This is a generic model class that will be instantiated as one of the model classes of the library (with a image classification head) when created
with the `),Iue=n(wbe,"A",{href:!0});var Den=s(Iue);gRt=r(Den,"from_pretrained()"),Den.forEach(t),hRt=r(wbe," class method or the "),Bue=n(wbe,"A",{href:!0});var Gen=s(Bue);uRt=r(Gen,"from_config()"),Gen.forEach(t),pRt=r(wbe,` class
method.`),wbe.forEach(t),_Rt=i(nd),MD=n(nd,"P",{});var iEo=s(MD);bRt=r(iEo,"This class cannot be instantiated directly using "),Uje=n(iEo,"CODE",{});var qen=s(Uje);vRt=r(qen,"__init__()"),qen.forEach(t),TRt=r(iEo," (throws an error)."),iEo.forEach(t),FRt=i(nd),sa=n(nd,"DIV",{class:!0});var GR=s(sa);T(ED.$$.fragment,GR),MRt=i(GR),Hje=n(GR,"P",{});var jen=s(Hje);ERt=r(jen,"Instantiates one of the model classes of the library (with a image classification head) from a configuration."),jen.forEach(t),CRt=i(GR),Qc=n(GR,"P",{});var Abe=s(Qc);wRt=r(Abe,`Note:
Loading a model from its configuration file does `),Jje=n(Abe,"STRONG",{});var Oen=s(Jje);ARt=r(Oen,"not"),Oen.forEach(t),LRt=r(Abe,` load the model weights. It only affects the
model\u2019s configuration. Use `),Nue=n(Abe,"A",{href:!0});var Ven=s(Nue);yRt=r(Ven,"from_pretrained()"),Ven.forEach(t),xRt=r(Abe," to load the model weights."),Abe.forEach(t),kRt=i(GR),T(fy.$$.fragment,GR),GR.forEach(t),$Rt=i(nd),bo=n(nd,"DIV",{class:!0});var Qa=s(bo);T(CD.$$.fragment,Qa),SRt=i(Qa),Yje=n(Qa,"P",{});var Xen=s(Yje);RRt=r(Xen,"Instantiate one of the model classes of the library (with a image classification head) from a pretrained model."),Xen.forEach(t),PRt=i(Qa),Hn=n(Qa,"P",{});var qR=s(Hn);IRt=r(qR,"The model class to instantiate is selected based on the "),Zje=n(qR,"CODE",{});var zen=s(Zje);BRt=r(zen,"model_type"),zen.forEach(t),NRt=r(qR,` property of the config object (either
passed as an argument or loaded from `),Kje=n(qR,"CODE",{});var Wen=s(Kje);DRt=r(Wen,"pretrained_model_name_or_path"),Wen.forEach(t),GRt=r(qR,` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),eOe=n(qR,"CODE",{});var Qen=s(eOe);qRt=r(Qen,"pretrained_model_name_or_path"),Qen.forEach(t),jRt=r(qR,":"),qR.forEach(t),ORt=i(Qa),ce=n(Qa,"UL",{});var ge=s(ce);gy=n(ge,"LI",{});var P1o=s(gy);oOe=n(P1o,"STRONG",{});var Uen=s(oOe);VRt=r(Uen,"beit"),Uen.forEach(t),XRt=r(P1o," \u2014 "),Due=n(P1o,"A",{href:!0});var Hen=s(Due);zRt=r(Hen,"BeitForImageClassification"),Hen.forEach(t),WRt=r(P1o," (BEiT model)"),P1o.forEach(t),QRt=i(ge),hy=n(ge,"LI",{});var I1o=s(hy);rOe=n(I1o,"STRONG",{});var Jen=s(rOe);URt=r(Jen,"bit"),Jen.forEach(t),HRt=r(I1o," \u2014 "),Gue=n(I1o,"A",{href:!0});var Yen=s(Gue);JRt=r(Yen,"BitForImageClassification"),Yen.forEach(t),YRt=r(I1o," (BiT model)"),I1o.forEach(t),ZRt=i(ge),uy=n(ge,"LI",{});var B1o=s(uy);tOe=n(B1o,"STRONG",{});var Zen=s(tOe);KRt=r(Zen,"convnext"),Zen.forEach(t),ePt=r(B1o," \u2014 "),que=n(B1o,"A",{href:!0});var Ken=s(que);oPt=r(Ken,"ConvNextForImageClassification"),Ken.forEach(t),rPt=r(B1o," (ConvNeXT model)"),B1o.forEach(t),tPt=i(ge),py=n(ge,"LI",{});var N1o=s(py);aOe=n(N1o,"STRONG",{});var eon=s(aOe);aPt=r(eon,"cvt"),eon.forEach(t),nPt=r(N1o," \u2014 "),jue=n(N1o,"A",{href:!0});var oon=s(jue);sPt=r(oon,"CvtForImageClassification"),oon.forEach(t),lPt=r(N1o," (CvT model)"),N1o.forEach(t),iPt=i(ge),_y=n(ge,"LI",{});var D1o=s(_y);nOe=n(D1o,"STRONG",{});var ron=s(nOe);dPt=r(ron,"data2vec-vision"),ron.forEach(t),mPt=r(D1o," \u2014 "),Oue=n(D1o,"A",{href:!0});var ton=s(Oue);cPt=r(ton,"Data2VecVisionForImageClassification"),ton.forEach(t),fPt=r(D1o," (Data2VecVision model)"),D1o.forEach(t),gPt=i(ge),Kl=n(ge,"LI",{});var wO=s(Kl);sOe=n(wO,"STRONG",{});var aon=s(sOe);hPt=r(aon,"deit"),aon.forEach(t),uPt=r(wO," \u2014 "),Vue=n(wO,"A",{href:!0});var non=s(Vue);pPt=r(non,"DeiTForImageClassification"),non.forEach(t),_Pt=r(wO," or "),Xue=n(wO,"A",{href:!0});var son=s(Xue);bPt=r(son,"DeiTForImageClassificationWithTeacher"),son.forEach(t),vPt=r(wO," (DeiT model)"),wO.forEach(t),TPt=i(ge),by=n(ge,"LI",{});var G1o=s(by);lOe=n(G1o,"STRONG",{});var lon=s(lOe);FPt=r(lon,"dinat"),lon.forEach(t),MPt=r(G1o," \u2014 "),zue=n(G1o,"A",{href:!0});var ion=s(zue);EPt=r(ion,"DinatForImageClassification"),ion.forEach(t),CPt=r(G1o," (DiNAT model)"),G1o.forEach(t),wPt=i(ge),ei=n(ge,"LI",{});var AO=s(ei);iOe=n(AO,"STRONG",{});var don=s(iOe);APt=r(don,"efficientformer"),don.forEach(t),LPt=r(AO," \u2014 "),Wue=n(AO,"A",{href:!0});var mon=s(Wue);yPt=r(mon,"EfficientFormerForImageClassification"),mon.forEach(t),xPt=r(AO," or "),Que=n(AO,"A",{href:!0});var con=s(Que);kPt=r(con,"EfficientFormerForImageClassificationWithTeacher"),con.forEach(t),$Pt=r(AO," (EfficientFormer model)"),AO.forEach(t),SPt=i(ge),vy=n(ge,"LI",{});var q1o=s(vy);dOe=n(q1o,"STRONG",{});var fon=s(dOe);RPt=r(fon,"imagegpt"),fon.forEach(t),PPt=r(q1o," \u2014 "),Uue=n(q1o,"A",{href:!0});var gon=s(Uue);IPt=r(gon,"ImageGPTForImageClassification"),gon.forEach(t),BPt=r(q1o," (ImageGPT model)"),q1o.forEach(t),NPt=i(ge),oi=n(ge,"LI",{});var LO=s(oi);mOe=n(LO,"STRONG",{});var hon=s(mOe);DPt=r(hon,"levit"),hon.forEach(t),GPt=r(LO," \u2014 "),Hue=n(LO,"A",{href:!0});var uon=s(Hue);qPt=r(uon,"LevitForImageClassification"),uon.forEach(t),jPt=r(LO," or "),Jue=n(LO,"A",{href:!0});var pon=s(Jue);OPt=r(pon,"LevitForImageClassificationWithTeacher"),pon.forEach(t),VPt=r(LO," (LeViT model)"),LO.forEach(t),XPt=i(ge),Ty=n(ge,"LI",{});var j1o=s(Ty);cOe=n(j1o,"STRONG",{});var _on=s(cOe);zPt=r(_on,"mobilenet_v1"),_on.forEach(t),WPt=r(j1o," \u2014 "),Yue=n(j1o,"A",{href:!0});var bon=s(Yue);QPt=r(bon,"MobileNetV1ForImageClassification"),bon.forEach(t),UPt=r(j1o," (MobileNetV1 model)"),j1o.forEach(t),HPt=i(ge),Fy=n(ge,"LI",{});var O1o=s(Fy);fOe=n(O1o,"STRONG",{});var von=s(fOe);JPt=r(von,"mobilenet_v2"),von.forEach(t),YPt=r(O1o," \u2014 "),Zue=n(O1o,"A",{href:!0});var Ton=s(Zue);ZPt=r(Ton,"MobileNetV2ForImageClassification"),Ton.forEach(t),KPt=r(O1o," (MobileNetV2 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Uon=s(gpe);gBt=r(Uon,"ViTMSNForImageClassification"),Uon.forEach(t),hBt=r(e2o," (ViTMSN model)"),e2o.forEach(t),ge.forEach(t),uBt=i(Qa),Py=n(Qa,"P",{});var o2o=s(Py);pBt=r(o2o,"The model is set in evaluation mode by default using "),AOe=n(o2o,"CODE",{});var Hon=s(AOe);_Bt=r(Hon,"model.eval()"),Hon.forEach(t),bBt=r(o2o,` (so for instance, dropout modules are
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method.`),Lbe.forEach(t),xBt=i(sd),LD=n(sd,"P",{});var mEo=s(LD);kBt=r(mEo,"This class cannot be instantiated directly using "),kOe=n(mEo,"CODE",{});var rrn=s(kOe);$Bt=r(rrn,"__init__()"),rrn.forEach(t),SBt=r(mEo," (throws an error)."),mEo.forEach(t),RBt=i(sd),ia=n(sd,"DIV",{class:!0});var jR=s(ia);T(yD.$$.fragment,jR),PBt=i(jR),$Oe=n(jR,"P",{});var trn=s($Oe);IBt=r(trn,"Instantiates one of the model classes of the library (with a image classification head) from a configuration."),trn.forEach(t),BBt=i(jR),Jc=n(jR,"P",{});var ybe=s(Jc);NBt=r(ybe,`Note:
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falling back to using pattern matching on `),BOe=n(OR,"CODE",{});var drn=s(BOe);ZBt=r(drn,"pretrained_model_name_or_path"),drn.forEach(t),KBt=r(OR,":"),OR.forEach(t),eNt=i(ld),Be=n(ld,"UL",{});var We=s(Be);Dy=n(We,"LI",{});var r2o=s(Dy);NOe=n(r2o,"STRONG",{});var mrn=s(NOe);oNt=r(mrn,"convnext"),mrn.forEach(t),rNt=r(r2o," \u2014 "),_pe=n(r2o,"A",{href:!0});var crn=s(_pe);tNt=r(crn,"TFConvNextForImageClassification"),crn.forEach(t),aNt=r(r2o," (ConvNeXT model)"),r2o.forEach(t),nNt=i(We),Gy=n(We,"LI",{});var t2o=s(Gy);DOe=n(t2o,"STRONG",{});var frn=s(DOe);sNt=r(frn,"cvt"),frn.forEach(t),lNt=r(t2o," \u2014 "),bpe=n(t2o,"A",{href:!0});var grn=s(bpe);iNt=r(grn,"TFCvtForImageClassification"),grn.forEach(t),dNt=r(t2o," (CvT model)"),t2o.forEach(t),mNt=i(We),qy=n(We,"LI",{});var a2o=s(qy);GOe=n(a2o,"STRONG",{});var hrn=s(GOe);cNt=r(hrn,"data2vec-vision"),hrn.forEach(t),fNt=r(a2o," \u2014 "),vpe=n(a2o,"A",{href:!0});var urn=s(vpe);gNt=r(urn,"TFData2VecVisionForImageClassification"),urn.forEach(t),hNt=r(a2o," (Data2VecVision model)"),a2o.forEach(t),uNt=i(We),ri=n(We,"LI",{});var yO=s(ri);qOe=n(yO,"STRONG",{});var prn=s(qOe);pNt=r(prn,"deit"),prn.forEach(t),_Nt=r(yO," \u2014 "),Tpe=n(yO,"A",{href:!0});var _rn=s(Tpe);bNt=r(_rn,"TFDeiTForImageClassification"),_rn.forEach(t),vNt=r(yO," or "),Fpe=n(yO,"A",{href:!0});var brn=s(Fpe);TNt=r(brn,"TFDeiTForImageClassificationWithTeacher"),brn.forEach(t),FNt=r(yO," (DeiT model)"),yO.forEach(t),MNt=i(We),jy=n(We,"LI",{});var n2o=s(jy);jOe=n(n2o,"STRONG",{});var vrn=s(jOe);ENt=r(vrn,"mobilevit"),vrn.forEach(t),CNt=r(n2o," \u2014 "),Mpe=n(n2o,"A",{href:!0});var Trn=s(Mpe);wNt=r(Trn,"TFMobileViTForImageClassification"),Trn.forEach(t),ANt=r(n2o," (MobileViT model)"),n2o.forEach(t),LNt=i(We),Oy=n(We,"LI",{});var s2o=s(Oy);OOe=n(s2o,"STRONG",{});var Frn=s(OOe);yNt=r(Frn,"regnet"),Frn.forEach(t),xNt=r(s2o," \u2014 "),Epe=n(s2o,"A",{href:!0});var Mrn=s(Epe);kNt=r(Mrn,"TFRegNetForImageClassification"),Mrn.forEach(t),$Nt=r(s2o," (RegNet model)"),s2o.forEach(t),SNt=i(We),Vy=n(We,"LI",{});var l2o=s(Vy);VOe=n(l2o,"STRONG",{});var Ern=s(VOe);RNt=r(Ern,"resnet"),Ern.forEach(t),PNt=r(l2o," \u2014 "),Cpe=n(l2o,"A",{href:!0});var Crn=s(Cpe);INt=r(Crn,"TFResNetForImageClassification"),Crn.forEach(t),BNt=r(l2o," (ResNet model)"),l2o.forEach(t),NNt=i(We),Xy=n(We,"LI",{});var i2o=s(Xy);XOe=n(i2o,"STRONG",{});var wrn=s(XOe);DNt=r(wrn,"segformer"),wrn.forEach(t),GNt=r(i2o," \u2014 "),wpe=n(i2o,"A",{href:!0});var Arn=s(wpe);qNt=r(Arn,"TFSegformerForImageClassification"),Arn.forEach(t),jNt=r(i2o," (SegFormer model)"),i2o.forEach(t),ONt=i(We),zy=n(We,"LI",{});var d2o=s(zy);zOe=n(d2o,"STRONG",{});var Lrn=s(zOe);VNt=r(Lrn,"swin"),Lrn.forEach(t),XNt=r(d2o," \u2014 "),Ape=n(d2o,"A",{href:!0});var yrn=s(Ape);zNt=r(yrn,"TFSwinForImageClassification"),yrn.forEach(t),WNt=r(d2o," (Swin Transformer model)"),d2o.forEach(t),QNt=i(We),Wy=n(We,"LI",{});var m2o=s(Wy);WOe=n(m2o,"STRONG",{});var xrn=s(WOe);UNt=r(xrn,"vit"),xrn.forEach(t),HNt=r(m2o," \u2014 "),Lpe=n(m2o,"A",{href:!0});var krn=s(Lpe);JNt=r(krn,"TFViTForImageClassification"),krn.forEach(t),YNt=r(m2o," (ViT model)"),m2o.forEach(t),We.forEach(t),ZNt=i(ld),T(Qy.$$.fragment,ld),ld.forEach(t),sd.forEach(t),$To=i(c),Yc=n(c,"H3",{class:!0});var cEo=s(Yc);Uy=n(cEo,"A",{id:!0,class:!0,href:!0});var $rn=s(Uy);QOe=n($rn,"SPAN",{});var Srn=s(QOe);T(kD.$$.fragment,Srn),Srn.forEach(t),$rn.forEach(t),KNt=i(cEo),UOe=n(cEo,"SPAN",{});var Rrn=s(UOe);eDt=r(Rrn,"FlaxAutoModelForImageClassification"),Rrn.forEach(t),cEo.forEach(t),STo=i(c),Fr=n(c,"DIV",{class:!0});var id=s(Fr);T($D.$$.fragment,id),oDt=i(id),Zc=n(id,"P",{});var xbe=s(Zc);rDt=r(xbe,`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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falling back to using pattern matching on `),oVe=n(XR,"CODE",{});var Vrn=s(oVe);kDt=r(Vrn,"pretrained_model_name_or_path"),Vrn.forEach(t),$Dt=r(XR,":"),XR.forEach(t),SDt=i(dd),ID=n(dd,"UL",{});var gEo=s(ID);Jy=n(gEo,"LI",{});var c2o=s(Jy);rVe=n(c2o,"STRONG",{});var Xrn=s(rVe);RDt=r(Xrn,"beit"),Xrn.forEach(t),PDt=r(c2o," \u2014 "),$pe=n(c2o,"A",{href:!0});var zrn=s($pe);IDt=r(zrn,"FlaxBeitForImageClassification"),zrn.forEach(t),BDt=r(c2o," (BEiT model)"),c2o.forEach(t),NDt=i(gEo),Yy=n(gEo,"LI",{});var f2o=s(Yy);tVe=n(f2o,"STRONG",{});var Wrn=s(tVe);DDt=r(Wrn,"vit"),Wrn.forEach(t),GDt=r(f2o," \u2014 "),Spe=n(f2o,"A",{href:!0});var Qrn=s(Spe);qDt=r(Qrn,"FlaxViTForImageClassification"),Qrn.forEach(t),jDt=r(f2o," (ViT model)"),f2o.forEach(t),gEo.forEach(t),ODt=i(dd),T(Zy.$$.fragment,dd),dd.forEach(t),id.forEach(t),RTo=i(c),ef=n(c,"H3",{class:!0});var hEo=s(ef);Ky=n(hEo,"A",{id:!0,class:!0,href:!0});var Urn=s(Ky);aVe=n(Urn,"SPAN",{});var Hrn=s(aVe);T(BD.$$.fragment,Hrn),Hrn.forEach(t),Urn.forEach(t),VDt=i(hEo),nVe=n(hEo,"SPAN",{});var Jrn=s(nVe);XDt=r(Jrn,"AutoModelForVideoClassification"),Jrn.forEach(t),hEo.forEach(t),PTo=i(c),Mr=n(c,"DIV",{class:!0});var md=s(Mr);T(ND.$$.fragment,md),zDt=i(md),of=n(md,"P",{});var $be=s(of);WDt=r($be,`This is a generic model class that will be instantiated as one of the model classes of the library (with a video classification head) when created
