import{S as oSt,i as rSt,s as tSt,e as a,k as l,w as F,t as o,M as aSt,c as n,d as t,m as i,a as s,x as T,h as r,b as c,G as e,g as b,y as M,q as E,o as C,B as w,v as nSt,L as I}from"../../chunks/vendor-hf-doc-builder.js";import{T as szr}from"../../chunks/Tip-hf-doc-builder.js";import{D as R}from"../../chunks/Docstring-hf-doc-builder.js";import{C as P}from"../../chunks/CodeBlock-hf-doc-builder.js";import{I as te}from"../../chunks/IconCopyLink-hf-doc-builder.js";import{E as B}from"../../chunks/ExampleCodeBlock-hf-doc-builder.js";function sSt(L){let g,v,p,m,_,d,h,Mo,mi,_f,rt,gi,hi,SA,uf,De,We,pi,yn,RA,Ln,xn,PA,_i,$n,BA,ui,bf,Ca;return{c(){g=a("p"),v=o("If your "),p=a("code"),m=o("NewModelConfig"),_=o(" is a subclass of "),d=a("code"),h=o("PretrainedConfig"),Mo=o(`, make sure its
`),mi=a("code"),_f=o("model_type"),rt=o(" attribute is set to the same key you use when registering the config (here "),gi=a("code"),hi=o('"new-model"'),SA=o(")."),uf=l(),De=a("p"),We=o("Likewise, if your "),pi=a("code"),yn=o("NewModel"),RA=o(" is a subclass of "),Ln=a("a"),xn=o("PreTrainedModel"),PA=o(`, make sure its
`),_i=a("code"),$n=o("config_class"),BA=o(` attribute is set to the same class you use when registering the model (here
`),ui=a("code"),bf=o("NewModelConfig"),Ca=o(")."),this.h()},l(Qe){g=n(Qe,"P",{});var Ae=s(g);v=r(Ae,"If your "),p=n(Ae,"CODE",{});var ok=s(p);m=r(ok,"NewModelConfig"),ok.forEach(t),_=r(Ae," is a subclass of "),d=n(Ae,"CODE",{});var bi=s(d);h=r(bi,"PretrainedConfig"),bi.forEach(t),Mo=r(Ae,`, make sure its
`),mi=n(Ae,"CODE",{});var rk=s(mi);_f=r(rk,"model_type"),rk.forEach(t),rt=r(Ae," attribute is set to the same key you use when registering the config (here "),gi=n(Ae,"CODE",{});var tk=s(gi);hi=r(tk,'"new-model"'),tk.forEach(t),SA=r(Ae,")."),Ae.forEach(t),uf=i(Qe),De=n(Qe,"P",{});var Eo=s(De);We=r(Eo,"Likewise, if your "),pi=n(Eo,"CODE",{});var wa=s(pi);yn=r(wa,"NewModel"),wa.forEach(t),RA=r(Eo," is a subclass of "),Ln=n(Eo,"A",{href:!0});var ak=s(Ln);xn=r(ak,"PreTrainedModel"),ak.forEach(t),PA=r(Eo,`, make sure its
`),_i=n(Eo,"CODE",{});var vf=s(_i);$n=r(vf,"config_class"),vf.forEach(t),BA=r(Eo,` attribute is set to the same class you use when registering the model (here
`),ui=n(Eo,"CODE",{});var nk=s(ui);bf=r(nk,"NewModelConfig"),nk.forEach(t),Ca=r(Eo,")."),Eo.forEach(t),this.h()},h(){c(Ln,"href","/docs/transformers/pr_17548/en/main_classes/model#transformers.PreTrainedModel")},m(Qe,Ae){b(Qe,g,Ae),e(g,v),e(g,p),e(p,m),e(g,_),e(g,d),e(d,h),e(g,Mo),e(g,mi),e(mi,_f),e(g,rt),e(g,gi),e(gi,hi),e(g,SA),b(Qe,uf,Ae),b(Qe,De,Ae),e(De,We),e(De,pi),e(pi,yn),e(De,RA),e(De,Ln),e(Ln,xn),e(De,PA),e(De,_i),e(_i,$n),e(De,BA),e(De,ui),e(ui,bf),e(De,Ca)},d(Qe){Qe&&t(g),Qe&&t(uf),Qe&&t(De)}}}function lSt(L){let g,v,p,m,_;return m=new P({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"),v=o("Examples:"),p=l(),F(m.$$.fragment)},l(d){g=n(d,"P",{});var h=s(g);v=r(h,"Examples:"),h.forEach(t),p=i(d),T(m.$$.fragment,d)},m(d,h){b(d,g,h),e(g,v),b(d,p,h),M(m,d,h),_=!0},p:I,i(d){_||(E(m.$$.fragment,d),_=!0)},o(d){C(m.$$.fragment,d),_=!1},d(d){d&&t(g),d&&t(p),w(m,d)}}}function iSt(L){let g,v,p,m,_;return m=new P({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"),v=o("Examples:"),p=l(),F(m.$$.fragment)},l(d){g=n(d,"P",{});var h=s(g);v=r(h,"Examples:"),h.forEach(t),p=i(d),T(m.$$.fragment,d)},m(d,h){b(d,g,h),e(g,v),b(d,p,h),M(m,d,h),_=!0},p:I,i(d){_||(E(m.