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method.`),$be.forEach(t),YDt=i(md),DD=n(md,"P",{});var uEo=s(DD);ZDt=r(uEo,"This class cannot be instantiated directly using "),sVe=n(uEo,"CODE",{});var Krn=s(sVe);KDt=r(Krn,"__init__()"),Krn.forEach(t),eGt=r(uEo," (throws an error)."),uEo.forEach(t),oGt=i(md),ma=n(md,"DIV",{class:!0});var zR=s(ma);T(GD.$$.fragment,zR),rGt=i(zR),lVe=n(zR,"P",{});var etn=s(lVe);tGt=r(etn,"Instantiates one of the model classes of the library (with a video classification head) from a configuration."),etn.forEach(t),aGt=i(zR),rf=n(zR,"P",{});var Sbe=s(rf);nGt=r(Sbe,`Note:
Loading a model from its configuration file does `),iVe=n(Sbe,"STRONG",{});var otn=s(iVe);sGt=r(otn,"not"),otn.forEach(t),lGt=r(Sbe,` load the model weights. It only affects the
model\u2019s configuration. Use `),Ipe=n(Sbe,"A",{href:!0});var rtn=s(Ipe);iGt=r(rtn,"from_pretrained()"),rtn.forEach(t),dGt=r(Sbe," to load the model weights."),Sbe.forEach(t),mGt=i(zR),T(ex.$$.fragment,zR),zR.forEach(t),cGt=i(md),vo=n(md,"DIV",{class:!0});var Ua=s(vo);T(qD.$$.fragment,Ua),fGt=i(Ua),dVe=n(Ua,"P",{});var ttn=s(dVe);gGt=r(ttn,"Instantiate one of the model classes of the library (with a video classification head) from a pretrained model."),ttn.forEach(t),hGt=i(Ua),Zn=n(Ua,"P",{});var WR=s(Zn);uGt=r(WR,"The model class to instantiate is selected based on the "),mVe=n(WR,"CODE",{});var atn=s(mVe);pGt=r(atn,"model_type"),atn.forEach(t),_Gt=r(WR,` property of the config object (either
passed as an argument or loaded from `),cVe=n(WR,"CODE",{});var ntn=s(cVe);bGt=r(ntn,"pretrained_model_name_or_path"),ntn.forEach(t),vGt=r(WR,` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),fVe=n(WR,"CODE",{});var stn=s(fVe);TGt=r(stn,"pretrained_model_name_or_path"),stn.forEach(t),FGt=r(WR,":"),WR.forEach(t),MGt=i(Ua),jD=n(Ua,"UL",{});var pEo=s(jD);ox=n(pEo,"LI",{});var g2o=s(ox);gVe=n(g2o,"STRONG",{});var ltn=s(gVe);EGt=r(ltn,"timesformer"),ltn.forEach(t),CGt=r(g2o," \u2014 "),Bpe=n(g2o,"A",{href:!0});var itn=s(Bpe);wGt=r(itn,"TimesformerForVideoClassification"),itn.forEach(t),AGt=r(g2o," (TimeSformer model)"),g2o.forEach(t),LGt=i(pEo),rx=n(pEo,"LI",{});var h2o=s(rx);hVe=n(h2o,"STRONG",{});var dtn=s(hVe);yGt=r(dtn,"videomae"),dtn.forEach(t),xGt=r(h2o," \u2014 "),Npe=n(h2o,"A",{href:!0});var mtn=s(Npe);kGt=r(mtn,"VideoMAEForVideoClassification"),mtn.forEach(t),$Gt=r(h2o," (VideoMAE model)"),h2o.forEach(t),pEo.forEach(t),SGt=i(Ua),tx=n(Ua,"P",{});var u2o=s(tx);RGt=r(u2o,"The model is set in evaluation mode by default using "),uVe=n(u2o,"CODE",{});var ctn=s(uVe);PGt=r(ctn,"model.eval()"),ctn.forEach(t),IGt=r(u2o,` (so for instance, dropout modules are
deactivated). To train the model, you should first set it back in training mode with `),pVe=n(u2o,"CODE",{});var ftn=s(pVe);BGt=r(ftn,"model.train()"),ftn.forEach(t),u2o.forEach(t),NGt=i(Ua),T(ax.$$.fragment,Ua),Ua.forEach(t),md.forEach(t),ITo=i(c),tf=n(c,"H3",{class:!0});var _Eo=s(tf);nx=n(_Eo,"A",{id:!0,class:!0,href:!0});var gtn=s(nx);_Ve=n(gtn,"SPAN",{});var htn=s(_Ve);T(OD.$$.fragment,htn),htn.forEach(t),gtn.forEach(t),DGt=i(_Eo),bVe=n(_Eo,"SPAN",{});var utn=s(bVe);GGt=r(utn,"AutoModelForMaskedImageModeling"),utn.forEach(t),_Eo.forEach(t),BTo=i(c),Er=n(c,"DIV",{class:!0});var cd=s(Er);T(VD.$$.fragment,cd),qGt=i(cd),af=n(cd,"P",{});var Rbe=s(af);jGt=r(Rbe,`This is a generic model class that will be instantiated as one of the model classes of the library (with a masked image modeling head) when created
with the `),Dpe=n(Rbe,"A",{href:!0});var ptn=s(Dpe);OGt=r(ptn,"from_pretrained()"),ptn.forEach(t),VGt=r(Rbe," class method or the "),Gpe=n(Rbe,"A",{href:!0});var _tn=s(Gpe);XGt=r(_tn,"from_config()"),_tn.forEach(t),zGt=r(Rbe,` class
method.`),Rbe.forEach(t),WGt=i(cd),XD=n(cd,"P",{});var bEo=s(XD);QGt=r(bEo,"This class cannot be instantiated directly using "),vVe=n(bEo,"CODE",{});var btn=s(vVe);UGt=r(btn,"__init__()"),btn.forEach(t),HGt=r(bEo," (throws an error)."),bEo.forEach(t),JGt=i(cd),ca=n(cd,"DIV",{class:!0});var QR=s(ca);T(zD.$$.fragment,QR),YGt=i(QR),TVe=n(QR,"P",{});var vtn=s(TVe);ZGt=r(vtn,"Instantiates one of the model classes of the library (with a masked image modeling head) from a configuration."),vtn.forEach(t),KGt=i(QR),nf=n(QR,"P",{});var Pbe=s(nf);eqt=r(Pbe,`Note:
Loading a model from its configuration file does `),FVe=n(Pbe,"STRONG",{});var Ttn=s(FVe);oqt=r(Ttn,"not"),Ttn.forEach(t),rqt=r(Pbe,` load the model weights. It only affects the
model\u2019s configuration. Use `),qpe=n(Pbe,"A",{href:!0});var Ftn=s(qpe);tqt=r(Ftn,"from_pretrained()"),Ftn.forEach(t),aqt=r(Pbe," to load the model weights."),Pbe.forEach(t),nqt=i(QR),T(sx.$$.fragment,QR),QR.forEach(t),sqt=i(cd),To=n(cd,"DIV",{class:!0});var Ha=s(To);T(WD.$$.fragment,Ha),lqt=i(Ha),MVe=n(Ha,"P",{});var Mtn=s(MVe);iqt=r(Mtn,"Instantiate one of the model classes of the library (with a masked image modeling head) from a pretrained model."),Mtn.forEach(t),dqt=i(Ha),Kn=n(Ha,"P",{});var UR=s(Kn);mqt=r(UR,"The model class to instantiate is selected based on the "),EVe=n(UR,"CODE",{});var Etn=s(EVe);cqt=r(Etn,"model_type"),Etn.forEach(t),fqt=r(UR,` property of the config object (either
passed as an argument or loaded from `),CVe=n(UR,"CODE",{});var Ctn=s(CVe);gqt=r(Ctn,"pretrained_model_name_or_path"),Ctn.forEach(t),hqt=r(UR,` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),wVe=n(UR,"CODE",{});var wtn=s(wVe);uqt=r(wtn,"pretrained_model_name_or_path"),wtn.forEach(t),pqt=r(UR,":"),UR.forEach(t),_qt=i(Ha),es=n(Ha,"UL",{});var HR=s(es);lx=n(HR,"LI",{});var p2o=s(lx);AVe=n(p2o,"STRONG",{});var Atn=s(AVe);bqt=r(Atn,"deit"),Atn.forEach(t),vqt=r(p2o," \u2014 "),jpe=n(p2o,"A",{href:!0});var Ltn=s(jpe);Tqt=r(Ltn,"DeiTForMaskedImageModeling"),Ltn.forEach(t),Fqt=r(p2o," (DeiT model)"),p2o.forEach(t),Mqt=i(HR),ix=n(HR,"LI",{});var _2o=s(ix);LVe=n(_2o,"STRONG",{});var ytn=s(LVe);Eqt=r(ytn,"swin"),ytn.forEach(t),Cqt=r(_2o," \u2014 "),Ope=n(_2o,"A",{href:!0});var xtn=s(Ope);wqt=r(xtn,"SwinForMaskedImageModeling"),xtn.forEach(t),Aqt=r(_2o," (Swin Transformer model)"),_2o.forEach(t),Lqt=i(HR),dx=n(HR,"LI",{});var b2o=s(dx);yVe=n(b2o,"STRONG",{});var ktn=s(yVe);yqt=r(ktn,"swinv2"),ktn.forEach(t),xqt=r(b2o," \u2014 "),Vpe=n(b2o,"A",{href:!0});var $tn=s(Vpe);kqt=r($tn,"Swinv2ForMaskedImageModeling"),$tn.forEach(t),$qt=r(b2o," (Swin Transformer V2 model)"),b2o.forEach(t),Sqt=i(HR),mx=n(HR,"LI",{});var v2o=s(mx);xVe=n(v2o,"STRONG",{});var Stn=s(xVe);Rqt=r(Stn,"vit"),Stn.forEach(t),Pqt=r(v2o," \u2014 "),Xpe=n(v2o,"A",{href:!0});var Rtn=s(Xpe);Iqt=r(Rtn,"ViTForMaskedImageModeling"),Rtn.forEach(t),Bqt=r(v2o," (ViT model)"),v2o.forEach(t),HR.forEach(t),Nqt=i(Ha),cx=n(Ha,"P",{});var T2o=s(cx);Dqt=r(T2o,"The model is set in evaluation mode by default using "),kVe=n(T2o,"CODE",{});var Ptn=s(kVe);Gqt=r(Ptn,"model.eval()"),Ptn.forEach(t),qqt=r(T2o,` (so for instance, dropout modules are
deactivated). To train the model, you should first set it back in training mode with `),$Ve=n(T2o,"CODE",{});var Itn=s($Ve);jqt=r(Itn,"model.train()"),Itn.forEach(t),T2o.forEach(t),Oqt=i(Ha),T(fx.$$.fragment,Ha),Ha.forEach(t),cd.forEach(t),NTo=i(c),sf=n(c,"H3",{class:!0});var vEo=s(sf);gx=n(vEo,"A",{id:!0,class:!0,href:!0});var Btn=s(gx);SVe=n(Btn,"SPAN",{});var Ntn=s(SVe);T(QD.$$.fragment,Ntn),Ntn.forEach(t),Btn.forEach(t),Vqt=i(vEo),RVe=n(vEo,"SPAN",{});var Dtn=s(RVe);Xqt=r(Dtn,"AutoModelForObjectDetection"),Dtn.forEach(t),vEo.forEach(t),DTo=i(c),Cr=n(c,"DIV",{class:!0});var fd=s(Cr);T(UD.$$.fragment,fd),zqt=i(fd),lf=n(fd,"P",{});var Ibe=s(lf);Wqt=r(Ibe,`This is a generic model class that will be instantiated as one of the model classes of the library (with a object detection head) when created
with the `),zpe=n(Ibe,"A",{href:!0});var Gtn=s(zpe);Qqt=r(Gtn,"from_pretrained()"),Gtn.forEach(t),Uqt=r(Ibe," class method or the "),Wpe=n(Ibe,"A",{href:!0});var qtn=s(Wpe);Hqt=r(qtn,"from_config()"),qtn.forEach(t),Jqt=r(Ibe,` class
method.`),Ibe.forEach(t),Yqt=i(fd),HD=n(fd,"P",{});var TEo=s(HD);Zqt=r(TEo,"This class cannot be instantiated directly using "),PVe=n(TEo,"CODE",{});var jtn=s(PVe);Kqt=r(jtn,"__init__()"),jtn.forEach(t),ejt=r(TEo," (throws an error)."),TEo.forEach(t),ojt=i(fd),fa=n(fd,"DIV",{class:!0});var JR=s(fa);T(JD.$$.fragment,JR),rjt=i(JR),IVe=n(JR,"P",{});var Otn=s(IVe);tjt=r(Otn,"Instantiates one of the model classes of the library (with a object detection head) from a configuration."),Otn.forEach(t),ajt=i(JR),df=n(JR,"P",{});var Bbe=s(df);njt=r(Bbe,`Note:
Loading a model from its configuration file does `),BVe=n(Bbe,"STRONG",{});var Vtn=s(BVe);sjt=r(Vtn,"not"),Vtn.forEach(t),ljt=r(Bbe,` load the model weights. It only affects the
model\u2019s configuration. Use `),Qpe=n(Bbe,"A",{href:!0});var Xtn=s(Qpe);ijt=r(Xtn,"from_pretrained()"),Xtn.forEach(t),djt=r(Bbe," to load the model weights."),Bbe.forEach(t),mjt=i(JR),T(hx.$$.fragment,JR),JR.forEach(t),cjt=i(fd),Fo=n(fd,"DIV",{class:!0});var Ja=s(Fo);T(YD.$$.fragment,Ja),fjt=i(Ja),NVe=n(Ja,"P",{});var ztn=s(NVe);gjt=r(ztn,"Instantiate one of the model classes of the library (with a object detection head) from a pretrained model."),ztn.forEach(t),hjt=i(Ja),os=n(Ja,"P",{});var YR=s(os);ujt=r(YR,"The model class to instantiate is selected based on the "),DVe=n(YR,"CODE",{});var Wtn=s(DVe);pjt=r(Wtn,"model_type"),Wtn.forEach(t),_jt=r(YR,` property of the config object (either
passed as an argument or loaded from `),GVe=n(YR,"CODE",{});var Qtn=s(GVe);bjt=r(Qtn,"pretrained_model_name_or_path"),Qtn.forEach(t),vjt=r(YR,` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),qVe=n(YR,"CODE",{});var Utn=s(qVe);Tjt=r(Utn,"pretrained_model_name_or_path"),Utn.forEach(t),Fjt=r(YR,":"),YR.forEach(t),Mjt=i(Ja),wr=n(Ja,"UL",{});var Ya=s(wr);ux=n(Ya,"LI",{});var F2o=s(ux);jVe=n(F2o,"STRONG",{});var Htn=s(jVe);Ejt=r(Htn,"conditional_detr"),Htn.forEach(t),Cjt=r(F2o," \u2014 "),Upe=n(F2o,"A",{href:!0});var Jtn=s(Upe);wjt=r(Jtn,"ConditionalDetrForObjectDetection"),Jtn.forEach(t),Ajt=r(F2o," (Conditional DETR model)"),F2o.forEach(t),Ljt=i(Ya),px=n(Ya,"LI",{});var M2o=s(px);OVe=n(M2o,"STRONG",{});var Ytn=s(OVe);yjt=r(Ytn,"deformable_detr"),Ytn.forEach(t),xjt=r(M2o," \u2014 "),Hpe=n(M2o,"A",{href:!0});var Ztn=s(Hpe);kjt=r(Ztn,"DeformableDetrForObjectDetection"),Ztn.forEach(t),$jt=r(M2o," (Deformable DETR model)"),M2o.forEach(t),Sjt=i(Ya),_x=n(Ya,"LI",{});var E2o=s(_x);VVe=n(E2o,"STRONG",{});var Ktn=s(VVe);Rjt=r(Ktn,"deta"),Ktn.forEach(t),Pjt=r(E2o," \u2014 "),Jpe=n(E2o,"A",{href:!0});var ean=s(Jpe);Ijt=r(ean,"DetaForObjectDetection"),ean.forEach(t),Bjt=r(E2o," (DETA model)"),E2o.forEach(t),Njt=i(Ya),bx=n(Ya,"LI",{});var C2o=s(bx);XVe=n(C2o,"STRONG",{});var oan=s(XVe);Djt=r(oan,"detr"),oan.forEach(t),Gjt=r(C2o," \u2014 "),Ype=n(C2o,"A",{href:!0});var ran=s(Ype);qjt=r(ran,"DetrForObjectDetection"),ran.forEach(t),jjt=r(C2o," (DETR model)"),C2o.forEach(t),Ojt=i(Ya),vx=n(Ya,"LI",{});var w2o=s(vx);zVe=n(w2o,"STRONG",{});var tan=s(zVe);Vjt=r(tan,"table-transformer"),tan.forEach(t),Xjt=r(w2o," \u2014 "),Zpe=n(w2o,"A",{href:!0});var aan=s(Zpe);zjt=r(aan,"TableTransformerForObjectDetection"),aan.forEach(t),Wjt=r(w2o," (Table Transformer model)"),w2o.forEach(t),Qjt=i(Ya),Tx=n(Ya,"LI",{});var A2o=s(Tx);WVe=n(A2o,"STRONG",{});var nan=s(WVe);Ujt=r(nan,"yolos"),nan.forEach(t),Hjt=r(A2o," \u2014 "),Kpe=n(A2o,"A",{href:!0});var san=s(Kpe);Jjt=r(san,"YolosForObjectDetection"),san.forEach(t),Yjt=r(A2o," (YOLOS model)"),A2o.forEach(t),Ya.forEach(t),Zjt=i(Ja),Fx=n(Ja,"P",{});var L2o=s(Fx);Kjt=r(L2o,"The model is set in evaluation mode by default using "),QVe=n(L2o,"CODE",{});var lan=s(QVe);eOt=r(lan,"model.eval()"),lan.forEach(t),oOt=r(L2o,` (so for instance, dropout modules are
deactivated). To train the model, you should first set it back in training mode with `),UVe=n(L2o,"CODE",{});var ian=s(UVe);rOt=r(ian,"model.train()"),ian.forEach(t),L2o.forEach(t),tOt=i(Ja),T(Mx.$$.fragment,Ja),Ja.forEach(t),fd.forEach(t),GTo=i(c),mf=n(c,"H3",{class:!0});var FEo=s(mf);Ex=n(FEo,"A",{id:!0,class:!0,href:!0});var dan=s(Ex);HVe=n(dan,"SPAN",{});var man=s(HVe);T(ZD.$$.fragment,man),man.forEach(t),dan.forEach(t),aOt=i(FEo),JVe=n(FEo,"SPAN",{});var can=s(JVe);nOt=r(can,"AutoModelForImageSegmentation"),can.forEach(t),FEo.forEach(t),qTo=i(c),Ar=n(c,"DIV",{class:!0});var gd=s(Ar);T(KD.$$.fragment,gd),sOt=i(gd),cf=n(gd,"P",{});var Nbe=s(cf);lOt=r(Nbe,`This is a generic model class that will be instantiated as one of the model classes of the library (with a image segmentation head) when created