$$.fragment,d),_=!0)},o(d){C(m.$$.fragment,d),_=!1},d(d){d&&t(g),d&&t(p),w(m,d)}}}function dSt(L){let g,v,p,m,_;return{c(){g=a("p"),v=o("Passing "),p=a("code"),m=o("use_auth_token=True"),_=o(" is required when you want to use a private model.")},l(d){g=n(d,"P",{});var h=s(g);v=r(h,"Passing "),p=n(h,"CODE",{});var Mo=s(p);m=r(Mo,"use_auth_token=True"),Mo.forEach(t),_=r(h," is required when you want to use a private model."),h.forEach(t)},m(d,h){b(d,g,h),e(g,v),e(g,p),e(p,m),e(g,_)},d(d){d&&t(g)}}}function cSt(L){let g,v,p,m,_;return m=new P({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"),v=o("Examples:"),p=l(),F(m.$$.fragment)},l(d){g=n(d,"P",{});var h=s(g);v=r(h,"Examples:"),h.forEach(t),p=i(d),T(m.$$.fragment,d)},m(d,h){b(d,g,h),e(g,v),b(d,p,h),M(m,d,h),_=!0},p:I,i(d){_||(E(m.$$.fragment,d),_=!0)},o(d){C(m.$$.fragment,d),_=!1},d(d){d&&t(g),d&&t(p),w(m,d)}}}function fSt(L){let g,v,p,m,_;return{c(){g=a("p"),v=o("Passing "),p=a("code"),m=o("use_auth_token=True"),_=o(" is required when you want to use a private model.")},l(d){g=n(d,"P",{});var h=s(g);v=r(h,"Passing "),p=n(h,"CODE",{});var Mo=s(p);m=r(Mo,"use_auth_token=True"),Mo.forEach(t),_=r(h," is required when you want to use a private model."),h.forEach(t)},m(d,h){b(d,g,h),e(g,v),e(g,p),e(p,m),e(g,_)},d(d){d&&t(g)}}}function mSt(L){let g,v,p,m,_;return m=new P({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"),v=o("Examples:"),p=l(),F(m.$$.fragment)},l(d){g=n(d,"P",{});var h=s(g);v=r(h,"Examples:"),h.forEach(t),p=i(d),T(m.$$.fragment,d)},m(d,h){b(d,g,h),e(g,v),b(d,p,h),M(m,d,h),_=!0},p:I,i(d){_||(E(m.$$.fragment,d),_=!0)},o(d){C(m.$$.fragment,d),_=!1},d(d){d&&t(g),d&&t(p),w(m,d)}}}function gSt(L){let g,v,p,m,_;return m=new P({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"),v=o("Examples:"),p=l(),F(m.$$.fragment)},l(d){g=n(d,"P",{});var h=s(g);v=r(h,"Examples:"),h.forEach(t),p=i(d),T(m.$$.fragment,d)},m(d,h){b(d,g,h),e(g,v),b(d,p,h),M(m,d,h),_=!0},p:I,i(d){_||(E(m.$$.fragment,d),_=!0)},o(d){C(m.$$.fragment,d),_=!1},d(d){d&&t(g),d&&t(p),w(m,d)}}}function hSt(L){let g,v,p,m,_;return m=new P({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"),v=o("Examples:"),p=l(),F(m.$$.fragment)},l(d){g=n(d,"P",{});var h=s(g);v=r(h,"Examples:"),h.forEach(t),p=i(d),T(m.$$.fragment,d)},m(d,h){b(d,g,h),e(g,v),b(d,p,h),M(m,d,h),_=!0},p:I,i(d){_||(E(m.$$.fragment,d),_=!0)},o(d){C(m.$$.fragment,d),_=!1},d(d){d&&t(g),d&&t(p),w(m,d)}}}function pSt(L){let g,v,p,m,_;return m=new P({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"),v=o("Examples:"),p=l(),F(m.$$.fragment)},l(d){g=n(d,"P",{});var h=s(g);v=r(h,"Examples:"),h.forEach(t),p=i(d),T(m.$$.fragment,d)},m(d,h){b(d,g,h),e(g,v),b(d,p,h),M(m,d,h),_=!0},p:I,i(d){_||(E(m.$$.fragment,d),_=!0)},o(d){C(m.$$.fragment,d),_=!1},d(d){d&&t(g),d&&t(p),w(m,d)}}}function _St(L){let g,v,p,m,_;return m=new P({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"),v=o("Examples:"),p=l(),F(m.$$.fragment)},l(d){g=n(d,"P",{});var h=s(g);v=r(h,"Examples:"),h.forEach(t),p=i(d),T(m.$$.fragment,d)},m(d,h){b(d,g,h),e(g,v),b(d,p,h),M(m,d,h),_=!0},p:I,i(d){_||(E(m.$$.fragment,d),_=!0)},o(d){C(m.$$.fragment,d),_=!1},d(d){d&&t(g),d&&t(p),w(m,d)}}}function uSt(L){let g,v,p,m,_;return m=new P({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"),v=o("Examples:"),p=l(),F(m.$$.fragment)},l(d){g=n(d,"P",{});var h=s(g);v=r(h,"Examples:"),h.forEach(t),p=i(d),T(m.$$.fragment,d)},m(d,h){b(d,g,h),e(g,v),b(d,p,h),M(m,d,h),_=!0},p:I,i(d){_||(E(m.$$.fragment,d),_=!0)},o(d){C(m.$$.fragment,d),_=!1},d(d){d&&t(g),d&&t(p),w(m,d)}}}function bSt(L){let g,v,p,m,_;return m=new P({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"),v=o("Examples:"),p=l(),F(m.$$.fragment)},l(d){g=n(d,"P",{});var h=s(g);v=r(h,"Examples:"),h.forEach(t),p=i(d),T(m.$$.fragment,d)},m(d,h){b(d,g,h),e(g,v),b(d,p,h),M(m,d,h),_=!0},p:I,i(d){_||(E(m.$$.fragment,d),_=!0)},o(d){C(m.$$.fragment,d),_=!1},d(d){d&&t(g),d&&t(p),w(m,d)}}}function vSt(L){let g,v,p,m,_;return m=new P({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"),v=o("Examples:"),p=l(),F(m.$$.fragment)},l(d){g=n(d,"P",{});var h=s(g);v=r(h,"Examples:"),h.forEach(t),p=i(d),T(m.$$.fragment,d)},m(d,h){b(d,g,h),e(g,v),b(d,p,h),M(m,d,h),_=!0},p:I,i(d){_||(E(m.$$.fragment,d),_=!0)},o(d){C(m.$$.fragment,d),_=!1},d(d){d&&t(g),d&&t(p),w(m,d)}}}function FSt(L){let g,v,p,m,_;return m=new P({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"),v=o("Examples:"),p=l(),F(m.$$.fragment)},l(d){g=n(d,"P",{});var h=s(g);v=r(h,"Examples:"),h.forEach(t),p=i(d),T(m.$$.fragment,d)},m(d,h){b(d,g,h),e(g,v),b(d,p,h),M(m,d,h),_=!0},p:I,i(d){_||(E(m.$$.fragment,d),_=!0)},o(d){C(m.$$.fragment,d),_=!1},d(d){d&&t(g),d&&t(p),w(m,d)}}}function TSt(L){let g,v,p,m,_;return m=new P({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"),v=o("Examples:"),p=l(),F(m.$$.fragment)},l(d){g=n(d,"P",{});var h=s(g);v=r(h,"Examples:"),h.forEach(t),p=i(d),T(m.$$.fragment,d)},m(d,h){b(d,g,h),e(g,v),b(d,p,h),M(m,d,h),_=!0},p:I,i(d){_||(E(m.$$.fragment,d),_=!0)},o(d){C(m.$$.fragment,d),_=!1},d(d){d&&t(g),d&&t(p),w(m,d)}}}function MSt(L){let g,v,p,m,_;return m=new P({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"),v=o("Examples:"),p=l(),F(m.$$.fragment)},l(d){g=n(d,"P",{});var h=s(g);v=r(h,"Examples:"),h.forEach(t),p=i(d),T(m.$$.fragment,d)},m(d,h){b(d,g,h),e(g,v),b(d,p,h),M(m,d,h),_=!0},p:I,i(d){_||(E(m.$$.fragment,d),_=!0)},o(d){C(m.$$.fragment,d),_=!1},d(d){d&&t(g),d&&t(p),w(m,d)}}}function ESt(L){let g,v,p,m,_;return m=new P({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"),v=o("Examples:"),p=l(),F(m.$$.fragment)},l(d){g=n(d,"P",{});var h=s(g);v=r(h,"Examples:"),h.forEach(t),p=i(d),T(m.$$.fragment,d)},m(d,h){b(d,g,h),e(g,v),b(d,p,h),M(m,d,h),_=!0},p:I,i(d){_||(E(m.$$.fragment,d),_=!0)},o(d){C(m.$$.fragment,d),_=!1},d(d){d&&t(g),d&&t(p),w(m,d)}}}function CSt(L){let g,v,p,m,_;return m=new P({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"),v=o("Examples:"),p=l(),F(m.$$.fragment)},l(d){g=n(d,"P",{});var h=s(g);v=r(h,"Examples:"),h.forEach(t),p=i(d),T(m.$$.fragment,d)},m(d,h){b(d,g,h),e(g,v),b(d,p,h),M(m,d,h),_=!0},p:I,i(d){_||(E(m.$$.fragment,d),_=!0)},o(d){C(m.$$.fragment,d),_=!1},d(d){d&&t(g),d&&t(p),w(m,d)}}}function wSt(L){let g,v,p,m,_;return m=new P({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"),v=o("Examples:"),p=l(),F(m.$$.fragment)},l(d){g=n(d,"P",{});var h=s(g);v=r(h,"Examples:"),h.forEach(t),p=i(d),T(m.$$.fragment,d)},m(d,h){b(d,g,h),e(g,v),b(d,p,h),M(m,d,h),_=!0},p:I,i(d){_||(E(m.$$.fragment,d),_=!0)},o(d){C(m.$$.fragment,d),_=!1},d(d){d&&t(g),d&&t(p),w(m,d)}}}function ASt(L){let g,v,p,m,_;return m=new P({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"),v=o("Examples:"),p=l(),F(m.