with the `),e_e=n(Nbe,"A",{href:!0});var fan=s(e_e);iOt=r(fan,"from_pretrained()"),fan.forEach(t),dOt=r(Nbe," class method or the "),o_e=n(Nbe,"A",{href:!0});var gan=s(o_e);mOt=r(gan,"from_config()"),gan.forEach(t),cOt=r(Nbe,` class
method.`),Nbe.forEach(t),fOt=i(gd),eG=n(gd,"P",{});var MEo=s(eG);gOt=r(MEo,"This class cannot be instantiated directly using "),YVe=n(MEo,"CODE",{});var han=s(YVe);hOt=r(han,"__init__()"),han.forEach(t),uOt=r(MEo," (throws an error)."),MEo.forEach(t),pOt=i(gd),ga=n(gd,"DIV",{class:!0});var ZR=s(ga);T(oG.$$.fragment,ZR),_Ot=i(ZR),ZVe=n(ZR,"P",{});var uan=s(ZVe);bOt=r(uan,"Instantiates one of the model classes of the library (with a image segmentation head) from a configuration."),uan.forEach(t),vOt=i(ZR),ff=n(ZR,"P",{});var Dbe=s(ff);TOt=r(Dbe,`Note:
Loading a model from its configuration file does `),KVe=n(Dbe,"STRONG",{});var pan=s(KVe);FOt=r(pan,"not"),pan.forEach(t),MOt=r(Dbe,` load the model weights. It only affects the
model\u2019s configuration. Use `),r_e=n(Dbe,"A",{href:!0});var _an=s(r_e);EOt=r(_an,"from_pretrained()"),_an.forEach(t),COt=r(Dbe," to load the model weights."),Dbe.forEach(t),wOt=i(ZR),T(Cx.$$.fragment,ZR),ZR.forEach(t),AOt=i(gd),Mo=n(gd,"DIV",{class:!0});var Za=s(Mo);T(rG.$$.fragment,Za),LOt=i(Za),eXe=n(Za,"P",{});var ban=s(eXe);yOt=r(ban,"Instantiate one of the model classes of the library (with a image segmentation head) from a pretrained model."),ban.forEach(t),xOt=i(Za),rs=n(Za,"P",{});var KR=s(rs);kOt=r(KR,"The model class to instantiate is selected based on the "),oXe=n(KR,"CODE",{});var van=s(oXe);$Ot=r(van,"model_type"),van.forEach(t),SOt=r(KR,` property of the config object (either
passed as an argument or loaded from `),rXe=n(KR,"CODE",{});var Tan=s(rXe);ROt=r(Tan,"pretrained_model_name_or_path"),Tan.forEach(t),POt=r(KR,` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),tXe=n(KR,"CODE",{});var Fan=s(tXe);IOt=r(Fan,"pretrained_model_name_or_path"),Fan.forEach(t),BOt=r(KR,":"),KR.forEach(t),NOt=i(Za),aXe=n(Za,"UL",{});var Man=s(aXe);wx=n(Man,"LI",{});var y2o=s(wx);nXe=n(y2o,"STRONG",{});var Ean=s(nXe);DOt=r(Ean,"detr"),Ean.forEach(t),GOt=r(y2o," \u2014 "),t_e=n(y2o,"A",{href:!0});var Can=s(t_e);qOt=r(Can,"DetrForSegmentation"),Can.forEach(t),jOt=r(y2o," (DETR model)"),y2o.forEach(t),Man.forEach(t),OOt=i(Za),Ax=n(Za,"P",{});var x2o=s(Ax);VOt=r(x2o,"The model is set in evaluation mode by default using "),sXe=n(x2o,"CODE",{});var wan=s(sXe);XOt=r(wan,"model.eval()"),wan.forEach(t),zOt=r(x2o,` (so for instance, dropout modules are
deactivated). To train the model, you should first set it back in training mode with `),lXe=n(x2o,"CODE",{});var Aan=s(lXe);WOt=r(Aan,"model.train()"),Aan.forEach(t),x2o.forEach(t),QOt=i(Za),T(Lx.$$.fragment,Za),Za.forEach(t),gd.forEach(t),jTo=i(c),gf=n(c,"H3",{class:!0});var EEo=s(gf);yx=n(EEo,"A",{id:!0,class:!0,href:!0});var Lan=s(yx);iXe=n(Lan,"SPAN",{});var yan=s(iXe);T(tG.$$.fragment,yan),yan.forEach(t),Lan.forEach(t),UOt=i(EEo),dXe=n(EEo,"SPAN",{});var xan=s(dXe);HOt=r(xan,"AutoModelForSemanticSegmentation"),xan.forEach(t),EEo.forEach(t),OTo=i(c),Lr=n(c,"DIV",{class:!0});var hd=s(Lr);T(aG.$$.fragment,hd),JOt=i(hd),hf=n(hd,"P",{});var Gbe=s(hf);YOt=r(Gbe,`This is a generic model class that will be instantiated as one of the model classes of the library (with a semantic segmentation head) when created
with the `),a_e=n(Gbe,"A",{href:!0});var kan=s(a_e);ZOt=r(kan,"from_pretrained()"),kan.forEach(t),KOt=r(Gbe," class method or the "),n_e=n(Gbe,"A",{href:!0});var $an=s(n_e);eVt=r($an,"from_config()"),$an.forEach(t),oVt=r(Gbe,` class
method.`),Gbe.forEach(t),rVt=i(hd),nG=n(hd,"P",{});var CEo=s(nG);tVt=r(CEo,"This class cannot be instantiated directly using "),mXe=n(CEo,"CODE",{});var San=s(mXe);aVt=r(San,"__init__()"),San.forEach(t),nVt=r(CEo," (throws an error)."),CEo.forEach(t),sVt=i(hd),ha=n(hd,"DIV",{class:!0});var eP=s(ha);T(sG.$$.fragment,eP),lVt=i(eP),cXe=n(eP,"P",{});var Ran=s(cXe);iVt=r(Ran,"Instantiates one of the model classes of the library (with a semantic segmentation head) from a configuration."),Ran.forEach(t),dVt=i(eP),uf=n(eP,"P",{});var qbe=s(uf);mVt=r(qbe,`Note:
Loading a model from its configuration file does `),fXe=n(qbe,"STRONG",{});var Pan=s(fXe);cVt=r(Pan,"not"),Pan.forEach(t),fVt=r(qbe,` load the model weights. It only affects the
model\u2019s configuration. Use `),s_e=n(qbe,"A",{href:!0});var Ian=s(s_e);gVt=r(Ian,"from_pretrained()"),Ian.forEach(t),hVt=r(qbe," to load the model weights."),qbe.forEach(t),uVt=i(eP),T(xx.$$.fragment,eP),eP.forEach(t),pVt=i(hd),Eo=n(hd,"DIV",{class:!0});var Ka=s(Eo);T(lG.$$.fragment,Ka),_Vt=i(Ka),gXe=n(Ka,"P",{});var Ban=s(gXe);bVt=r(Ban,"Instantiate one of the model classes of the library (with a semantic segmentation head) from a pretrained model."),Ban.forEach(t),vVt=i(Ka),ts=n(Ka,"P",{});var oP=s(ts);TVt=r(oP,"The model class to instantiate is selected based on the "),hXe=n(oP,"CODE",{});var Nan=s(hXe);FVt=r(Nan,"model_type"),Nan.forEach(t),MVt=r(oP,` property of the config object (either
passed as an argument or loaded from `),uXe=n(oP,"CODE",{});var Dan=s(uXe);EVt=r(Dan,"pretrained_model_name_or_path"),Dan.forEach(t),CVt=r(oP,` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),pXe=n(oP,"CODE",{});var Gan=s(pXe);wVt=r(Gan,"pretrained_model_name_or_path"),Gan.forEach(t),AVt=r(oP,":"),oP.forEach(t),LVt=i(Ka),eo=n(Ka,"UL",{});var Et=s(eo);kx=n(Et,"LI",{});var k2o=s(kx);_Xe=n(k2o,"STRONG",{});var qan=s(_Xe);yVt=r(qan,"beit"),qan.forEach(t),xVt=r(k2o," \u2014 "),l_e=n(k2o,"A",{href:!0});var jan=s(l_e);kVt=r(jan,"BeitForSemanticSegmentation"),jan.forEach(t),$Vt=r(k2o," (BEiT model)"),k2o.forEach(t),SVt=i(Et),$x=n(Et,"LI",{});var $2o=s($x);bXe=n($2o,"STRONG",{});var Oan=s(bXe);RVt=r(Oan,"data2vec-vision"),Oan.forEach(t),PVt=r($2o," \u2014 "),i_e=n($2o,"A",{href:!0});var Van=s(i_e);IVt=r(Van,"Data2VecVisionForSemanticSegmentation"),Van.forEach(t),BVt=r($2o," (Data2VecVision model)"),$2o.forEach(t),NVt=i(Et),Sx=n(Et,"LI",{});var S2o=s(Sx);vXe=n(S2o,"STRONG",{});var Xan=s(vXe);DVt=r(Xan,"dpt"),Xan.forEach(t),GVt=r(S2o," \u2014 "),d_e=n(S2o,"A",{href:!0});var zan=s(d_e);qVt=r(zan,"DPTForSemanticSegmentation"),zan.forEach(t),jVt=r(S2o," (DPT model)"),S2o.forEach(t),OVt=i(Et),Rx=n(Et,"LI",{});var R2o=s(Rx);TXe=n(R2o,"STRONG",{});var Wan=s(TXe);VVt=r(Wan,"mobilenet_v2"),Wan.forEach(t),XVt=r(R2o," \u2014 "),m_e=n(R2o,"A",{href:!0});var Qan=s(m_e);zVt=r(Qan,"MobileNetV2ForSemanticSegmentation"),Qan.forEach(t),WVt=r(R2o," (MobileNetV2 model)"),R2o.forEach(t),QVt=i(Et),Px=n(Et,"LI",{});var P2o=s(Px);FXe=n(P2o,"STRONG",{});var Uan=s(FXe);UVt=r(Uan,"mobilevit"),Uan.forEach(t),HVt=r(P2o," \u2014 "),c_e=n(P2o,"A",{href:!0});var Han=s(c_e);JVt=r(Han,"MobileViTForSemanticSegmentation"),Han.forEach(t),YVt=r(P2o," (MobileViT model)"),P2o.forEach(t),ZVt=i(Et),Ix=n(Et,"LI",{});var I2o=s(Ix);MXe=n(I2o,"STRONG",{});var Jan=s(MXe);KVt=r(Jan,"segformer"),Jan.forEach(t),eXt=r(I2o," \u2014 "),f_e=n(I2o,"A",{href:!0});var Yan=s(f_e);oXt=r(Yan,"SegformerForSemanticSegmentation"),Yan.forEach(t),rXt=r(I2o," (SegFormer model)"),I2o.forEach(t),tXt=i(Et),Bx=n(Et,"LI",{});var B2o=s(Bx);EXe=n(B2o,"STRONG",{});var Zan=s(EXe);aXt=r(Zan,"upernet"),Zan.forEach(t),nXt=r(B2o," \u2014 "),g_e=n(B2o,"A",{href:!0});var Kan=s(g_e);sXt=r(Kan,"UperNetForSemanticSegmentation"),Kan.forEach(t),lXt=r(B2o," (UPerNet model)"),B2o.forEach(t),Et.forEach(t),iXt=i(Ka),Nx=n(Ka,"P",{});var N2o=s(Nx);dXt=r(N2o,"The model is set in evaluation mode by default using "),CXe=n(N2o,"CODE",{});var enn=s(CXe);mXt=r(enn,"model.eval()"),enn.forEach(t),cXt=r(N2o,` (so for instance, dropout modules are
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with the `),h_e=n(jbe,"A",{href:!0});var nnn=s(h_e);bXt=r(nnn,"from_pretrained()"),nnn.forEach(t),vXt=r(jbe," class method or the "),u_e=n(jbe,"A",{href:!0});var snn=s(u_e);TXt=r(snn,"from_config()"),snn.forEach(t),FXt=r(jbe,` class
method.`),jbe.forEach(t),MXt=i(ud),mG=n(ud,"P",{});var AEo=s(mG);EXt=r(AEo,"This class cannot be instantiated directly using "),yXe=n(AEo,"CODE",{});var lnn=s(yXe);CXt=r(lnn,"__init__()"),lnn.forEach(t),wXt=r(AEo," (throws an error)."),AEo.forEach(t),AXt=i(ud),ua=n(ud,"DIV",{class:!0});var rP=s(ua);T(cG.$$.fragment,rP),LXt=i(rP),xXe=n(rP,"P",{});var inn=s(xXe);yXt=r(inn,"Instantiates one of the model classes of the library (with a semantic segmentation head) from a configuration."),inn.forEach(t),xXt=i(rP),bf=n(rP,"P",{});var Obe=s(bf);kXt=r(Obe,`Note:
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model\u2019s configuration. Use `),p_e=n(Obe,"A",{href:!0});var mnn=s(p_e);RXt=r(mnn,"from_pretrained()"),mnn.forEach(t),PXt=r(Obe," to load the model weights."),Obe.forEach(t),IXt=i(rP),T(qx.$$.fragment,rP),rP.forEach(t),BXt=i(ud),pt=n(ud,"DIV",{class:!0});var pd=s(pt);T(fG.$$.fragment,pd),NXt=i(pd),$Xe=n(pd,"P",{});var cnn=s($Xe);DXt=r(cnn,"Instantiate one of the model classes of the library (with a semantic segmentation head) from a pretrained model."),cnn.forEach(t),GXt=i(pd),as=n(pd,"P",{});var tP=s(as);qXt=r(tP,"The model class to instantiate is selected based on the "),SXe=n(tP,"CODE",{});var fnn=s(SXe);jXt=r(fnn,"model_type"),fnn.forEach(t),OXt=r(tP,` property of the config object (either
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falling back to using pattern matching on `),PXe=n(tP,"CODE",{});var hnn=s(PXe);zXt=r(hnn,"pretrained_model_name_or_path"),hnn.forEach(t),WXt=r(tP,":"),tP.forEach(t),QXt=i(pd),vf=n(pd,"UL",{});var Vbe=s(vf);jx=n(Vbe,"LI",{});var D2o=s(jx);IXe=n(D2o,"STRONG",{});var unn=s(IXe);UXt=r(unn,"data2vec-vision"),unn.forEach(t),HXt=r(D2o," \u2014 "),__e=n(D2o,"A",{href:!0});var pnn=s(__e);JXt=r(pnn,"TFData2VecVisionForSemanticSegmentation"),pnn.forEach(t),YXt=r(D2o," (Data2VecVision model)"),D2o.forEach(t),ZXt=i(Vbe),Ox=n(Vbe,"LI",{});var G2o=s(Ox);BXe=n(G2o,"STRONG",{});var _nn=s(BXe);KXt=r(_nn,"mobilevit"),_nn.forEach(t),ezt=r(G2o," \u2014 "),b_e=n(G2o,"A",{href:!0});var bnn=s(b_e);ozt=r(bnn,"TFMobileViTForSemanticSegmentation"),bnn.forEach(t),rzt=r(G2o," (MobileViT model)"),G2o.forEach(t),tzt=i(Vbe),Vx=n(Vbe,"LI",{});var q2o=s(Vx);NXe=n(q2o,"STRONG",{});var vnn=s(NXe);azt=r(vnn,"segformer"),vnn.forEach(t),nzt=r(q2o," \u2014 "),v_e=n(q2o,"A",{href:!0});var Tnn=s(v_e);szt=r(Tnn,"TFSegformerForSemanticSegmentation"),Tnn.forEach(t),lzt=r(q2o," (SegFormer model)"),q2o.forEach(t),Vbe.forEach(t),izt=i(pd),T(Xx.$$.fragment,pd),pd.forEach(t),ud.forEach(t),zTo=i(c),Tf=n(c,"H3",{class:!0});var LEo=s(Tf);zx=n(LEo,"A",{id:!0,class:!0,href:!0});var Fnn=s(zx);DXe=n(Fnn,"SPAN",{});var Mnn=s(DXe);T(gG.$$.fragment,Mnn),Mnn.forEach(t),Fnn.forEach(t),dzt=i(LEo),GXe=n(LEo,"SPAN",{});var Enn=s(GXe);mzt=r(Enn,"AutoModelForInstanceSegmentation"),Enn.forEach(t),LEo.forEach(t),WTo=i(c),xr=n(c,"DIV",{class:!0});var _d=s(xr);T(hG.$$.fragment,_d),czt=i(_d),Ff=n(_d,"P",{});var Xbe=s(Ff);fzt=r(Xbe,`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 `),T_e=n(Xbe,"A",{href:!0});var Cnn=s(T_e);gzt=r(Cnn,"from_pretrained()"),Cnn.forEach(t),hzt=r(Xbe," class method or the "),F_e=n(Xbe,"A",{href:!0});var wnn=s(F_e);uzt=r(wnn,"from_config()"),wnn.forEach(t),pzt=r(Xbe,` class
method.`),Xbe.forEach(t),_zt=i(_d),uG=n(_d,"P",{});var yEo=s(uG);bzt=r(yEo,"This class cannot be instantiated directly using "),qXe=n(yEo,"CODE",{});var Ann=s(qXe);vzt=r(Ann,"__init__()"),Ann.forEach(t),Tzt=r(yEo," (throws an error)."),yEo.forEach(t),Fzt=i(_d),pa=n(_d,"DIV",{class:!0});var aP=s(pa);T(pG.$$.fragment,aP),Mzt=i(aP),jXe=n(aP,"P",{});var Lnn=s(jXe);Ezt=r(Lnn,"Instantiates one of the model classes of the library (with a instance segmentation head) from a configuration."),Lnn.forEach(t),Czt=i(aP),Mf=n(aP,"P",{});var zbe=s(Mf);wzt=r(zbe,`Note:
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model\u2019s configuration. Use `),M_e=n(zbe,"A",{href:!0});var xnn=s(M_e);yzt=r(xnn,"from_pretrained()"),xnn.forEach(t),xzt=r(zbe," to load the model weights."),zbe.forEach(t),kzt=i(aP),T(Wx.$$.fragment,aP),aP.forEach(t),$zt=i(_d),Co=n(_d,"DIV",{class:!0});var en=s(Co);T(_G.$$.fragment,en),Szt=i(en),VXe=n(en,"P",{});var knn=s(VXe);Rzt=r(knn,"Instantiate one of the model classes of the library (with a instance segmentation head) from a pretrained model."),knn.forEach(t),Pzt=i(en),ns=n(en,"P",{});var nP=s(ns);Izt=r(nP,"The model class to instantiate is selected based on the "),XXe=n(nP,"CODE",{});var $nn=s(XXe);Bzt=r($nn,"model_type"),$nn.forEach(t),Nzt=r(nP,` property of the config object (either
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falling back to using pattern matching on `),WXe=n(nP,"CODE",{});var Rnn=s(WXe);qzt=r(Rnn,"pretrained_model_name_or_path"),Rnn.forEach(t),jzt=r(nP,":"),nP.forEach(t),Ozt=i(en),QXe=n(en,"UL",{});var Pnn=s(QXe);Qx=n(Pnn,"LI",{});var j2o=s(Qx);UXe=n(j2o,"STRONG",{});var Inn=s(UXe);Vzt=r(Inn,"maskformer"),Inn.forEach(t),Xzt=r(j2o," \u2014 "),E_e=n(j2o,"A",{href:!0});var Bnn=s(E_e);zzt=r(Bnn,"MaskFormerForInstanceSegmentation"),Bnn.forEach(t),Wzt=r(j2o," (MaskFormer model)"),j2o.forEach(t),Pnn.forEach(t),Qzt=i(en),Ux=n(en,"P",{});var O2o=s(Ux);Uzt=r(O2o,"The model is set in evaluation mode by default using "),HXe=n(O2o,"CODE",{});var Nnn=s(HXe);Hzt=r(Nnn,"model.eval()"),Nnn.forEach(t),Jzt=r(O2o,` (so for instance, dropout modules are
deactivated). To train the model, you should first set it back in training mode with `),JXe=n(O2o,"CODE",{});var Dnn=s(JXe);Yzt=r(Dnn,"model.train()"),Dnn.forEach(t),O2o.forEach(t),Zzt=i(en),T(Hx.$$.fragment,en),en.forEach(t),_d.forEach(t),QTo=i(c),Ef=n(c,"H3",{class:!0});var xEo=s(Ef);Jx=n(xEo,"A",{id:!0,class:!0,href:!0});var Gnn=s(Jx);YXe=n(Gnn,"SPAN",{});var qnn=s(YXe);T(bG.$$.fragment,qnn),qnn.forEach(t),Gnn.forEach(t),Kzt=i(xEo),ZXe=n(xEo,"SPAN",{});var jnn=s(ZXe);eWt=r(jnn,"AutoModelForUniversalSegmentation"),jnn.forEach(t),xEo.forEach(t),UTo=i(c),kr=n(c,"DIV",{class:!0});var bd=s(kr);T(vG.$$.fragment,bd),oWt=i(bd),Cf=n(bd,"P",{});var Wbe=s(Cf);rWt=r(Wbe,`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 `),C_e=n(Wbe,"A",{href:!0});var Onn=s(C_e);tWt=r(Onn,"from_pretrained()"),Onn.forEach(t),aWt=r(Wbe," class method or the "),w_e=n(Wbe,"A",{href:!0});var Vnn=s(w_e);nWt=r(Vnn,"from_config()"),Vnn.forEach(t),sWt=r(Wbe,` class
method.`),Wbe.forEach(t),lWt=i(bd),TG=n(bd,"P",{});var kEo=s(TG);iWt=r(kEo,"This class cannot be instantiated directly using "),KXe=n(kEo,"CODE",{});var Xnn=s(KXe);dWt=r(Xnn,"__init__()"),Xnn.forEach(t),mWt=r(kEo," (throws an error)."),kEo.forEach(t),cWt=i(bd),_a=n(bd,"DIV",{class:!0});var sP=s(_a);T(FG.$$.fragment,sP),fWt=i(sP),eze=n(sP,"P",{});var znn=s(eze);gWt=r(znn,"Instantiates one of the model classes of the library (with a universal image segmentation head) from a configuration."),znn.forEach(t),hWt=i(sP),wf=n(sP,"P",{});var Qbe=s(wf);uWt=r(Qbe,`Note:
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model\u2019s configuration. Use `),A_e=n(Qbe,"A",{href:!0});var Qnn=s(A_e);bWt=r(Qnn,"from_pretrained()"),Qnn.forEach(t),vWt=r(Qbe," to load the model weights."),Qbe.forEach(t),TWt=i(sP),T(Yx.$$.fragment,sP),sP.forEach(t),FWt=i(bd),wo=n(bd,"DIV",{class:!0});var on=s(wo);T(MG.$$.fragment,on),MWt=i(on),rze=n(on,"P",{});var Unn=s(rze);EWt=r(Unn,"Instantiate one of the model classes of the library (with a universal image segmentation head) from a pretrained model."),Unn.forEach(t),CWt=i(on),ss=n(on,"P",{});var lP=s(ss);wWt=r(lP,"The model class to instantiate is selected based on the "),tze=n(lP,"CODE",{});var Hnn=s(tze);AWt=r(Hnn,"model_type"),Hnn.forEach(t),LWt=r(lP,` property of the config object (either
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falling back to using pattern matching on `),nze=n(lP,"CODE",{});var Ynn=s(nze);kWt=r(Ynn,"pretrained_model_name_or_path"),Ynn.forEach(t),$Wt=r(lP,":"),lP.forEach(t),SWt=i(on),ls=n(on,"UL",{});var iP=s(ls);Zx=n(iP,"LI",{});var V2o=s(Zx);sze=n(V2o,"STRONG",{});var Znn=s(sze);RWt=r(Znn,"detr"),Znn.forEach(t),PWt=r(V2o," \u2014 "),L_e=n(V2o,"A",{href:!0});var Knn=s(L_e);IWt=r(Knn,"DetrForSegmentation"),Knn.forEach(t),BWt=r(V2o," (DETR model)"),V2o.forEach(t),NWt=i(iP),Kx=n(iP,"LI",{});var X2o=s(Kx);lze=n(X2o,"STRONG",{});var esn=s(lze);DWt=r(esn,"mask2former"),esn.forEach(t),GWt=r(X2o," \u2014 "),y_e=n(X2o,"A",{href:!0});var osn=s(y_e);qWt=r(osn,"Mask2FormerForUniversalSegmentation"),osn.forEach(t),jWt=r(X2o," (Mask2Former model)"),X2o.forEach(t),OWt=i(iP),ek=n(iP,"LI",{});var z2o=s(ek);ize=n(z2o,"STRONG",{});var rsn=s(ize);VWt=r(rsn,"maskformer"),rsn.forEach(t),XWt=r(z2o," \u2014 "),x_e=n(z2o,"A",{href:!0});var tsn=s(x_e);zWt=r(tsn,"MaskFormerForInstanceSegmentation"),tsn.forEach(t),WWt=r(z2o," (MaskFormer model)"),z2o.forEach(t),QWt=i(iP),ok=n(iP,"LI",{});var W2o=s(ok);dze=n(W2o,"STRONG",{});var asn=s(dze);UWt=r(asn,"oneformer"),asn.forEach(t),HWt=r(W2o," \u2014 "),k_e=n(W2o,"A",{href:!0});var nsn=s(k_e);JWt=r(nsn,"OneFormerForUniversalSegmentation"),nsn.forEach(t),YWt=r(W2o," (OneFormer model)"),W2o.forEach(t),iP.forEach(t),ZWt=i(on),rk=n(on,"P",{});var Q2o=s(rk);KWt=r(Q2o,"The model is set in evaluation mode by default using "),mze=n(Q2o,"CODE",{});var ssn=s(mze);eQt=r(ssn,"model.eval()"),ssn.forEach(t),oQt=r(Q2o,` (so for instance, dropout modules are
deactivated). To train the model, you should first set it back in training mode with `),cze=n(Q2o,"CODE",{});var lsn=s(cze);rQt=r(lsn,"model.train()"),lsn.forEach(t),Q2o.forEach(t),tQt=i(on),T(tk.$$.fragment,on),on.forEach(t),bd.forEach(t),HTo=i(c),Af=n(c,"H3",{class:!0});var $Eo=s(Af);ak=n($Eo,"A",{id:!0,class:!0,href:!0});var isn=s(ak);fze=n(isn,"SPAN",{});var dsn=s(fze);T(EG.$$.fragment,dsn),dsn.forEach(t),isn.forEach(t),aQt=i($Eo),gze=n($Eo,"SPAN",{});var msn=s(gze);nQt=r(msn,"AutoModelForZeroShotObjectDetection"),msn.forEach(t),$Eo.forEach(t),JTo=i(c),$r=n(c,"DIV",{class:!0});var vd=s($r);T(CG.$$.fragment,vd),sQt=i(vd),Lf=n(vd,"P",{});var Ube=s(Lf);lQt=r(Ube,`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 `),$_e=n(Ube,"A",{href:!0});var csn=s($_e);iQt=r(csn,"from_pretrained()"),csn.forEach(t),dQt=r(Ube," class method or the "),S_e=n(Ube,"A",{href:!0});var fsn=s(S_e);mQt=r(fsn,"from_config()"),fsn.forEach(t),cQt=r(Ube,` class
method.`),Ube.forEach(t),fQt=i(vd),wG=n(vd,"P",{});var SEo=s(wG);gQt=r(SEo,"This class cannot be instantiated directly using "),hze=n(SEo,"CODE",{});var gsn=s(hze);hQt=r(gsn,"__init__()"),gsn.forEach(t),uQt=r(SEo," (throws an error)."),SEo.forEach(t),pQt=i(vd),ba=n(vd,"DIV",{class:!0});var dP=s(ba);T(AG.$$.fragment,dP),_Qt=i(dP),uze=n(dP,"P",{});var hsn=s(uze);bQt=r(hsn,"Instantiates one of the model classes of the library (with a zero-shot object detection head) from a configuration."),hsn.forEach(t),vQt=i(dP),yf=n(dP,"P",{});var Hbe=s(yf);TQt=r(Hbe,`Note:
Loading a model from its configuration file does `),pze=n(Hbe,"STRONG",{});var usn=s(pze);FQt=r(usn,"not"),usn.forEach(t),MQt=r(Hbe,` load the model weights. It only affects the
model\u2019s configuration. Use `),R_e=n(Hbe,"A",{href:!0});var psn=s(R_e);EQt=r(psn,"from_pretrained()"),psn.forEach(t),CQt=r(Hbe," to load the model weights."),Hbe.forEach(t),wQt=i(dP),T(nk.$$.fragment,dP),dP.forEach(t),AQt=i(vd),Ao=n(vd,"DIV",{class:!0});var rn=s(Ao);T(LG.$$.fragment,rn),LQt=i(rn),_ze=n(rn,"P",{});var _sn=s(_ze);yQt=r(_sn,"Instantiate one of the model classes of the library (with a zero-shot object detection head) from a pretrained model."),_sn.forEach(t),xQt=i(rn),is=n(rn,"P",{});var mP=s(is);kQt=r(mP,"The model class to instantiate is selected based on the "),bze=n(mP,"CODE",{});var bsn=s(bze);$Qt=r(bsn,"model_type"),bsn.forEach(t),SQt=r(mP,` property of the config object (either
passed as an argument or loaded from `),vze=n(mP,"CODE",{});var vsn=s(vze);RQt=r(vsn,"pretrained_model_name_or_path"),vsn.forEach(t),PQt=r(mP,` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),Tze=n(mP,"CODE",{});var Tsn=s(Tze);IQt=r(Tsn,"pretrained_model_name_or_path"),Tsn.forEach(t),BQt=r(mP,":"),mP.forEach(t),NQt=i(rn),Fze=n(rn,"UL",{});var Fsn=s(Fze);sk=n(Fsn,"LI",{});var U2o=s(sk);Mze=n(U2o,"STRONG",{});var Msn=s(Mze);DQt=r(Msn,"owlvit"),Msn.forEach(t),GQt=r(U2o," \u2014 "),P_e=n(U2o,"A",{href:!0});var Esn=s(P_e);qQt=r(Esn,"OwlViTForObjectDetection"),Esn.forEach(t),jQt=r(U2o," (OWL-ViT model)"),U2o.forEach(t),Fsn.forEach(t),OQt=i(rn),lk=n(rn,"P",{});var H2o=s(lk);VQt=r(H2o,"The model is set in evaluation mode by default using "),Eze=n(H2o,"CODE",{});var Csn=s(Eze);XQt=r(Csn,"model.eval()"),Csn.forEach(t),zQt=r(H2o,` (so for instance, dropout modules are
deactivated). To train the model, you should first set it back in training mode with `),Cze=n(H2o,"CODE",{});var wsn=s(Cze);WQt=r(wsn,"model.train()"),wsn.forEach(t),H2o.forEach(t),QQt=i(rn),T(ik.$$.fragment,rn),rn.forEach(t),vd.forEach(t),YTo=i(c),xf=n(c,"H2",{class:!0});var REo=s(xf);dk=n(REo,"A",{id:!0,class:!0,href:!0});var Asn=s(dk);wze=n(Asn,"SPAN",{});var Lsn=s(wze);T(yG.$$.fragment,Lsn),Lsn.forEach(t),Asn.forEach(t),UQt=i(REo),Aze=n(REo,"SPAN",{});var ysn=s(Aze);HQt=r(ysn,"Audio"),ysn.forEach(t),REo.forEach(t),ZTo=i(c),I_e=n(c,"P",{});var xsn=s(I_e);JQt=r(xsn,"The following auto classes are available for the following audio tasks."),xsn.forEach(t),KTo=i(c),kf=n(c,"H3",{class:!0});var PEo=s(kf);mk=n(PEo,"A",{id:!0,class:!0,href:!0});var ksn=s(mk);Lze=n(ksn,"SPAN",{});var $sn=s(Lze);T(xG.$$.fragment,$sn),$sn.forEach(t),ksn.forEach(t),YQt=i(PEo),yze=n(PEo,"SPAN",{});var Ssn=s(yze);ZQt=r(Ssn,"AutoModelForAudioClassification"),Ssn.forEach(t),PEo.forEach(t),eFo=i(c),Sr=n(c,"DIV",{class:!0});var Td=s(Sr);T(kG.$$.fragment,Td),KQt=i(Td),$f=n(Td,"P",{});var Jbe=s($f);eUt=r(Jbe,`This is a generic model class that will be instantiated as one of the model classes of the library (with a audio classification head) when created
with the `),B_e=n(Jbe,"A",{href:!0});var Rsn=s(B_e);oUt=r(Rsn,"from_pretrained()"),Rsn.forEach(t),rUt=r(Jbe," class method or the "),N_e=n(Jbe,"A",{href:!0});var Psn=s(N_e);tUt=r(Psn,"from_config()"),Psn.forEach(t),aUt=r(Jbe,` class
method.`),Jbe.forEach(t),nUt=i(Td),$G=n(Td,"P",{});var IEo=s($G);sUt=r(IEo,"This class cannot be instantiated directly using "),xze=n(IEo,"CODE",{});var Isn=s(xze);lUt=r(Isn,"__init__()"),Isn.forEach(t),iUt=r(IEo," (throws an error)."),IEo.forEach(t),dUt=i(Td),va=n(Td,"DIV",{class:!0});var cP=s(va);T(SG.$$.fragment,cP),mUt=i(cP),kze=n(cP,"P",{});var Bsn=s(kze);cUt=r(Bsn,"Instantiates one of the model classes of the library (with a audio classification head) from a configuration."),Bsn.forEach(t),fUt=i(cP),Sf=n(cP,"P",{});var Ybe=s(Sf);gUt=r(Ybe,`Note:
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model\u2019s configuration. Use `),D_e=n(Ybe,"A",{href:!0});var Dsn=s(D_e);pUt=r(Dsn,"from_pretrained()"),Dsn.forEach(t),_Ut=r(Ybe," to load the model weights."),Ybe.forEach(t),bUt=i(cP),T(ck.$$.fragment,cP),cP.forEach(t),vUt=i(Td),Lo=n(Td,"DIV",{class:!0});var tn=s(Lo);T(RG.$$.fragment,tn),TUt=i(tn),Sze=n(tn,"P",{});var Gsn=s(Sze);FUt=r(Gsn,"Instantiate one of the model classes of the library (with a audio classification head) from a pretrained model."),Gsn.forEach(t),MUt=i(tn),ds=n(tn,"P",{});var fP=s(ds);EUt=r(fP,"The model class to instantiate is selected based on the "),Rze=n(fP,"CODE",{});var qsn=s(Rze);CUt=r(qsn,"model_type"),qsn.forEach(t),wUt=r(fP,` property of the config object (either