$$.fragment)},l(d){g=n(d,"P",{});var h=s(g);v=r(h,"Examples:"),h.forEach(t),p=i(d),T(m.$$.fragment,d)},m(d,h){b(d,g,h),e(g,v),b(d,p,h),M(m,d,h),_=!0},p:I,i(d){_||(E(m.$$.fragment,d),_=!0)},o(d){C(m.$$.fragment,d),_=!1},d(d){d&&t(g),d&&t(p),w(m,d)}}}function ySt(L){let g,v,p,m,_;return m=new P({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"),v=o("Examples:"),p=l(),F(m.$$.fragment)},l(d){g=n(d,"P",{});var h=s(g);v=r(h,"Examples:"),h.forEach(t),p=i(d),T(m.$$.fragment,d)},m(d,h){b(d,g,h),e(g,v),b(d,p,h),M(m,d,h),_=!0},p:I,i(d){_||(E(m.$$.fragment,d),_=!0)},o(d){C(m.$$.fragment,d),_=!1},d(d){d&&t(g),d&&t(p),w(m,d)}}}function LSt(L){let g,v,p,m,_;return m=new P({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"),v=o("Examples:"),p=l(),F(m.$$.fragment)},l(d){g=n(d,"P",{});var h=s(g);v=r(h,"Examples:"),h.forEach(t),p=i(d),T(m.$$.fragment,d)},m(d,h){b(d,g,h),e(g,v),b(d,p,h),M(m,d,h),_=!0},p:I,i(d){_||(E(m.$$.fragment,d),_=!0)},o(d){C(m.$$.fragment,d),_=!1},d(d){d&&t(g),d&&t(p),w(m,d)}}}function xSt(L){let g,v,p,m,_;return m=new P({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"),v=o("Examples:"),p=l(),F(m.$$.fragment)},l(d){g=n(d,"P",{});var h=s(g);v=r(h,"Examples:"),h.forEach(t),p=i(d),T(m.$$.fragment,d)},m(d,h){b(d,g,h),e(g,v),b(d,p,h),M(m,d,h),_=!0},p:I,i(d){_||(E(m.$$.fragment,d),_=!0)},o(d){C(m.$$.fragment,d),_=!1},d(d){d&&t(g),d&&t(p),w(m,d)}}}function $St(L){let g,v,p,m,_;return m=new P({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"),v=o("Examples:"),p=l(),F(m.$$.fragment)},l(d){g=n(d,"P",{});var h=s(g);v=r(h,"Examples:"),h.forEach(t),p=i(d),T(m.$$.fragment,d)},m(d,h){b(d,g,h),e(g,v),b(d,p,h),M(m,d,h),_=!0},p:I,i(d){_||(E(m.$$.fragment,d),_=!0)},o(d){C(m.$$.fragment,d),_=!1},d(d){d&&t(g),d&&t(p),w(m,d)}}}function kSt(L){let g,v,p,m,_;return m=new P({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"),v=o("Examples:"),p=l(),F(m.$$.fragment)},l(d){g=n(d,"P",{});var h=s(g);v=r(h,"Examples:"),h.forEach(t),p=i(d),T(m.$$.fragment,d)},m(d,h){b(d,g,h),e(g,v),b(d,p,h),M(m,d,h),_=!0},p:I,i(d){_||(E(m.$$.fragment,d),_=!0)},o(d){C(m.$$.fragment,d),_=!1},d(d){d&&t(g),d&&t(p),w(m,d)}}}function SSt(L){let g,v,p,m,_;return m=new P({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"),v=o("Examples:"),p=l(),F(m.$$.fragment)},l(d){g=n(d,"P",{});var h=s(g);v=r(h,"Examples:"),h.forEach(t),p=i(d),T(m.$$.fragment,d)},m(d,h){b(d,g,h),e(g,v),b(d,p,h),M(m,d,h),_=!0},p:I,i(d){_||(E(m.$$.fragment,d),_=!0)},o(d){C(m.$$.fragment,d),_=!1},d(d){d&&t(g),d&&t(p),w(m,d)}}}function RSt(L){let g,v,p,m,_;return m=new P({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"),v=o("Examples:"),p=l(),F(m.$$.fragment)},l(d){g=n(d,"P",{});var h=s(g);v=r(h,"Examples:"),h.forEach(t),p=i(d),T(m.$$.fragment,d)},m(d,h){b(d,g,h),e(g,v),b(d,p,h),M(m,d,h),_=!0},p:I,i(d){_||(E(m.$$.fragment,d),_=!0)},o(d){C(m.$$.fragment,d),_=!1},d(d){d&&t(g),d&&t(p),w(m,d)}}}function PSt(L){let g,v,p,m,_;return m=new P({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"),v=o("Examples:"),p=l(),F(m.$$.fragment)},l(d){g=n(d,"P",{});var h=s(g);v=r(h,"Examples:"),h.forEach(t),p=i(d),T(m.$$.fragment,d)},m(d,h){b(d,g,h),e(g,v),b(d,p,h),M(m,d,h),_=!0},p:I,i(d){_||(E(m.$$.fragment,d),_=!0)},o(d){C(m.$$.fragment,d),_=!1},d(d){d&&t(g),d&&t(p),w(m,d)}}}function BSt(L){let g,v,p,m,_;return m=new P({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"),v=o("Examples:"),p=l(),F(m.