passed as an argument or loaded from `),Pze=n(fP,"CODE",{});var jsn=s(Pze);AUt=r(jsn,"pretrained_model_name_or_path"),jsn.forEach(t),LUt=r(fP,` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),Ize=n(fP,"CODE",{});var Osn=s(Ize);yUt=r(Osn,"pretrained_model_name_or_path"),Osn.forEach(t),xUt=r(fP,":"),fP.forEach(t),kUt=i(tn),Ne=n(tn,"UL",{});var Qe=s(Ne);fk=n(Qe,"LI",{});var J2o=s(fk);Bze=n(J2o,"STRONG",{});var Vsn=s(Bze);$Ut=r(Vsn,"audio-spectrogram-transformer"),Vsn.forEach(t),SUt=r(J2o," \u2014 "),G_e=n(J2o,"A",{href:!0});var Xsn=s(G_e);RUt=r(Xsn,"ASTForAudioClassification"),Xsn.forEach(t),PUt=r(J2o," (Audio Spectrogram Transformer model)"),J2o.forEach(t),IUt=i(Qe),gk=n(Qe,"LI",{});var Y2o=s(gk);Nze=n(Y2o,"STRONG",{});var zsn=s(Nze);BUt=r(zsn,"data2vec-audio"),zsn.forEach(t),NUt=r(Y2o," \u2014 "),q_e=n(Y2o,"A",{href:!0});var Wsn=s(q_e);DUt=r(Wsn,"Data2VecAudioForSequenceClassification"),Wsn.forEach(t),GUt=r(Y2o," (Data2VecAudio model)"),Y2o.forEach(t),qUt=i(Qe),hk=n(Qe,"LI",{});var Z2o=s(hk);Dze=n(Z2o,"STRONG",{});var Qsn=s(Dze);jUt=r(Qsn,"hubert"),Qsn.forEach(t),OUt=r(Z2o," \u2014 "),j_e=n(Z2o,"A",{href:!0});var Usn=s(j_e);VUt=r(Usn,"HubertForSequenceClassification"),Usn.forEach(t),XUt=r(Z2o," (Hubert model)"),Z2o.forEach(t),zUt=i(Qe),uk=n(Qe,"LI",{});var K2o=s(uk);Gze=n(K2o,"STRONG",{});var Hsn=s(Gze);WUt=r(Hsn,"sew"),Hsn.forEach(t),QUt=r(K2o," \u2014 "),O_e=n(K2o,"A",{href:!0});var Jsn=s(O_e);UUt=r(Jsn,"SEWForSequenceClassification"),Jsn.forEach(t),HUt=r(K2o," (SEW model)"),K2o.forEach(t),JUt=i(Qe),pk=n(Qe,"LI",{});var ebo=s(pk);qze=n(ebo,"STRONG",{});var Ysn=s(qze);YUt=r(Ysn,"sew-d"),Ysn.forEach(t),ZUt=r(ebo," \u2014 "),V_e=n(ebo,"A",{href:!0});var Zsn=s(V_e);KUt=r(Zsn,"SEWDForSequenceClassification"),Zsn.forEach(t),eHt=r(ebo," (SEW-D model)"),ebo.forEach(t),oHt=i(Qe),_k=n(Qe,"LI",{});var obo=s(_k);jze=n(obo,"STRONG",{});var Ksn=s(jze);rHt=r(Ksn,"unispeech"),Ksn.forEach(t),tHt=r(obo," \u2014 "),X_e=n(obo,"A",{href:!0});var eln=s(X_e);aHt=r(eln,"UniSpeechForSequenceClassification"),eln.forEach(t),nHt=r(obo," (UniSpeech model)"),obo.forEach(t),sHt=i(Qe),bk=n(Qe,"LI",{});var rbo=s(bk);Oze=n(rbo,"STRONG",{});var oln=s(Oze);lHt=r(oln,"unispeech-sat"),oln.forEach(t),iHt=r(rbo," \u2014 "),z_e=n(rbo,"A",{href:!0});var rln=s(z_e);dHt=r(rln,"UniSpeechSatForSequenceClassification"),rln.forEach(t),mHt=r(rbo," (UniSpeechSat model)"),rbo.forEach(t),cHt=i(Qe),vk=n(Qe,"LI",{});var tbo=s(vk);Vze=n(tbo,"STRONG",{});var tln=s(Vze);fHt=r(tln,"wav2vec2"),tln.forEach(t),gHt=r(tbo," \u2014 "),W_e=n(tbo,"A",{href:!0});var aln=s(W_e);hHt=r(aln,"Wav2Vec2ForSequenceClassification"),aln.forEach(t),uHt=r(tbo," (Wav2Vec2 model)"),tbo.forEach(t),pHt=i(Qe),Tk=n(Qe,"LI",{});var abo=s(Tk);Xze=n(abo,"STRONG",{});var nln=s(Xze);_Ht=r(nln,"wav2vec2-conformer"),nln.forEach(t),bHt=r(abo," \u2014 "),Q_e=n(abo,"A",{href:!0});var sln=s(Q_e);vHt=r(sln,"Wav2Vec2ConformerForSequenceClassification"),sln.forEach(t),THt=r(abo," (Wav2Vec2-Conformer model)"),abo.forEach(t),FHt=i(Qe),Fk=n(Qe,"LI",{});var nbo=s(Fk);zze=n(nbo,"STRONG",{});var lln=s(zze);MHt=r(lln,"wavlm"),lln.forEach(t),EHt=r(nbo," \u2014 "),U_e=n(nbo,"A",{href:!0});var iln=s(U_e);CHt=r(iln,"WavLMForSequenceClassification"),iln.forEach(t),wHt=r(nbo," (WavLM model)"),nbo.forEach(t),Qe.forEach(t),AHt=i(tn),Mk=n(tn,"P",{});var sbo=s(Mk);LHt=r(sbo,"The model is set in evaluation mode by default using "),Wze=n(sbo,"CODE",{});var dln=s(Wze);yHt=r(dln,"model.eval()"),dln.forEach(t),xHt=r(sbo,` (so for instance, dropout modules are
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with the `),H_e=n(Zbe,"A",{href:!0});var hln=s(H_e);BHt=r(hln,"from_pretrained()"),hln.forEach(t),NHt=r(Zbe," class method or the "),J_e=n(Zbe,"A",{href:!0});var uln=s(J_e);DHt=r(uln,"from_config()"),uln.forEach(t),GHt=r(Zbe,` class
method.`),Zbe.forEach(t),qHt=i(Fd),BG=n(Fd,"P",{});var NEo=s(BG);jHt=r(NEo,"This class cannot be instantiated directly using "),Jze=n(NEo,"CODE",{});var pln=s(Jze);OHt=r(pln,"__init__()"),pln.forEach(t),VHt=r(NEo," (throws an error)."),NEo.forEach(t),XHt=i(Fd),Ta=n(Fd,"DIV",{class:!0});var gP=s(Ta);T(NG.$$.fragment,gP),zHt=i(gP),Yze=n(gP,"P",{});var _ln=s(Yze);WHt=r(_ln,"Instantiates one of the model classes of the library (with a audio frame (token) classification head) from a configuration."),_ln.forEach(t),QHt=i(gP),If=n(gP,"P",{});var Kbe=s(If);UHt=r(Kbe,`Note:
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model\u2019s configuration. Use `),Y_e=n(Kbe,"A",{href:!0});var vln=s(Y_e);YHt=r(vln,"from_pretrained()"),vln.forEach(t),ZHt=r(Kbe," to load the model weights."),Kbe.forEach(t),KHt=i(gP),T(wk.$$.fragment,gP),gP.forEach(t),eJt=i(Fd),yo=n(Fd,"DIV",{class:!0});var an=s(yo);T(DG.$$.fragment,an),oJt=i(an),Kze=n(an,"P",{});var Tln=s(Kze);rJt=r(Tln,"Instantiate one of the model classes of the library (with a audio frame (token) classification head) from a pretrained model."),Tln.forEach(t),tJt=i(an),ms=n(an,"P",{});var hP=s(ms);aJt=r(hP,"The model class to instantiate is selected based on the "),eWe=n(hP,"CODE",{});var Fln=s(eWe);nJt=r(Fln,"model_type"),Fln.forEach(t),sJt=r(hP,` property of the config object (either
passed as an argument or loaded from `),oWe=n(hP,"CODE",{});var Mln=s(oWe);lJt=r(Mln,"pretrained_model_name_or_path"),Mln.forEach(t),iJt=r(hP,` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),rWe=n(hP,"CODE",{});var Eln=s(rWe);dJt=r(Eln,"pretrained_model_name_or_path"),Eln.forEach(t),mJt=r(hP,":"),hP.forEach(t),cJt=i(an),Ct=n(an,"UL",{});var Md=s(Ct);Ak=n(Md,"LI",{});var lbo=s(Ak);tWe=n(lbo,"STRONG",{});var Cln=s(tWe);fJt=r(Cln,"data2vec-audio"),Cln.forEach(t),gJt=r(lbo," \u2014 "),Z_e=n(lbo,"A",{href:!0});var wln=s(Z_e);hJt=r(wln,"Data2VecAudioForAudioFrameClassification"),wln.forEach(t),uJt=r(lbo," (Data2VecAudio model)"),lbo.forEach(t),pJt=i(Md),Lk=n(Md,"LI",{});var ibo=s(Lk);aWe=n(ibo,"STRONG",{});var Aln=s(aWe);_Jt=r(Aln,"unispeech-sat"),Aln.forEach(t),bJt=r(ibo," \u2014 "),K_e=n(ibo,"A",{href:!0});var Lln=s(K_e);vJt=r(Lln,"UniSpeechSatForAudioFrameClassification"),Lln.forEach(t),TJt=r(ibo," (UniSpeechSat model)"),ibo.forEach(t),FJt=i(Md),yk=n(Md,"LI",{});var dbo=s(yk);nWe=n(dbo,"STRONG",{});var yln=s(nWe);MJt=r(yln,"wav2vec2"),yln.forEach(t),EJt=r(dbo," \u2014 "),e1e=n(dbo,"A",{href:!0});var xln=s(e1e);CJt=r(xln,"Wav2Vec2ForAudioFrameClassification"),xln.forEach(t),wJt=r(dbo," (Wav2Vec2 model)"),dbo.forEach(t),AJt=i(Md),xk=n(Md,"LI",{});var mbo=s(xk);sWe=n(mbo,"STRONG",{});var kln=s(sWe);LJt=r(kln,"wav2vec2-conformer"),kln.forEach(t),yJt=r(mbo," \u2014 "),o1e=n(mbo,"A",{href:!0});var $ln=s(o1e);xJt=r($ln,"Wav2Vec2ConformerForAudioFrameClassification"),$ln.forEach(t),kJt=r(mbo," (Wav2Vec2-Conformer model)"),mbo.forEach(t),$Jt=i(Md),kk=n(Md,"LI",{});var cbo=s(kk);lWe=n(cbo,"STRONG",{});var Sln=s(lWe);SJt=r(Sln,"wavlm"),Sln.forEach(t),RJt=r(cbo," \u2014 "),r1e=n(cbo,"A",{href:!0});var Rln=s(r1e);PJt=r(Rln,"WavLMForAudioFrameClassification"),Rln.forEach(t),IJt=r(cbo," (WavLM model)"),cbo.forEach(t),Md.forEach(t),BJt=i(an),$k=n(an,"P",{});var fbo=s($k);NJt=r(fbo,"The model is set in evaluation mode by default using "),iWe=n(fbo,"CODE",{});var Pln=s(iWe);DJt=r(Pln,"model.eval()"),Pln.forEach(t),GJt=r(fbo,` (so for instance, dropout modules are
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with the `),t1e=n(eve,"A",{href:!0});var Gln=s(t1e);WJt=r(Gln,"from_pretrained()"),Gln.forEach(t),QJt=r(eve," class method or the "),a1e=n(eve,"A",{href:!0});var qln=s(a1e);UJt=r(qln,"from_config()"),qln.forEach(t),HJt=r(eve,` class
method.`),eve.forEach(t),JJt=i(Ed),jG=n(Ed,"P",{});var GEo=s(jG);YJt=r(GEo,"This class cannot be instantiated directly using "),fWe=n(GEo,"CODE",{});var jln=s(fWe);ZJt=r(jln,"__init__()"),jln.forEach(t),KJt=r(GEo," (throws an error)."),GEo.forEach(t),eYt=i(Ed),Fa=n(Ed,"DIV",{class:!0});var uP=s(Fa);T(OG.$$.fragment,uP),oYt=i(uP),gWe=n(uP,"P",{});var Oln=s(gWe);rYt=r(Oln,"Instantiates one of the model classes of the library (with a connectionist temporal classification head) from a configuration."),Oln.forEach(t),tYt=i(uP),Df=n(uP,"P",{});var ove=s(Df);aYt=r(ove,`Note:
Loading a model from its configuration file does `),hWe=n(ove,"STRONG",{});var Vln=s(hWe);nYt=r(Vln,"not"),Vln.forEach(t),sYt=r(ove,` load the model weights. It only affects the
model\u2019s configuration. Use `),n1e=n(ove,"A",{href:!0});var Xln=s(n1e);lYt=r(Xln,"from_pretrained()"),Xln.forEach(t),iYt=r(ove," to load the model weights."),ove.forEach(t),dYt=i(uP),T(Pk.$$.fragment,uP),uP.forEach(t),mYt=i(Ed),xo=n(Ed,"DIV",{class:!0});var nn=s(xo);T(VG.$$.fragment,nn),cYt=i(nn),uWe=n(nn,"P",{});var zln=s(uWe);fYt=r(zln,"Instantiate one of the model classes of the library (with a connectionist temporal classification head) from a pretrained model."),zln.forEach(t),gYt=i(nn),cs=n(nn,"P",{});var pP=s(cs);hYt=r(pP,"The model class to instantiate is selected based on the "),pWe=n(pP,"CODE",{});var Wln=s(pWe);uYt=r(Wln,"model_type"),Wln.forEach(t),pYt=r(pP,` property of the config object (either
passed as an argument or loaded from `),_We=n(pP,"CODE",{});var Qln=s(_We);_Yt=r(Qln,"pretrained_model_name_or_path"),Qln.forEach(t),bYt=r(pP,` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),bWe=n(pP,"CODE",{});var Uln=s(bWe);vYt=r(Uln,"pretrained_model_name_or_path"),Uln.forEach(t),TYt=r(pP,":"),pP.forEach(t),FYt=i(nn),De=n(nn,"UL",{});var Ue=s(De);Ik=n(Ue,"LI",{});var gbo=s(Ik);vWe=n(gbo,"STRONG",{});var Hln=s(vWe);MYt=r(Hln,"data2vec-audio"),Hln.forEach(t),EYt=r(gbo," \u2014 "),s1e=n(gbo,"A",{href:!0});var Jln=s(s1e);CYt=r(Jln,"Data2VecAudioForCTC"),Jln.forEach(t),wYt=r(gbo," (Data2VecAudio model)"),gbo.forEach(t),AYt=i(Ue),Bk=n(Ue,"LI",{});var hbo=s(Bk);TWe=n(hbo,"STRONG",{});var Yln=s(TWe);LYt=r(Yln,"hubert"),Yln.forEach(t),yYt=r(hbo," \u2014 "),l1e=n(hbo,"A",{href:!0});var Zln=s(l1e);xYt=r(Zln,"HubertForCTC"),Zln.forEach(t),kYt=r(hbo," (Hubert model)"),hbo.forEach(t),$Yt=i(Ue),Nk=n(Ue,"LI",{});var ubo=s(Nk);FWe=n(ubo,"STRONG",{});var Kln=s(FWe);SYt=r(Kln,"mctct"),Kln.forEach(t),RYt=r(ubo," \u2014 "),i1e=n(ubo,"A",{href:!0});var ein=s(i1e);PYt=r(ein,"MCTCTForCTC"),ein.forEach(t),IYt=r(ubo," (M-CTC-T model)"),ubo.forEach(t),BYt=i(Ue),Dk=n(Ue,"LI",{});var pbo=s(Dk);MWe=n(pbo,"STRONG",{});var oin=s(MWe);NYt=r(oin,"sew"),oin.forEach(t),DYt=r(pbo," \u2014 "),d1e=n(pbo,"A",{href:!0});var rin=s(d1e);GYt=r(rin,"SEWForCTC"),rin.forEach(t),qYt=r(pbo," (SEW model)"),pbo.forEach(t),jYt=i(Ue),Gk=n(Ue,"LI",{});var _bo=s(Gk);EWe=n(_bo,"STRONG",{});var tin=s(EWe);OYt=r(tin,"sew-d"),tin.forEach(t),VYt=r(_bo," \u2014 "),m1e=n(_bo,"A",{href:!0});var ain=s(m1e);XYt=r(ain,"SEWDForCTC"),ain.forEach(t),zYt=r(_bo," (SEW-D model)"),_bo.forEach(t),WYt=i(Ue),qk=n(Ue,"LI",{});var bbo=s(qk);CWe=n(bbo,"STRONG",{});var nin=s(CWe);QYt=r(nin,"unispeech"),nin.forEach(t),UYt=r(bbo," \u2014 "),c1e=n(bbo,"A",{href:!0});var sin=s(c1e);HYt=r(sin,"UniSpeechForCTC"),sin.forEach(t),JYt=r(bbo," (UniSpeech model)"),bbo.forEach(t),YYt=i(Ue),jk=n(Ue,"LI",{});var vbo=s(jk);wWe=n(vbo,"STRONG",{});var lin=s(wWe);ZYt=r(lin,"unispeech-sat"),lin.forEach(t),KYt=r(vbo," \u2014 "),f1e=n(vbo,"A",{href:!0});var iin=s(f1e);eZt=r(iin,"UniSpeechSatForCTC"),iin.forEach(t),oZt=r(vbo," (UniSpeechSat model)"),vbo.forEach(t),rZt=i(Ue),Ok=n(Ue,"LI",{});var Tbo=s(Ok);AWe=n(Tbo,"STRONG",{});var din=s(AWe);tZt=r(din,"wav2vec2"),din.forEach(t),aZt=r(Tbo," \u2014 "),g1e=n(Tbo,"A",{href:!0});var min=s(g1e);nZt=r(min,"Wav2Vec2ForCTC"),min.forEach(t),sZt=r(Tbo," (Wav2Vec2 model)"),Tbo.forEach(t),lZt=i(Ue),Vk=n(Ue,"LI",{});var Fbo=s(Vk);LWe=n(Fbo,"STRONG",{});var cin=s(LWe);iZt=r(cin,"wav2vec2-conformer"),cin.forEach(t),dZt=r(Fbo," \u2014 "),h1e=n(Fbo,"A",{href:!0});var fin=s(h1e);mZt=r(fin,"Wav2Vec2ConformerForCTC"),fin.forEach(t),cZt=r(Fbo," (Wav2Vec2-Conformer model)"),Fbo.forEach(t),fZt=i(Ue),Xk=n(Ue,"LI",{});var Mbo=s(Xk);yWe=n(Mbo,"STRONG",{});var gin=s(yWe);gZt=r(gin,"wavlm"),gin.forEach(t),hZt=r(Mbo," \u2014 "),u1e=n(Mbo,"A",{href:!0});var hin=s(u1e);uZt=r(hin,"WavLMForCTC"),hin.forEach(t),pZt=r(Mbo," (WavLM model)"),Mbo.forEach(t),Ue.forEach(t),_Zt=i(nn),zk=n(nn,"P",{});var Ebo=s(zk);bZt=r(Ebo,"The model is set in evaluation mode by default using "),xWe=n(Ebo,"CODE",{});var uin=s(xWe);vZt=r(uin,"model.eval()"),uin.forEach(t),TZt=r(Ebo,` (so for instance, dropout modules are
deactivated). To train the model, you should first set it back in training mode with `),kWe=n(Ebo,"CODE",{});var pin=s(kWe);FZt=r(pin,"model.train()"),pin.forEach(t),Ebo.forEach(t),MZt=i(nn),T(Wk.$$.fragment,nn),nn.forEach(t),Ed.forEach(t),nFo=i(c),Gf=n(c,"H3",{class:!0});var qEo=s(Gf);Qk=n(qEo,"A",{id:!0,class:!0,href:!0});var _in=s(Qk);$We=n(_in,"SPAN",{});var bin=s($We);T(XG.$$.fragment,bin),bin.forEach(t),_in.forEach(t),EZt=i(qEo),SWe=n(qEo,"SPAN",{});var vin=s(SWe);CZt=r(vin,"AutoModelForSpeechSeq2Seq"),vin.forEach(t),qEo.forEach(t),sFo=i(c),Ir=n(c,"DIV",{class:!0});var Cd=s(Ir);T(zG.$$.fragment,Cd),wZt=i(Cd),qf=n(Cd,"P",{});var rve=s(qf);AZt=r(rve,`This is a generic model class that will be instantiated as one of the model classes of the library (with a sequence-to-sequence speech-to-text modeling head) when created
with the `),p1e=n(rve,"A",{href:!0});var Tin=s(p1e);LZt=r(Tin,"from_pretrained()"),Tin.forEach(t),yZt=r(rve," class method or the "),_1e=n(rve,"A",{href:!0});var Fin=s(_1e);xZt=r(Fin,"from_config()"),Fin.forEach(t),kZt=r(rve,` class
method.`),rve.forEach(t),$Zt=i(Cd),WG=n(Cd,"P",{});var jEo=s(WG);SZt=r(jEo,"This class cannot be instantiated directly using "),RWe=n(jEo,"CODE",{});var Min=s(RWe);RZt=r(Min,"__init__()"),Min.forEach(t),PZt=r(jEo," (throws an error)."),jEo.forEach(t),IZt=i(Cd),Ma=n(Cd,"DIV",{class:!0});var _P=s(Ma);T(QG.$$.fragment,_P),BZt=i(_P),PWe=n(_P,"P",{});var Ein=s(PWe);NZt=r(Ein,"Instantiates one of the model classes of the library (with a sequence-to-sequence speech-to-text modeling head) from a configuration."),Ein.forEach(t),DZt=i(_P),jf=n(_P,"P",{});var tve=s(jf);GZt=r(tve,`Note:
Loading a model from its configuration file does `),IWe=n(tve,"STRONG",{});var Cin=s(IWe);qZt=r(Cin,"not"),Cin.forEach(t),jZt=r(tve,` load the model weights. It only affects the
model\u2019s configuration. Use `),b1e=n(tve,"A",{href:!0});var win=s(b1e);OZt=r(win,"from_pretrained()"),win.forEach(t),VZt=r(tve," to load the model weights."),tve.forEach(t),XZt=i(_P),T(Uk.$$.fragment,_P),_P.forEach(t),zZt=i(Cd),ko=n(Cd,"DIV",{class:!0});var sn=s(ko);T(UG.$$.fragment,sn),WZt=i(sn),BWe=n(sn,"P",{});var Ain=s(BWe);QZt=r(Ain,"Instantiate one of the model classes of the library (with a sequence-to-sequence speech-to-text modeling head) from a pretrained model."),Ain.forEach(t),UZt=i(sn),fs=n(sn,"P",{});var bP=s(fs);HZt=r(bP,"The model class to instantiate is selected based on the "),NWe=n(bP,"CODE",{});var Lin=s(NWe);JZt=r(Lin,"model_type"),Lin.forEach(t),YZt=r(bP,` property of the config object (either
passed as an argument or loaded from `),DWe=n(bP,"CODE",{});var yin=s(DWe);ZZt=r(yin,"pretrained_model_name_or_path"),yin.forEach(t),KZt=r(bP,` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),GWe=n(bP,"CODE",{});var xin=s(GWe);eKt=r(xin,"pretrained_model_name_or_path"),xin.forEach(t),oKt=r(bP,":"),bP.forEach(t),rKt=i(sn),Of=n(sn,"UL",{});var ave=s(Of);Hk=n(ave,"LI",{});var Cbo=s(Hk);qWe=n(Cbo,"STRONG",{});var kin=s(qWe);tKt=r(kin,"speech-encoder-decoder"),kin.forEach(t),aKt=r(Cbo," \u2014 "),v1e=n(Cbo,"A",{href:!0});var $in=s(v1e);nKt=r($in,"SpeechEncoderDecoderModel"),$in.forEach(t),sKt=r(Cbo," (Speech Encoder decoder model)"),Cbo.forEach(t),lKt=i(ave),Jk=n(ave,"LI",{});var wbo=s(Jk);jWe=n(wbo,"STRONG",{});var Sin=s(jWe);iKt=r(Sin,"speech_to_text"),Sin.forEach(t),dKt=r(wbo," \u2014 "),T1e=n(wbo,"A",{href:!0});var Rin=s(T1e);mKt=r(Rin,"Speech2TextForConditionalGeneration"),Rin.forEach(t),cKt=r(wbo," (Speech2Text model)"),wbo.forEach(t),fKt=i(ave),Yk=n(ave,"LI",{});var Abo=s(Yk);OWe=n(Abo,"STRONG",{});var Pin=s(OWe);gKt=r(Pin,"whisper"),Pin.forEach(t),hKt=r(Abo," \u2014 "),F1e=n(Abo,"A",{href:!0});var Iin=s(F1e);uKt=r(Iin,"WhisperForConditionalGeneration"),Iin.forEach(t),pKt=r(Abo," (Whisper model)"),Abo.forEach(t),ave.forEach(t),_Kt=i(sn),Zk=n(sn,"P",{});var Lbo=s(Zk);bKt=r(Lbo,"The model is set in evaluation mode by default using "),VWe=n(Lbo,"CODE",{});var Bin=s(VWe);vKt=r(Bin,"model.eval()"),Bin.forEach(t),TKt=r(Lbo,` (so for instance, dropout modules are
deactivated). To train the model, you should first set it back in training mode with `),XWe=n(Lbo,"CODE",{});var Nin=s(XWe);FKt=r(Nin,"model.train()"),Nin.forEach(t),Lbo.forEach(t),MKt=i(sn),T(Kk.$$.fragment,sn),sn.forEach(t),Cd.forEach(t),lFo=i(c),Vf=n(c,"H3",{class:!0});var OEo=s(Vf);e$=n(OEo,"A",{id:!0,class:!0,href:!0});var Din=s(e$);zWe=n(Din,"SPAN",{});var Gin=s(zWe);T(HG.$$.fragment,Gin),Gin.forEach(t),Din.forEach(t),EKt=i(OEo),WWe=n(OEo,"SPAN",{});var qin=s(WWe);CKt=r(qin,"TFAutoModelForSpeechSeq2Seq"),qin.forEach(t),OEo.forEach(t),iFo=i(c),Br=n(c,"DIV",{class:!0});var wd=s(Br);T(JG.$$.fragment,wd),wKt=i(wd),Xf=n(wd,"P",{});var nve=s(Xf);AKt=r(nve,`This is a generic model class that will be instantiated as one of the model classes of the library (with a sequence-to-sequence speech-to-text modeling head) when created
with the `),M1e=n(nve,"A",{href:!0});var jin=s(M1e);LKt=r(jin,"from_pretrained()"),jin.forEach(t),yKt=r(nve," class method or the "),E1e=n(nve,"A",{href:!0});var Oin=s(E1e);xKt=r(Oin,"from_config()"),Oin.forEach(t),kKt=r(nve,` class
method.`),nve.forEach(t),$Kt=i(wd),YG=n(wd,"P",{});var VEo=s(YG);SKt=r(VEo,"This class cannot be instantiated directly using "),QWe=n(VEo,"CODE",{});var Vin=s(QWe);RKt=r(Vin,"__init__()"),Vin.forEach(t),PKt=r(VEo," (throws an error)."),VEo.forEach(t),IKt=i(wd),Ea=n(wd,"DIV",{class:!0});var vP=s(Ea);T(ZG.$$.fragment,vP),BKt=i(vP),UWe=n(vP,"P",{});var Xin=s(UWe);NKt=r(Xin,"Instantiates one of the model classes of the library (with a sequence-to-sequence speech-to-text modeling head) from a configuration."),Xin.forEach(t),DKt=i(vP),zf=n(vP,"P",{});var sve=s(zf);GKt=r(sve,`Note:
Loading a model from its configuration file does `),HWe=n(sve,"STRONG",{});var zin=s(HWe);qKt=r(zin,"not"),zin.forEach(t),jKt=r(sve,` load the model weights. It only affects the
model\u2019s configuration. Use `),C1e=n(sve,"A",{href:!0});var Win=s(C1e);OKt=r(Win,"from_pretrained()"),Win.forEach(t),VKt=r(sve," to load the model weights."),sve.forEach(t),XKt=i(vP),T(o$.$$.fragment,vP),vP.forEach(t),zKt=i(wd),_t=n(wd,"DIV",{class:!0});var Ad=s(_t);T(KG.$$.fragment,Ad),WKt=i(Ad),JWe=n(Ad,"P",{});var Qin=s(JWe);QKt=r(Qin,"Instantiate one of the model classes of the library (with a sequence-to-sequence speech-to-text modeling head) from a pretrained model."),Qin.forEach(t),UKt=i(Ad),gs=n(Ad,"P",{});var TP=s(gs);HKt=r(TP,"The model class to instantiate is selected based on the "),YWe=n(TP,"CODE",{});var Uin=s(YWe);JKt=r(Uin,"model_type"),Uin.forEach(t),YKt=r(TP,` property of the config object (either
passed as an argument or loaded from `),ZWe=n(TP,"CODE",{});var Hin=s(ZWe);ZKt=r(Hin,"pretrained_model_name_or_path"),Hin.forEach(t),KKt=r(TP,` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),KWe=n(TP,"CODE",{});var Jin=s(KWe);eea=r(Jin,"pretrained_model_name_or_path"),Jin.forEach(t),oea=r(TP,":"),TP.forEach(t),rea=i(Ad),eq=n(Ad,"UL",{});var XEo=s(eq);r$=n(XEo,"LI",{});var ybo=s(r$);eQe=n(ybo,"STRONG",{});var Yin=s(eQe);tea=r(Yin,"speech_to_text"),Yin.forEach(t),aea=r(ybo," \u2014 "),w1e=n(ybo,"A",{href:!0});var Zin=s(w1e);nea=r(Zin,"TFSpeech2TextForConditionalGeneration"),Zin.forEach(t),sea=r(ybo," (Speech2Text model)"),ybo.forEach(t),lea=i(XEo),t$=n(XEo,"LI",{});var xbo=s(t$);oQe=n(xbo,"STRONG",{});var Kin=s(oQe);iea=r(Kin,"whisper"),Kin.forEach(t),dea=r(xbo," \u2014 "),A1e=n(xbo,"A",{href:!0});var edn=s(A1e);mea=r(edn,"TFWhisperForConditionalGeneration"),edn.forEach(t),cea=r(xbo," (Whisper model)"),xbo.forEach(t),XEo.forEach(t),fea=i(Ad),T(a$.$$.fragment,Ad),Ad.forEach(t),wd.forEach(t),dFo=i(c),Wf=n(c,"H3",{class:!0});var zEo=s(Wf);n$=n(zEo,"A",{id:!0,class:!0,href:!0});var odn=s(n$);rQe=n(odn,"SPAN",{});var rdn=s(rQe);T(oq.$$.fragment,rdn),rdn.forEach(t),odn.forEach(t),gea=i(zEo),tQe=n(zEo,"SPAN",{});var tdn=s(tQe);hea=r(tdn,"AutoModelForAudioXVector"),tdn.forEach(t),zEo.forEach(t),mFo=i(c),Nr=n(c,"DIV",{class:!0});var Ld=s(Nr);T(rq.$$.fragment,Ld),uea=i(Ld),Qf=n(Ld,"P",{});var lve=s(Qf);pea=r(lve,`This is a generic model class that will be instantiated as one of the model classes of the library (with a audio retrieval via x-vector head) when created
with the `),L1e=n(lve,"A",{href:!0});var adn=s(L1e);_ea=r(adn,"from_pretrained()"),adn.forEach(t),bea=r(lve," class method or the "),y1e=n(lve,"A",{href:!0});var ndn=s(y1e);vea=r(ndn,"from_config()"),ndn.forEach(t),Tea=r(lve,` class
method.`),lve.forEach(t),Fea=i(Ld),tq=n(Ld,"P",{});var WEo=s(tq);Mea=r(WEo,"This class cannot be instantiated directly using "),aQe=n(WEo,"CODE",{});var sdn=s(aQe);Eea=r(sdn,"__init__()"),sdn.forEach(t),Cea=r(WEo," (throws an error)."),WEo.forEach(t),wea=i(Ld),Ca=n(Ld,"DIV",{class:!0});var FP=s(Ca);T(aq.$$.fragment,FP),Aea=i(FP),nQe=n(FP,"P",{});var ldn=s(nQe);Lea=r(ldn,"Instantiates one of the model classes of the library (with a audio retrieval via x-vector head) from a configuration."),ldn.forEach(t),yea=i(FP),Uf=n(FP,"P",{});var ive=s(Uf);xea=r(ive,`Note:
Loading a model from its configuration file does `),sQe=n(ive,"STRONG",{});var idn=s(sQe);kea=r(idn,"not"),idn.forEach(t),$ea=r(ive,` load the model weights. It only affects the
model\u2019s configuration. Use `),x1e=n(ive,"A",{href:!0});var ddn=s(x1e);Sea=r(ddn,"from_pretrained()"),ddn.forEach(t),Rea=r(ive," to load the model weights."),ive.forEach(t),Pea=i(FP),T(s$.$$.fragment,FP),FP.forEach(t),Iea=i(Ld),$o=n(Ld,"DIV",{class:!0});var ln=s($o);T(nq.$$.fragment,ln),Bea=i(ln),lQe=n(ln,"P",{});var mdn=s(lQe);Nea=r(mdn,"Instantiate one of the model classes of the library (with a audio retrieval via x-vector head) from a pretrained model."),mdn.forEach(t),Dea=i(ln),hs=n(ln,"P",{});var MP=s(hs);Gea=r(MP,"The model class to instantiate is selected based on the "),iQe=n(MP,"CODE",{});var cdn=s(iQe);qea=r(cdn,"model_type"),cdn.forEach(t),jea=r(MP,` property of the config object (either
passed as an argument or loaded from `),dQe=n(MP,"CODE",{});var fdn=s(dQe);Oea=r(fdn,"pretrained_model_name_or_path"),fdn.forEach(t),Vea=r(MP,` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),mQe=n(MP,"CODE",{});var gdn=s(mQe);Xea=r(gdn,"pretrained_model_name_or_path"),gdn.forEach(t),zea=r(MP,":"),MP.forEach(t),Wea=i(ln),wt=n(ln,"UL",{});var yd=s(wt);l$=n(yd,"LI",{});var kbo=s(l$);cQe=n(kbo,"STRONG",{});var hdn=s(cQe);Qea=r(hdn,"data2vec-audio"),hdn.forEach(t),Uea=r(kbo," \u2014 "),k1e=n(kbo,"A",{href:!0});var udn=s(k1e);Hea=r(udn,"Data2VecAudioForXVector"),udn.forEach(t),Jea=r(kbo," (Data2VecAudio model)"),kbo.forEach(t),Yea=i(yd),i$=n(yd,"LI",{});var $bo=s(i$);fQe=n($bo,"STRONG",{});var pdn=s(fQe);Zea=r(pdn,"unispeech-sat"),pdn.forEach(t),Kea=r($bo," \u2014 "),$1e=n($bo,"A",{href:!0});var _dn=s($1e);eoa=r(_dn,"UniSpeechSatForXVector"),_dn.forEach(t),ooa=r($bo," (UniSpeechSat model)"),$bo.forEach(t),roa=i(yd),d$=n(yd,"LI",{});var Sbo=s(d$);gQe=n(Sbo,"STRONG",{});var bdn=s(gQe);toa=r(bdn,"wav2vec2"),bdn.forEach(t),aoa=r(Sbo," \u2014 "),S1e=n(Sbo,"A",{href:!0});var vdn=s(S1e);noa=r(vdn,"Wav2Vec2ForXVector"),vdn.forEach(t),soa=r(Sbo," (Wav2Vec2 model)"),Sbo.forEach(t),loa=i(yd),m$=n(yd,"LI",{});var Rbo=s(m$);hQe=n(Rbo,"STRONG",{});var Tdn=s(hQe);ioa=r(Tdn,"wav2vec2-conformer"),Tdn.forEach(t),doa=r(Rbo," \u2014 "),R1e=n(Rbo,"A",{href:!0});var Fdn=s(R1e);moa=r(Fdn,"Wav2Vec2ConformerForXVector"),Fdn.forEach(t),coa=r(Rbo," (Wav2Vec2-Conformer model)"),Rbo.forEach(t),foa=i(yd),c$=n(yd,"LI",{});var Pbo=s(c$);uQe=n(Pbo,"STRONG",{});var Mdn=s(uQe);goa=r(Mdn,"wavlm"),Mdn.forEach(t),hoa=r(Pbo," \u2014 "),P1e=n(Pbo,"A",{href:!0});var Edn=s(P1e);uoa=r(Edn,"WavLMForXVector"),Edn.forEach(t),poa=r(Pbo," (WavLM model)"),Pbo.forEach(t),yd.forEach(t),_oa=i(ln),f$=n(ln,"P",{});var Ibo=s(f$);boa=r(Ibo,"The model is set in evaluation mode by default using "),pQe=n(Ibo,"CODE",{});var Cdn=s(pQe);voa=r(Cdn,"model.eval()"),Cdn.forEach(t),Toa=r(Ibo,` (so for instance, dropout modules are
deactivated). To train the model, you should first set it back in training mode with `),_Qe=n(Ibo,"CODE",{});var wdn=s(_Qe);Foa=r(wdn,"model.train()"),wdn.forEach(t),Ibo.forEach(t),Moa=i(ln),T(g$.$$.fragment,ln),ln.forEach(t),Ld.forEach(t),cFo=i(c),Hf=n(c,"H2",{class:!0});var QEo=s(Hf);h$=n(QEo,"A",{id:!0,class:!0,href:!0});var Adn=s(h$);bQe=n(Adn,"SPAN",{});var Ldn=s(bQe);T(sq.$$.fragment,Ldn),Ldn.forEach(t),Adn.forEach(t),Eoa=i(QEo),vQe=n(QEo,"SPAN",{});var ydn=s(vQe);Coa=r(ydn,"Multimodal"),ydn.forEach(t),QEo.forEach(t),fFo=i(c),I1e=n(c,"P",{});var xdn=s(I1e);woa=r(xdn,"The following auto classes are available for the following multimodal tasks."),xdn.forEach(t),gFo=i(c),Jf=n(c,"H3",{class:!0});var UEo=s(Jf);u$=n(UEo,"A",{id:!0,class:!0,href:!0});var kdn=s(u$);TQe=n(kdn,"SPAN",{});var $dn=s(TQe);T(lq.$$.fragment,$dn),$dn.forEach(t),kdn.forEach(t),Aoa=i(UEo),FQe=n(UEo,"SPAN",{});var Sdn=s(FQe);Loa=r(Sdn,"AutoModelForTableQuestionAnswering"),Sdn.forEach(t),UEo.forEach(t),hFo=i(c),Dr=n(c,"DIV",{class:!0});var xd=s(Dr);T(iq.$$.fragment,xd),yoa=i(xd),Yf=n(xd,"P",{});var dve=s(Yf);xoa=r(dve,`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 `),B1e=n(dve,"A",{href:!0});var Rdn=s(B1e);koa=r(Rdn,"from_pretrained()"),Rdn.forEach(t),$oa=r(dve," class method or the "),N1e=n(dve,"A",{href:!0});var Pdn=s(N1e);Soa=r(Pdn,"from_config()"),Pdn.forEach(t),Roa=r(dve,` class