$$.fragment)},l(d){g=n(d,"P",{});var h=s(g);v=r(h,"Examples:"),h.forEach(t),p=i(d),T(m.$$.fragment,d)},m(d,h){b(d,g,h),e(g,v),b(d,p,h),M(m,d,h),_=!0},p:I,i(d){_||(E(m.$$.fragment,d),_=!0)},o(d){C(m.$$.fragment,d),_=!1},d(d){d&&t(g),d&&t(p),w(m,d)}}}function ISt(L){let g,v,p,m,_;return m=new P({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"),v=o("Examples:"),p=l(),F(m.$$.fragment)},l(d){g=n(d,"P",{});var h=s(g);v=r(h,"Examples:"),h.forEach(t),p=i(d),T(m.$$.fragment,d)},m(d,h){b(d,g,h),e(g,v),b(d,p,h),M(m,d,h),_=!0},p:I,i(d){_||(E(m.$$.fragment,d),_=!0)},o(d){C(m.$$.fragment,d),_=!1},d(d){d&&t(g),d&&t(p),w(m,d)}}}function NSt(L){let g,v,p,m,_;return m=new P({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"),v=o("Examples:"),p=l(),F(m.$$.fragment)},l(d){g=n(d,"P",{});var h=s(g);v=r(h,"Examples:"),h.forEach(t),p=i(d),T(m.$$.fragment,d)},m(d,h){b(d,g,h),e(g,v),b(d,p,h),M(m,d,h),_=!0},p:I,i(d){_||(E(m.$$.fragment,d),_=!0)},o(d){C(m.$$.fragment,d),_=!1},d(d){d&&t(g),d&&t(p),w(m,d)}}}function qSt(L){let g,v,p,m,_;return m=new P({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"),v=o("Examples:"),p=l(),F(m.$$.fragment)},l(d){g=n(d,"P",{});var h=s(g);v=r(h,"Examples:"),h.forEach(t),p=i(d),T(m.$$.fragment,d)},m(d,h){b(d,g,h),e(g,v),b(d,p,h),M(m,d,h),_=!0},p:I,i(d){_||(E(m.$$.fragment,d),_=!0)},o(d){C(m.$$.fragment,d),_=!1},d(d){d&&t(g),d&&t(p),w(m,d)}}}function jSt(L){let g,v,p,m,_;return m=new P({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"),v=o("Examples:"),p=l(),F(m.$$.fragment)},l(d){g=n(d,"P",{});var h=s(g);v=r(h,"Examples:"),h.forEach(t),p=i(d),T(m.$$.fragment,d)},m(d,h){b(d,g,h),e(g,v),b(d,p,h),M(m,d,h),_=!0},p:I,i(d){_||(E(m.$$.fragment,d),_=!0)},o(d){C(m.$$.fragment,d),_=!1},d(d){d&&t(g),d&&t(p),w(m,d)}}}function DSt(L){let g,v,p,m,_;return m=new P({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"),v=o("Examples:"),p=l(),F(m.$$.fragment)},l(d){g=n(d,"P",{});var h=s(g);v=r(h,"Examples:"),h.forEach(t),p=i(d),T(m.$$.fragment,d)},m(d,h){b(d,g,h),e(g,v),b(d,p,h),M(m,d,h),_=!0},p:I,i(d){_||(E(m.$$.fragment,d),_=!0)},o(d){C(m.$$.fragment,d),_=!1},d(d){d&&t(g),d&&t(p),w(m,d)}}}function GSt(L){let g,v,p,m,_;return m=new P({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"),v=o("Examples:"),p=l(),F(m.$$.fragment)},l(d){g=n(d,"P",{});var h=s(g);v=r(h,"Examples:"),h.forEach(t),p=i(d),T(m.$$.fragment,d)},m(d,h){b(d,g,h),e(g,v),b(d,p,h),M(m,d,h),_=!0},p:I,i(d){_||(E(m.$$.fragment,d),_=!0)},o(d){C(m.$$.fragment,d),_=!1},d(d){d&&t(g),d&&t(p),w(m,d)}}}function OSt(L){let g,v,p,m,_;return m=new P({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"),v=o("Examples:"),p=l(),F(m.$$.fragment)},l(d){g=n(d,"P",{});var h=s(g);v=r(h,"Examples:"),h.forEach(t),p=i(d),T(m.$$.fragment,d)},m(d,h){b(d,g,h),e(g,v),b(d,p,h),M(m,d,h),_=!0},p:I,i(d){_||(E(m.$$.fragment,d),_=!0)},o(d){C(m.$$.fragment,d),_=!1},d(d){d&&t(g),d&&t(p),w(m,d)}}}function VSt(L){let g,v,p,m,_;return m=new P({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"),v=o("Examples:"),p=l(),F(m.$$.fragment)},l(d){g=n(d,"P",{});var h=s(g);v=r(h,"Examples:"),h.forEach(t),p=i(d),T(m.$$.fragment,d)},m(d,h){b(d,g,h),e(g,v),b(d,p,h),M(m,d,h),_=!0},p:I,i(d){_||(E(m.$$.fragment,d),_=!0)},o(d){C(m.$$.fragment,d),_=!1},d(d){d&&t(g),d&&t(p),w(m,d)}}}function XSt(L){let g,v,p,m,_;return m=new P({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"),v=o("Examples:"),p=l(),F(m.$$.fragment)},l(d){g=n(d,"P",{});var h=s(g);v=r(h,"Examples:"),h.forEach(t),p=i(d),T(m.$$.fragment,d)},m(d,h){b(d,g,h),e(g,v),b(d,p,h),M(m,d,h),_=!0},p:I,i(d){_||(E(m.$$.fragment,d),_=!0)},o(d){C(m.$$.fragment,d),_=!1},d(d){d&&t(g),d&&t(p),w(m,d)}}}function zSt(L){let g,v,p,m,_;return m=new P({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"),v=o("Examples:"),p=l(),F(m.$$.fragment)},l(d){g=n(d,"P",{});var h=s(g);v=r(h,"Examples:"),h.forEach(t),p=i(d),T(m.$$.fragment,d)},m(d,h){b(d,g,h),e(g,v),b(d,p,h),M(m,d,h),_=!0},p:I,i(d){_||(E(m.$$.fragment,d),_=!0)},o(d){C(m.$$.fragment,d),_=!1},d(d){d&&t(g),d&&t(p),w(m,d)}}}function WSt(L){let g,v,p,m,_;return m=new P({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"),v=o("Examples:"),p=l(),F(m.$$.fragment)},l(d){g=n(d,"P",{});var h=s(g);v=r(h,"Examples:"),h.forEach(t),p=i(d),T(m.$$.fragment,d)},m(d,h){b(d,g,h),e(g,v),b(d,p,h),M(m,d,h),_=!0},p:I,i(d){_||(E(m.$$.fragment,d),_=!0)},o(d){C(m.$$.fragment,d),_=!1},d(d){d&&t(g),d&&t(p),w(m,d)}}}function QSt(L){let g,v,p,m,_;return m=new P({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"),v=o("Examples:"),p=l(),F(m.$$.fragment)},l(d){g=n(d,"P",{});var h=s(g);v=r(h,"Examples:"),h.forEach(t),p=i(d),T(m.$$.fragment,d)},m(d,h){b(d,g,h),e(g,v),b(d,p,h),M(m,d,h),_=!0},p:I,i(d){_||(E(m.$$.fragment,d),_=!0)},o(d){C(m.$$.fragment,d),_=!1},d(d){d&&t(g),d&&t(p),w(m,d)}}}function HSt(L){let g,v,p,m,_;return m=new P({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"),v=o("Examples:"),p=l(),F(m.$$.fragment)},l(d){g=n(d,"P",{});var h=s(g);v=r(h,"Examples:"),h.forEach(t),p=i(d),T(m.$$.fragment,d)},m(d,h){b(d,g,h),e(g,v),b(d,p,h),M(m,d,h),_=!0},p:I,i(d){_||(E(m.$$.fragment,d),_=!0)},o(d){C(m.$$.fragment,d),_=!1},d(d){d&&t(g),d&&t(p),w(m,d)}}}function USt(L){let g,v,p,m,_;return m=new P({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"),v=o("Examples:"),p=l(),F(m.$$.fragment)},l(d){g=n(d,"P",{});var h=s(g);v=r(h,"Examples:"),h.forEach(t),p=i(d),T(m.$$.fragment,d)},m(d,h){b(d,g,h),e(g,v),b(d,p,h),M(m,d,h),_=!0},p:I,i(d){_||(E(m.$$.fragment,d),_=!0)},o(d){C(m.$$.fragment,d),_=!1},d(d){d&&t(g),d&&t(p),w(m,d)}}}function JSt(L){let g,v,p,m,_;return m=new P({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"),v=o("Examples:"),p=l(),F(m.$$.fragment)},l(d){g=n(d,"P",{});var h=s(g);v=r(h,"Examples:"),h.forEach(t),p=i(d),T(m.$$.fragment,d)},m(d,h){b(d,g,h),e(g,v),b(d,p,h),M(m,d,h),_=!0},p:I,i(d){_||(E(m.$$.fragment,d),_=!0)},o(d){C(m.$$.fragment,d),_=!1},d(d){d&&t(g),d&&t(p),w(m,d)}}}function YSt(L){let g,v,p,m,_;return m=new P({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"),v=o("Examples:"),p=l(),F(m.$$.fragment)},l(d){g=n(d,"P",{});var h=s(g);v=r(h,"Examples:"),h.forEach(t),p=i(d),T(m.$$.fragment,d)},m(d,h){b(d,g,h),e(g,v),b(d,p,h),M(m,d,h),_=!0},p:I,i(d){_||(E(m.$$.fragment,d),_=!0)},o(d){C(m.$$.fragment,d),_=!1},d(d){d&&t(g),d&&t(p),w(m,d)}}}function KSt(L){let g,v,p,m,_;return m=new P({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"),v=o("Examples:"),p=l(),F(m.$$.fragment)},l(d){g=n(d,"P",{});var h=s(g);v=r(h,"Examples:"),h.forEach(t),p=i(d),T(m.$$.fragment,d)},m(d,h){b(d,g,h),e(g,v),b(d,p,h),M(m,d,h),_=!0},p:I,i(d){_||(E(m.$$.fragment,d),_=!0)},o(d){C(m.$$.fragment,d),_=!1},d(d){d&&t(g),d&&t(p),w(m,d)}}}function ZSt(L){let g,v,p,m,_;return m=new P({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"),v=o("Examples:"),p=l(),F(m.