method.`),dve.forEach(t),Poa=i(xd),dq=n(xd,"P",{});var HEo=s(dq);Ioa=r(HEo,"This class cannot be instantiated directly using "),MQe=n(HEo,"CODE",{});var Idn=s(MQe);Boa=r(Idn,"__init__()"),Idn.forEach(t),Noa=r(HEo," (throws an error)."),HEo.forEach(t),Doa=i(xd),wa=n(xd,"DIV",{class:!0});var EP=s(wa);T(mq.$$.fragment,EP),Goa=i(EP),EQe=n(EP,"P",{});var Bdn=s(EQe);qoa=r(Bdn,"Instantiates one of the model classes of the library (with a table question answering head) from a configuration."),Bdn.forEach(t),joa=i(EP),Zf=n(EP,"P",{});var mve=s(Zf);Ooa=r(mve,`Note:
Loading a model from its configuration file does `),CQe=n(mve,"STRONG",{});var Ndn=s(CQe);Voa=r(Ndn,"not"),Ndn.forEach(t),Xoa=r(mve,` load the model weights. It only affects the
model\u2019s configuration. Use `),D1e=n(mve,"A",{href:!0});var Ddn=s(D1e);zoa=r(Ddn,"from_pretrained()"),Ddn.forEach(t),Woa=r(mve," to load the model weights."),mve.forEach(t),Qoa=i(EP),T(p$.$$.fragment,EP),EP.forEach(t),Uoa=i(xd),So=n(xd,"DIV",{class:!0});var dn=s(So);T(cq.$$.fragment,dn),Hoa=i(dn),wQe=n(dn,"P",{});var Gdn=s(wQe);Joa=r(Gdn,"Instantiate one of the model classes of the library (with a table question answering head) from a pretrained model."),Gdn.forEach(t),Yoa=i(dn),us=n(dn,"P",{});var CP=s(us);Zoa=r(CP,"The model class to instantiate is selected based on the "),AQe=n(CP,"CODE",{});var qdn=s(AQe);Koa=r(qdn,"model_type"),qdn.forEach(t),era=r(CP,` property of the config object (either
passed as an argument or loaded from `),LQe=n(CP,"CODE",{});var jdn=s(LQe);ora=r(jdn,"pretrained_model_name_or_path"),jdn.forEach(t),rra=r(CP,` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),yQe=n(CP,"CODE",{});var Odn=s(yQe);tra=r(Odn,"pretrained_model_name_or_path"),Odn.forEach(t),ara=r(CP,":"),CP.forEach(t),nra=i(dn),xQe=n(dn,"UL",{});var Vdn=s(xQe);_$=n(Vdn,"LI",{});var Bbo=s(_$);kQe=n(Bbo,"STRONG",{});var Xdn=s(kQe);sra=r(Xdn,"tapas"),Xdn.forEach(t),lra=r(Bbo," \u2014 "),G1e=n(Bbo,"A",{href:!0});var zdn=s(G1e);ira=r(zdn,"TapasForQuestionAnswering"),zdn.forEach(t),dra=r(Bbo," (TAPAS model)"),Bbo.forEach(t),Vdn.forEach(t),mra=i(dn),b$=n(dn,"P",{});var Nbo=s(b$);cra=r(Nbo,"The model is set in evaluation mode by default using "),$Qe=n(Nbo,"CODE",{});var Wdn=s($Qe);fra=r(Wdn,"model.eval()"),Wdn.forEach(t),gra=r(Nbo,` (so for instance, dropout modules are
deactivated). To train the model, you should first set it back in training mode with `),SQe=n(Nbo,"CODE",{});var Qdn=s(SQe);hra=r(Qdn,"model.train()"),Qdn.forEach(t),Nbo.forEach(t),ura=i(dn),T(v$.$$.fragment,dn),dn.forEach(t),xd.forEach(t),uFo=i(c),Kf=n(c,"H3",{class:!0});var JEo=s(Kf);T$=n(JEo,"A",{id:!0,class:!0,href:!0});var Udn=s(T$);RQe=n(Udn,"SPAN",{});var Hdn=s(RQe);T(fq.$$.fragment,Hdn),Hdn.forEach(t),Udn.forEach(t),pra=i(JEo),PQe=n(JEo,"SPAN",{});var Jdn=s(PQe);_ra=r(Jdn,"TFAutoModelForTableQuestionAnswering"),Jdn.forEach(t),JEo.forEach(t),pFo=i(c),Gr=n(c,"DIV",{class:!0});var kd=s(Gr);T(gq.$$.fragment,kd),bra=i(kd),eg=n(kd,"P",{});var cve=s(eg);vra=r(cve,`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 `),q1e=n(cve,"A",{href:!0});var Ydn=s(q1e);Tra=r(Ydn,"from_pretrained()"),Ydn.forEach(t),Fra=r(cve," class method or the "),j1e=n(cve,"A",{href:!0});var Zdn=s(j1e);Mra=r(Zdn,"from_config()"),Zdn.forEach(t),Era=r(cve,` class
method.`),cve.forEach(t),Cra=i(kd),hq=n(kd,"P",{});var YEo=s(hq);wra=r(YEo,"This class cannot be instantiated directly using "),IQe=n(YEo,"CODE",{});var Kdn=s(IQe);Ara=r(Kdn,"__init__()"),Kdn.forEach(t),Lra=r(YEo," (throws an error)."),YEo.forEach(t),yra=i(kd),Aa=n(kd,"DIV",{class:!0});var wP=s(Aa);T(uq.$$.fragment,wP),xra=i(wP),BQe=n(wP,"P",{});var emn=s(BQe);kra=r(emn,"Instantiates one of the model classes of the library (with a table question answering head) from a configuration."),emn.forEach(t),$ra=i(wP),og=n(wP,"P",{});var fve=s(og);Sra=r(fve,`Note:
Loading a model from its configuration file does `),NQe=n(fve,"STRONG",{});var omn=s(NQe);Rra=r(omn,"not"),omn.forEach(t),Pra=r(fve,` load the model weights. It only affects the
model\u2019s configuration. Use `),O1e=n(fve,"A",{href:!0});var rmn=s(O1e);Ira=r(rmn,"from_pretrained()"),rmn.forEach(t),Bra=r(fve," to load the model weights."),fve.forEach(t),Nra=i(wP),T(F$.$$.fragment,wP),wP.forEach(t),Dra=i(kd),bt=n(kd,"DIV",{class:!0});var $d=s(bt);T(pq.$$.fragment,$d),Gra=i($d),DQe=n($d,"P",{});var tmn=s(DQe);qra=r(tmn,"Instantiate one of the model classes of the library (with a table question answering head) from a pretrained model."),tmn.forEach(t),jra=i($d),ps=n($d,"P",{});var AP=s(ps);Ora=r(AP,"The model class to instantiate is selected based on the "),GQe=n(AP,"CODE",{});var amn=s(GQe);Vra=r(amn,"model_type"),amn.forEach(t),Xra=r(AP,` property of the config object (either
passed as an argument or loaded from `),qQe=n(AP,"CODE",{});var nmn=s(qQe);zra=r(nmn,"pretrained_model_name_or_path"),nmn.forEach(t),Wra=r(AP,` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),jQe=n(AP,"CODE",{});var smn=s(jQe);Qra=r(smn,"pretrained_model_name_or_path"),smn.forEach(t),Ura=r(AP,":"),AP.forEach(t),Hra=i($d),OQe=n($d,"UL",{});var lmn=s(OQe);M$=n(lmn,"LI",{});var Dbo=s(M$);VQe=n(Dbo,"STRONG",{});var imn=s(VQe);Jra=r(imn,"tapas"),imn.forEach(t),Yra=r(Dbo," \u2014 "),V1e=n(Dbo,"A",{href:!0});var dmn=s(V1e);Zra=r(dmn,"TFTapasForQuestionAnswering"),dmn.forEach(t),Kra=r(Dbo," (TAPAS model)"),Dbo.forEach(t),lmn.forEach(t),eta=i($d),T(E$.$$.fragment,$d),$d.forEach(t),kd.forEach(t),_Fo=i(c),rg=n(c,"H3",{class:!0});var ZEo=s(rg);C$=n(ZEo,"A",{id:!0,class:!0,href:!0});var mmn=s(C$);XQe=n(mmn,"SPAN",{});var cmn=s(XQe);T(_q.$$.fragment,cmn),cmn.forEach(t),mmn.forEach(t),ota=i(ZEo),zQe=n(ZEo,"SPAN",{});var fmn=s(zQe);rta=r(fmn,"AutoModelForDocumentQuestionAnswering"),fmn.forEach(t),ZEo.forEach(t),bFo=i(c),qr=n(c,"DIV",{class:!0});var Sd=s(qr);T(bq.$$.fragment,Sd),tta=i(Sd),tg=n(Sd,"P",{});var gve=s(tg);ata=r(gve,`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 `),X1e=n(gve,"A",{href:!0});var gmn=s(X1e);nta=r(gmn,"from_pretrained()"),gmn.forEach(t),sta=r(gve," class method or the "),z1e=n(gve,"A",{href:!0});var hmn=s(z1e);lta=r(hmn,"from_config()"),hmn.forEach(t),ita=r(gve,` class
method.`),gve.forEach(t),dta=i(Sd),vq=n(Sd,"P",{});var KEo=s(vq);mta=r(KEo,"This class cannot be instantiated directly using "),WQe=n(KEo,"CODE",{});var umn=s(WQe);cta=r(umn,"__init__()"),umn.forEach(t),fta=r(KEo," (throws an error)."),KEo.forEach(t),gta=i(Sd),La=n(Sd,"DIV",{class:!0});var LP=s(La);T(Tq.$$.fragment,LP),hta=i(LP),QQe=n(LP,"P",{});var pmn=s(QQe);uta=r(pmn,"Instantiates one of the model classes of the library (with a document question answering head) from a configuration."),pmn.forEach(t),pta=i(LP),ag=n(LP,"P",{});var hve=s(ag);_ta=r(hve,`Note:
Loading a model from its configuration file does `),UQe=n(hve,"STRONG",{});var _mn=s(UQe);bta=r(_mn,"not"),_mn.forEach(t),vta=r(hve,` load the model weights. It only affects the
model\u2019s configuration. Use `),W1e=n(hve,"A",{href:!0});var bmn=s(W1e);Tta=r(bmn,"from_pretrained()"),bmn.forEach(t),Fta=r(hve," to load the model weights."),hve.forEach(t),Mta=i(LP),T(w$.$$.fragment,LP),LP.forEach(t),Eta=i(Sd),Ro=n(Sd,"DIV",{class:!0});var mn=s(Ro);T(Fq.$$.fragment,mn),Cta=i(mn),HQe=n(mn,"P",{});var vmn=s(HQe);wta=r(vmn,"Instantiate one of the model classes of the library (with a document question answering head) from a pretrained model."),vmn.forEach(t),Ata=i(mn),_s=n(mn,"P",{});var yP=s(_s);Lta=r(yP,"The model class to instantiate is selected based on the "),JQe=n(yP,"CODE",{});var Tmn=s(JQe);yta=r(Tmn,"model_type"),Tmn.forEach(t),xta=r(yP,` property of the config object (either
passed as an argument or loaded from `),YQe=n(yP,"CODE",{});var Fmn=s(YQe);kta=r(Fmn,"pretrained_model_name_or_path"),Fmn.forEach(t),$ta=r(yP,` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),ZQe=n(yP,"CODE",{});var Mmn=s(ZQe);Sta=r(Mmn,"pretrained_model_name_or_path"),Mmn.forEach(t),Rta=r(yP,":"),yP.forEach(t),Pta=i(mn),ng=n(mn,"UL",{});var uve=s(ng);A$=n(uve,"LI",{});var Gbo=s(A$);KQe=n(Gbo,"STRONG",{});var Emn=s(KQe);Ita=r(Emn,"layoutlm"),Emn.forEach(t),Bta=r(Gbo," \u2014 "),Q1e=n(Gbo,"A",{href:!0});var Cmn=s(Q1e);Nta=r(Cmn,"LayoutLMForQuestionAnswering"),Cmn.forEach(t),Dta=r(Gbo," (LayoutLM model)"),Gbo.forEach(t),Gta=i(uve),L$=n(uve,"LI",{});var qbo=s(L$);eUe=n(qbo,"STRONG",{});var wmn=s(eUe);qta=r(wmn,"layoutlmv2"),wmn.forEach(t),jta=r(qbo," \u2014 "),U1e=n(qbo,"A",{href:!0});var Amn=s(U1e);Ota=r(Amn,"LayoutLMv2ForQuestionAnswering"),Amn.forEach(t),Vta=r(qbo," (LayoutLMv2 model)"),qbo.forEach(t),Xta=i(uve),y$=n(uve,"LI",{});var jbo=s(y$);oUe=n(jbo,"STRONG",{});var Lmn=s(oUe);zta=r(Lmn,"layoutlmv3"),Lmn.forEach(t),Wta=r(jbo," \u2014 "),H1e=n(jbo,"A",{href:!0});var ymn=s(H1e);Qta=r(ymn,"LayoutLMv3ForQuestionAnswering"),ymn.forEach(t),Uta=r(jbo," (LayoutLMv3 model)"),jbo.forEach(t),uve.forEach(t),Hta=i(mn),x$=n(mn,"P",{});var Obo=s(x$);Jta=r(Obo,"The model is set in evaluation mode by default using "),rUe=n(Obo,"CODE",{});var xmn=s(rUe);Yta=r(xmn,"model.eval()"),xmn.forEach(t),Zta=r(Obo,` (so for instance, dropout modules are
deactivated). To train the model, you should first set it back in training mode with `),tUe=n(Obo,"CODE",{});var kmn=s(tUe);Kta=r(kmn,"model.train()"),kmn.forEach(t),Obo.forEach(t),eaa=i(mn),T(k$.$$.fragment,mn),mn.forEach(t),Sd.forEach(t),vFo=i(c),sg=n(c,"H3",{class:!0});var e3o=s(sg);$$=n(e3o,"A",{id:!0,class:!0,href:!0});var $mn=s($$);aUe=n($mn,"SPAN",{});var Smn=s(aUe);T(Mq.$$.fragment,Smn),Smn.forEach(t),$mn.forEach(t),oaa=i(e3o),nUe=n(e3o,"SPAN",{});var Rmn=s(nUe);raa=r(Rmn,"TFAutoModelForDocumentQuestionAnswering"),Rmn.forEach(t),e3o.forEach(t),TFo=i(c),jr=n(c,"DIV",{class:!0});var Rd=s(jr);T(Eq.$$.fragment,Rd),taa=i(Rd),lg=n(Rd,"P",{});var pve=s(lg);aaa=r(pve,`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 `),J1e=n(pve,"A",{href:!0});var Pmn=s(J1e);naa=r(Pmn,"from_pretrained()"),Pmn.forEach(t),saa=r(pve," class method or the "),Y1e=n(pve,"A",{href:!0});var Imn=s(Y1e);laa=r(Imn,"from_config()"),Imn.forEach(t),iaa=r(pve,` class
method.`),pve.forEach(t),daa=i(Rd),Cq=n(Rd,"P",{});var o3o=s(Cq);maa=r(o3o,"This class cannot be instantiated directly using "),sUe=n(o3o,"CODE",{});var Bmn=s(sUe);caa=r(Bmn,"__init__()"),Bmn.forEach(t),faa=r(o3o," (throws an error)."),o3o.forEach(t),gaa=i(Rd),ya=n(Rd,"DIV",{class:!0});var xP=s(ya);T(wq.$$.fragment,xP),haa=i(xP),lUe=n(xP,"P",{});var Nmn=s(lUe);uaa=r(Nmn,"Instantiates one of the model classes of the library (with a document question answering head) from a configuration."),Nmn.forEach(t),paa=i(xP),ig=n(xP,"P",{});var _ve=s(ig);_aa=r(_ve,`Note:
Loading a model from its configuration file does `),iUe=n(_ve,"STRONG",{});var Dmn=s(iUe);baa=r(Dmn,"not"),Dmn.forEach(t),vaa=r(_ve,` load the model weights. It only affects the
model\u2019s configuration. Use `),Z1e=n(_ve,"A",{href:!0});var Gmn=s(Z1e);Taa=r(Gmn,"from_pretrained()"),Gmn.forEach(t),Faa=r(_ve," to load the model weights."),_ve.forEach(t),Maa=i(xP),T(S$.$$.fragment,xP),xP.forEach(t),Eaa=i(Rd),vt=n(Rd,"DIV",{class:!0});var Pd=s(vt);T(Aq.$$.fragment,Pd),Caa=i(Pd),dUe=n(Pd,"P",{});var qmn=s(dUe);waa=r(qmn,"Instantiate one of the model classes of the library (with a document question answering head) from a pretrained model."),qmn.forEach(t),Aaa=i(Pd),bs=n(Pd,"P",{});var kP=s(bs);Laa=r(kP,"The model class to instantiate is selected based on the "),mUe=n(kP,"CODE",{});var jmn=s(mUe);yaa=r(jmn,"model_type"),jmn.forEach(t),xaa=r(kP,` property of the config object (either