$$.fragment)},l(d){g=n(d,"P",{});var h=s(g);v=r(h,"Examples:"),h.forEach(t),p=i(d),T(m.$$.fragment,d)},m(d,h){b(d,g,h),e(g,v),b(d,p,h),M(m,d,h),_=!0},p:I,i(d){_||(E(m.$$.fragment,d),_=!0)},o(d){C(m.$$.fragment,d),_=!1},d(d){d&&t(g),d&&t(p),w(m,d)}}}function eRt(L){let g,v,p,m,_;return m=new P({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"),v=o("Examples:"),p=l(),F(m.$$.fragment)},l(d){g=n(d,"P",{});var h=s(g);v=r(h,"Examples:"),h.forEach(t),p=i(d),T(m.$$.fragment,d)},m(d,h){b(d,g,h),e(g,v),b(d,p,h),M(m,d,h),_=!0},p:I,i(d){_||(E(m.$$.fragment,d),_=!0)},o(d){C(m.$$.fragment,d),_=!1},d(d){d&&t(g),d&&t(p),w(m,d)}}}function oRt(L){let g,v,p,m,_;return m=new P({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"),v=o("Examples:"),p=l(),F(m.$$.fragment)},l(d){g=n(d,"P",{});var h=s(g);v=r(h,"Examples:"),h.forEach(t),p=i(d),T(m.$$.fragment,d)},m(d,h){b(d,g,h),e(g,v),b(d,p,h),M(m,d,h),_=!0},p:I,i(d){_||(E(m.$$.fragment,d),_=!0)},o(d){C(m.$$.fragment,d),_=!1},d(d){d&&t(g),d&&t(p),w(m,d)}}}function rRt(L){let g,v,p,m,_;return m=new P({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"),v=o("Examples:"),p=l(),F(m.$$.fragment)},l(d){g=n(d,"P",{});var h=s(g);v=r(h,"Examples:"),h.forEach(t),p=i(d),T(m.$$.fragment,d)},m(d,h){b(d,g,h),e(g,v),b(d,p,h),M(m,d,h),_=!0},p:I,i(d){_||(E(m.$$.fragment,d),_=!0)},o(d){C(m.$$.fragment,d),_=!1},d(d){d&&t(g),d&&t(p),w(m,d)}}}function tRt(L){let g,v,p,m,_;return m=new P({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"),v=o("Examples:"),p=l(),F(m.$$.fragment)},l(d){g=n(d,"P",{});var h=s(g);v=r(h,"Examples:"),h.forEach(t),p=i(d),T(m.$$.fragment,d)},m(d,h){b(d,g,h),e(g,v),b(d,p,h),M(m,d,h),_=!0},p:I,i(d){_||(E(m.$$.fragment,d),_=!0)},o(d){C(m.$$.fragment,d),_=!1},d(d){d&&t(g),d&&t(p),w(m,d)}}}function aRt(L){let g,v,p,m,_;return m=new P({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"),v=o("Examples:"),p=l(),F(m.$$.fragment)},l(d){g=n(d,"P",{});var h=s(g);v=r(h,"Examples:"),h.forEach(t),p=i(d),T(m.$$.fragment,d)},m(d,h){b(d,g,h),e(g,v),b(d,p,h),M(m,d,h),_=!0},p:I,i(d){_||(E(m.$$.fragment,d),_=!0)},o(d){C(m.$$.fragment,d),_=!1},d(d){d&&t(g),d&&t(p),w(m,d)}}}function nRt(L){let g,v,p,m,_;return m=new P({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"),v=o("Examples:"),p=l(),F(m.$$.fragment)},l(d){g=n(d,"P",{});var h=s(g);v=r(h,"Examples:"),h.forEach(t),p=i(d),T(m.$$.fragment,d)},m(d,h){b(d,g,h),e(g,v),b(d,p,h),M(m,d,h),_=!0},p:I,i(d){_||(E(m.$$.fragment,d),_=!0)},o(d){C(m.$$.fragment,d),_=!1},d(d){d&&t(g),d&&t(p),w(m,d)}}}function sRt(L){let g,v,p,m,_;return m=new P({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"),v=o("Examples:"),p=l(),F(m.$$.fragment)},l(d){g=n(d,"P",{});var h=s(g);v=r(h,"Examples:"),h.forEach(t),p=i(d),T(m.$$.fragment,d)},m(d,h){b(d,g,h),e(g,v),b(d,p,h),M(m,d,h),_=!0},p:I,i(d){_||(E(m.$$.fragment,d),_=!0)},o(d){C(m.$$.fragment,d),_=!1},d(d){d&&t(g),d&&t(p),w(m,d)}}}function lRt(L){let g,v,p,m,_;return m=new P({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"),v=o("Examples:"),p=l(),F(m.$$.fragment)},l(d){g=n(d,"P",{});var h=s(g);v=r(h,"Examples:"),h.forEach(t),p=i(d),T(m.$$.fragment,d)},m(d,h){b(d,g,h),e(g,v),b(d,p,h),M(m,d,h),_=!0},p:I,i(d){_||(E(m.$$.fragment,d),_=!0)},o(d){C(m.