passed as an argument or loaded from `),cUe=n(kP,"CODE",{});var Omn=s(cUe);kaa=r(Omn,"pretrained_model_name_or_path"),Omn.forEach(t),$aa=r(kP,` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),fUe=n(kP,"CODE",{});var Vmn=s(fUe);Saa=r(Vmn,"pretrained_model_name_or_path"),Vmn.forEach(t),Raa=r(kP,":"),kP.forEach(t),Paa=i(Pd),gUe=n(Pd,"UL",{});var Xmn=s(gUe);R$=n(Xmn,"LI",{});var Vbo=s(R$);hUe=n(Vbo,"STRONG",{});var zmn=s(hUe);Iaa=r(zmn,"layoutlm"),zmn.forEach(t),Baa=r(Vbo," \u2014 "),K1e=n(Vbo,"A",{href:!0});var Wmn=s(K1e);Naa=r(Wmn,"TFLayoutLMForQuestionAnswering"),Wmn.forEach(t),Daa=r(Vbo," (LayoutLM model)"),Vbo.forEach(t),Xmn.forEach(t),Gaa=i(Pd),T(P$.$$.fragment,Pd),Pd.forEach(t),Rd.forEach(t),FFo=i(c),dg=n(c,"H3",{class:!0});var r3o=s(dg);I$=n(r3o,"A",{id:!0,class:!0,href:!0});var Qmn=s(I$);uUe=n(Qmn,"SPAN",{});var Umn=s(uUe);T(Lq.$$.fragment,Umn),Umn.forEach(t),Qmn.forEach(t),qaa=i(r3o),pUe=n(r3o,"SPAN",{});var Hmn=s(pUe);jaa=r(Hmn,"AutoModelForVisualQuestionAnswering"),Hmn.forEach(t),r3o.forEach(t),MFo=i(c),Or=n(c,"DIV",{class:!0});var Id=s(Or);T(yq.$$.fragment,Id),Oaa=i(Id),mg=n(Id,"P",{});var bve=s(mg);Vaa=r(bve,`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 `),e2e=n(bve,"A",{href:!0});var Jmn=s(e2e);Xaa=r(Jmn,"from_pretrained()"),Jmn.forEach(t),zaa=r(bve," class method or the "),o2e=n(bve,"A",{href:!0});var Ymn=s(o2e);Waa=r(Ymn,"from_config()"),Ymn.forEach(t),Qaa=r(bve,` class
method.`),bve.forEach(t),Uaa=i(Id),xq=n(Id,"P",{});var t3o=s(xq);Haa=r(t3o,"This class cannot be instantiated directly using "),_Ue=n(t3o,"CODE",{});var Zmn=s(_Ue);Jaa=r(Zmn,"__init__()"),Zmn.forEach(t),Yaa=r(t3o," (throws an error)."),t3o.forEach(t),Zaa=i(Id),xa=n(Id,"DIV",{class:!0});var $P=s(xa);T(kq.$$.fragment,$P),Kaa=i($P),bUe=n($P,"P",{});var Kmn=s(bUe);ena=r(Kmn,"Instantiates one of the model classes of the library (with a visual question answering head) from a configuration."),Kmn.forEach(t),ona=i($P),cg=n($P,"P",{});var vve=s(cg);rna=r(vve,`Note:
Loading a model from its configuration file does `),vUe=n(vve,"STRONG",{});var ecn=s(vUe);tna=r(ecn,"not"),ecn.forEach(t),ana=r(vve,` load the model weights. It only affects the
model\u2019s configuration. Use `),r2e=n(vve,"A",{href:!0});var ocn=s(r2e);nna=r(ocn,"from_pretrained()"),ocn.forEach(t),sna=r(vve," to load the model weights."),vve.forEach(t),lna=i($P),T(B$.$$.fragment,$P),$P.forEach(t),ina=i(Id),Po=n(Id,"DIV",{class:!0});var cn=s(Po);T($q.$$.fragment,cn),dna=i(cn),TUe=n(cn,"P",{});var rcn=s(TUe);mna=r(rcn,"Instantiate one of the model classes of the library (with a visual question answering head) from a pretrained model."),rcn.forEach(t),cna=i(cn),vs=n(cn,"P",{});var SP=s(vs);fna=r(SP,"The model class to instantiate is selected based on the "),FUe=n(SP,"CODE",{});var tcn=s(FUe);gna=r(tcn,"model_type"),tcn.forEach(t),hna=r(SP,` property of the config object (either
passed as an argument or loaded from `),MUe=n(SP,"CODE",{});var acn=s(MUe);una=r(acn,"pretrained_model_name_or_path"),acn.forEach(t),pna=r(SP,` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),EUe=n(SP,"CODE",{});var ncn=s(EUe);_na=r(ncn,"pretrained_model_name_or_path"),ncn.forEach(t),bna=r(SP,":"),SP.forEach(t),vna=i(cn),CUe=n(cn,"UL",{});var scn=s(CUe);N$=n(scn,"LI",{});var Xbo=s(N$);wUe=n(Xbo,"STRONG",{});var lcn=s(wUe);Tna=r(lcn,"vilt"),lcn.forEach(t),Fna=r(Xbo," \u2014 "),t2e=n(Xbo,"A",{href:!0});var icn=s(t2e);Mna=r(icn,"ViltForQuestionAnswering"),icn.forEach(t),Ena=r(Xbo," (ViLT model)"),Xbo.forEach(t),scn.forEach(t),Cna=i(cn),D$=n(cn,"P",{});var zbo=s(D$);wna=r(zbo,"The model is set in evaluation mode by default using "),AUe=n(zbo,"CODE",{});var dcn=s(AUe);Ana=r(dcn,"model.eval()"),dcn.forEach(t),Lna=r(zbo,` (so for instance, dropout modules are
deactivated). To train the model, you should first set it back in training mode with `),LUe=n(zbo,"CODE",{});var mcn=s(LUe);yna=r(mcn,"model.train()"),mcn.forEach(t),zbo.forEach(t),xna=i(cn),T(G$.$$.fragment,cn),cn.forEach(t),Id.forEach(t),EFo=i(c),fg=n(c,"H3",{class:!0});var a3o=s(fg);q$=n(a3o,"A",{id:!0,class:!0,href:!0});var ccn=s(q$);yUe=n(ccn,"SPAN",{});var fcn=s(yUe);T(Sq.$$.fragment,fcn),fcn.forEach(t),ccn.forEach(t),kna=i(a3o),xUe=n(a3o,"SPAN",{});var gcn=s(xUe);$na=r(gcn,"AutoModelForVision2Seq"),gcn.forEach(t),a3o.forEach(t),CFo=i(c),Vr=n(c,"DIV",{class:!0});var Bd=s(Vr);T(Rq.$$.fragment,Bd),Sna=i(Bd),gg=n(Bd,"P",{});var Tve=s(gg);Rna=r(Tve,`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 `),a2e=n(Tve,"A",{href:!0});var hcn=s(a2e);Pna=r(hcn,"from_pretrained()"),hcn.forEach(t),Ina=r(Tve," class method or the "),n2e=n(Tve,"A",{href:!0});var ucn=s(n2e);Bna=r(ucn,"from_config()"),ucn.forEach(t),Nna=r(Tve,` class
method.`),Tve.forEach(t),Dna=i(Bd),Pq=n(Bd,"P",{});var n3o=s(Pq);Gna=r(n3o,"This class cannot be instantiated directly using "),kUe=n(n3o,"CODE",{});var pcn=s(kUe);qna=r(pcn,"__init__()"),pcn.forEach(t),jna=r(n3o," (throws an error)."),n3o.forEach(t),Ona=i(Bd),ka=n(Bd,"DIV",{class:!0});var RP=s(ka);T(Iq.$$.fragment,RP),Vna=i(RP),$Ue=n(RP,"P",{});var _cn=s($Ue);Xna=r(_cn,"Instantiates one of the model classes of the library (with a vision-to-text modeling head) from a configuration."),_cn.forEach(t),zna=i(RP),hg=n(RP,"P",{});var Fve=s(hg);Wna=r(Fve,`Note:
Loading a model from its configuration file does `),SUe=n(Fve,"STRONG",{});var bcn=s(SUe);Qna=r(bcn,"not"),bcn.forEach(t),Una=r(Fve,` load the model weights. It only affects the
model\u2019s configuration. Use `),s2e=n(Fve,"A",{href:!0});var vcn=s(s2e);Hna=r(vcn,"from_pretrained()"),vcn.forEach(t),Jna=r(Fve," to load the model weights."),Fve.forEach(t),Yna=i(RP),T(j$.$$.fragment,RP),RP.forEach(t),Zna=i(Bd),Io=n(Bd,"DIV",{class:!0});var fn=s(Io);T(Bq.$$.fragment,fn),Kna=i(fn),RUe=n(fn,"P",{});var Tcn=s(RUe);esa=r(Tcn,"Instantiate one of the model classes of the library (with a vision-to-text modeling head) from a pretrained model."),Tcn.forEach(t),osa=i(fn),Ts=n(fn,"P",{});var PP=s(Ts);rsa=r(PP,"The model class to instantiate is selected based on the "),PUe=n(PP,"CODE",{});var Fcn=s(PUe);tsa=r(Fcn,"model_type"),Fcn.forEach(t),asa=r(PP,` property of the config object (either
passed as an argument or loaded from `),IUe=n(PP,"CODE",{});var Mcn=s(IUe);nsa=r(Mcn,"pretrained_model_name_or_path"),Mcn.forEach(t),ssa=r(PP,` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),BUe=n(PP,"CODE",{});var Ecn=s(BUe);lsa=r(Ecn,"pretrained_model_name_or_path"),Ecn.forEach(t),isa=r(PP,":"),PP.forEach(t),dsa=i(fn),NUe=n(fn,"UL",{});var Ccn=s(NUe);O$=n(Ccn,"LI",{});var Wbo=s(O$);DUe=n(Wbo,"STRONG",{});var wcn=s(DUe);msa=r(wcn,"vision-encoder-decoder"),wcn.forEach(t),csa=r(Wbo," \u2014 "),l2e=n(Wbo,"A",{href:!0});var Acn=s(l2e);fsa=r(Acn,"VisionEncoderDecoderModel"),Acn.forEach(t),gsa=r(Wbo," (Vision Encoder decoder model)"),Wbo.forEach(t),Ccn.forEach(t),hsa=i(fn),V$=n(fn,"P",{});var Qbo=s(V$);usa=r(Qbo,"The model is set in evaluation mode by default using "),GUe=n(Qbo,"CODE",{});var Lcn=s(GUe);psa=r(Lcn,"model.eval()"),Lcn.forEach(t),_sa=r(Qbo,` (so for instance, dropout modules are
deactivated). To train the model, you should first set it back in training mode with `),qUe=n(Qbo,"CODE",{});var ycn=s(qUe);bsa=r(ycn,"model.train()"),ycn.forEach(t),Qbo.forEach(t),vsa=i(fn),T(X$.$$.fragment,fn),fn.forEach(t),Bd.forEach(t),wFo=i(c),ug=n(c,"H3",{class:!0});var s3o=s(ug);z$=n(s3o,"A",{id:!0,class:!0,href:!0});var xcn=s(z$);jUe=n(xcn,"SPAN",{});var kcn=s(jUe);T(Nq.$$.fragment,kcn),kcn.forEach(t),xcn.forEach(t),Tsa=i(s3o),OUe=n(s3o,"SPAN",{});var $cn=s(OUe);Fsa=r($cn,"TFAutoModelForVision2Seq"),$cn.forEach(t),s3o.forEach(t),AFo=i(c),Xr=n(c,"DIV",{class:!0});var Nd=s(Xr);T(Dq.$$.fragment,Nd),Msa=i(Nd),pg=n(Nd,"P",{});var Mve=s(pg);Esa=r(Mve,`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 `),i2e=n(Mve,"A",{href:!0});var Scn=s(i2e);Csa=r(Scn,"from_pretrained()"),Scn.forEach(t),wsa=r(Mve," class method or the "),d2e=n(Mve,"A",{href:!0});var Rcn=s(d2e);Asa=r(Rcn,"from_config()"),Rcn.forEach(t),Lsa=r(Mve,` class
method.`),Mve.forEach(t),ysa=i(Nd),Gq=n(Nd,"P",{});var l3o=s(Gq);xsa=r(l3o,"This class cannot be instantiated directly using "),VUe=n(l3o,"CODE",{});var Pcn=s(VUe);ksa=r(Pcn,"__init__()"),Pcn.forEach(t),$sa=r(l3o," (throws an error)."),l3o.forEach(t),Ssa=i(Nd),$a=n(Nd,"DIV",{class:!0});var IP=s($a);T(qq.$$.fragment,IP),Rsa=i(IP),XUe=n(IP,"P",{});var Icn=s(XUe);Psa=r(Icn,"Instantiates one of the model classes of the library (with a vision-to-text modeling head) from a configuration."),Icn.forEach(t),Isa=i(IP),_g=n(IP,"P",{});var Eve=s(_g);Bsa=r(Eve,`Note:
Loading a model from its configuration file does `),zUe=n(Eve,"STRONG",{});var Bcn=s(zUe);Nsa=r(Bcn,"not"),Bcn.forEach(t),Dsa=r(Eve,` load the model weights. It only affects the
model\u2019s configuration. Use `),m2e=n(Eve,"A",{href:!0});var Ncn=s(m2e);Gsa=r(Ncn,"from_pretrained()"),Ncn.forEach(t),qsa=r(Eve," to load the model weights."),Eve.forEach(t),jsa=i(IP),T(W$.$$.fragment,IP),IP.forEach(t),Osa=i(Nd),Tt=n(Nd,"DIV",{class:!0});var Dd=s(Tt);T(jq.$$.fragment,Dd),Vsa=i(Dd),WUe=n(Dd,"P",{});var Dcn=s(WUe);Xsa=r(Dcn,"Instantiate one of the model classes of the library (with a vision-to-text modeling head) from a pretrained model."),Dcn.forEach(t),zsa=i(Dd),Fs=n(Dd,"P",{});var BP=s(Fs);Wsa=r(BP,"The model class to instantiate is selected based on the "),QUe=n(BP,"CODE",{});var Gcn=s(QUe);Qsa=r(Gcn,"model_type"),Gcn.forEach(t),Usa=r(BP,` property of the config object (either
passed as an argument or loaded from `),UUe=n(BP,"CODE",{});var qcn=s(UUe);Hsa=r(qcn,"pretrained_model_name_or_path"),qcn.forEach(t),Jsa=r(BP,` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),HUe=n(BP,"CODE",{});var jcn=s(HUe);Ysa=r(jcn,"pretrained_model_name_or_path"),jcn.forEach(t),Zsa=r(BP,":"),BP.forEach(t),Ksa=i(Dd),JUe=n(Dd,"UL",{});var Ocn=s(JUe);Q$=n(Ocn,"LI",{});var Ubo=s(Q$);YUe=n(Ubo,"STRONG",{});var Vcn=s(YUe);ela=r(Vcn,"vision-encoder-decoder"),Vcn.forEach(t),ola=r(Ubo," \u2014 "),c2e=n(Ubo,"A",{href:!0});var Xcn=s(c2e);rla=r(Xcn,"TFVisionEncoderDecoderModel"),Xcn.forEach(t),tla=r(Ubo," (Vision Encoder decoder model)"),Ubo.forEach(t),Ocn.forEach(t),ala=i(Dd),T(U$.$$.fragment,Dd),Dd.forEach(t),Nd.forEach(t),LFo=i(c),bg=n(c,"H3",{class:!0});var i3o=s(bg);H$=n(i3o,"A",{id:!0,class:!0,href:!0});var zcn=s(H$);ZUe=n(zcn,"SPAN",{});var Wcn=s(ZUe);T(Oq.$$.fragment,Wcn),Wcn.forEach(t),zcn.forEach(t),nla=i(i3o),KUe=n(i3o,"SPAN",{});var Qcn=s(KUe);sla=r(Qcn,"FlaxAutoModelForVision2Seq"),Qcn.forEach(t),i3o.forEach(t),yFo=i(c),zr=n(c,"DIV",{class:!0});var Gd=s(zr);T(Vq.$$.fragment,Gd),lla=i(Gd),vg=n(Gd,"P",{});var Cve=s(vg);ila=r(Cve,`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 `),f2e=n(Cve,"A",{href:!0});var Ucn=s(f2e);dla=r(Ucn,"from_pretrained()"),Ucn.forEach(t),mla=r(Cve," class method or the "),g2e=n(Cve,"A",{href:!0});var Hcn=s(g2e);cla=r(Hcn,"from_config()"),Hcn.forEach(t),fla=r(Cve,` class
method.`),Cve.forEach(t),gla=i(Gd),Xq=n(Gd,"P",{});var d3o=s(Xq);hla=r(d3o,"This class cannot be instantiated directly using "),eHe=n(d3o,"CODE",{});var Jcn=s(eHe);ula=r(Jcn,"__init__()"),Jcn.forEach(t),pla=r(d3o," (throws an error)."),d3o.forEach(t),_la=i(Gd),Sa=n(Gd,"DIV",{class:!0});var NP=s(Sa);T(zq.$$.fragment,NP),bla=i(NP),oHe=n(NP,"P",{});var Ycn=s(oHe);vla=r(Ycn,"Instantiates one of the model classes of the library (with a vision-to-text modeling head) from a configuration."),Ycn.forEach(t),Tla=i(NP),Tg=n(NP,"P",{});var wve=s(Tg);Fla=r(wve,`Note:
Loading a model from its configuration file does `),rHe=n(wve,"STRONG",{});var Zcn=s(rHe);Mla=r(Zcn,"not"),Zcn.forEach(t),Ela=r(wve,` load the model weights. It only affects the
model\u2019s configuration. Use `),h2e=n(wve,"A",{href:!0});var Kcn=s(h2e);Cla=r(Kcn,"from_pretrained()"),Kcn.forEach(t),wla=r(wve," to load the model weights."),wve.forEach(t),Ala=i(NP),T(J$.$$.fragment,NP),NP.forEach(t),Lla=i(Gd),Ft=n(Gd,"DIV",{class:!0});var qd=s(Ft);T(Wq.$$.fragment,qd),yla=i(qd),tHe=n(qd,"P",{});var efn=s(tHe);xla=r(efn,"Instantiate one of the model classes of the library (with a vision-to-text modeling head) from a pretrained model."),efn.forEach(t),kla=i(qd),Ms=n(qd,"P",{});var DP=s(Ms);$la=r(DP,"The model class to instantiate is selected based on the "),aHe=n(DP,"CODE",{});var ofn=s(aHe);Sla=r(ofn,"model_type"),ofn.forEach(t),Rla=r(DP,` property of the config object (either
passed as an argument or loaded from `),nHe=n(DP,"CODE",{});var rfn=s(nHe);Pla=r(rfn,"pretrained_model_name_or_path"),rfn.forEach(t),Ila=r(DP,` if possible), or when it\u2019s missing, by
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