$$.fragment,d),_=!1},d(d){d&&t(g),d&&t(p),w(m,d)}}}function iRt(L){let g,v,p,m,_;return m=new P({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"),v=o("Examples:"),p=l(),F(m.$$.fragment)},l(d){g=n(d,"P",{});var h=s(g);v=r(h,"Examples:"),h.forEach(t),p=i(d),T(m.$$.fragment,d)},m(d,h){b(d,g,h),e(g,v),b(d,p,h),M(m,d,h),_=!0},p:I,i(d){_||(E(m.$$.fragment,d),_=!0)},o(d){C(m.$$.fragment,d),_=!1},d(d){d&&t(g),d&&t(p),w(m,d)}}}function dRt(L){let g,v,p,m,_;return m=new P({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"),v=o("Examples:"),p=l(),F(m.$$.fragment)},l(d){g=n(d,"P",{});var h=s(g);v=r(h,"Examples:"),h.forEach(t),p=i(d),T(m.$$.fragment,d)},m(d,h){b(d,g,h),e(g,v),b(d,p,h),M(m,d,h),_=!0},p:I,i(d){_||(E(m.$$.fragment,d),_=!0)},o(d){C(m.$$.fragment,d),_=!1},d(d){d&&t(g),d&&t(p),w(m,d)}}}function cRt(L){let g,v,p,m,_;return m=new P({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"),v=o("Examples:"),p=l(),F(m.$$.fragment)},l(d){g=n(d,"P",{});var h=s(g);v=r(h,"Examples:"),h.forEach(t),p=i(d),T(m.$$.fragment,d)},m(d,h){b(d,g,h),e(g,v),b(d,p,h),M(m,d,h),_=!0},p:I,i(d){_||(E(m.$$.fragment,d),_=!0)},o(d){C(m.$$.fragment,d),_=!1},d(d){d&&t(g),d&&t(p),w(m,d)}}}function fRt(L){let g,v,p,m,_;return m=new P({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"),v=o("Examples:"),p=l(),F(m.$$.fragment)},l(d){g=n(d,"P",{});var h=s(g);v=r(h,"Examples:"),h.forEach(t),p=i(d),T(m.$$.fragment,d)},m(d,h){b(d,g,h),e(g,v),b(d,p,h),M(m,d,h),_=!0},p:I,i(d){_||(E(m.$$.fragment,d),_=!0)},o(d){C(m.$$.fragment,d),_=!1},d(d){d&&t(g),d&&t(p),w(m,d)}}}function mRt(L){let g,v,p,m,_;return m=new P({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"),v=o("Examples:"),p=l(),F(m.$$.fragment)},l(d){g=n(d,"P",{});var h=s(g);v=r(h,"Examples:"),h.forEach(t),p=i(d),T(m.$$.fragment,d)},m(d,h){b(d,g,h),e(g,v),b(d,p,h),M(m,d,h),_=!0},p:I,i(d){_||(E(m.$$.fragment,d),_=!0)},o(d){C(m.$$.fragment,d),_=!1},d(d){d&&t(g),d&&t(p),w(m,d)}}}function gRt(L){let g,v,p,m,_;return m=new P({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"),v=o("Examples:"),p=l(),F(m.$$.fragment)},l(d){g=n(d,"P",{});var h=s(g);v=r(h,"Examples:"),h.forEach(t),p=i(d),T(m.$$.fragment,d)},m(d,h){b(d,g,h),e(g,v),b(d,p,h),M(m,d,h),_=!0},p:I,i(d){_||(E(m.$$.fragment,d),_=!0)},o(d){C(m.$$.fragment,d),_=!1},d(d){d&&t(g),d&&t(p),w(m,d)}}}function hRt(L){let g,v,p,m,_;return m=new P({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"),v=o("Examples:"),p=l(),F(m.$$.fragment)},l(d){g=n(d,"P",{});var h=s(g);v=r(h,"Examples:"),h.forEach(t),p=i(d),T(m.$$.fragment,d)},m(d,h){b(d,g,h),e(g,v),b(d,p,h),M(m,d,h),_=!0},p:I,i(d){_||(E(m.$$.fragment,d),_=!0)},o(d){C(m.$$.fragment,d),_=!1},d(d){d&&t(g),d&&t(p),w(m,d)}}}function pRt(L){let g,v,p,m,_;return m=new P({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"),v=o("Examples:"),p=l(),F(m.$$.fragment)},l(d){g=n(d,"P",{});var h=s(g);v=r(h,"Examples:"),h.forEach(t),p=i(d),T(m.$$.fragment,d)},m(d,h){b(d,g,h),e(g,v),b(d,p,h),M(m,d,h),_=!0},p:I,i(d){_||(E(m.$$.fragment,d),_=!0)},o(d){C(m.$$.fragment,d),_=!1},d(d){d&&t(g),d&&t(p),w(m,d)}}}function _Rt(L){let g,v,p,m,_;return m=new P({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"),v=o("Examples:"),p=l(),F(m.$$.fragment)},l(d){g=n(d,"P",{});var h=s(g);v=r(h,"Examples:"),h.forEach(t),p=i(d),T(m.$$.fragment,d)},m(d,h){b(d,g,h),e(g,v),b(d,p,h),M(m,d,h),_=!0},p:I,i(d){_||(E(m.$$.fragment,d),_=!0)},o(d){C(m.$$.fragment,d),_=!1},d(d){d&&t(g),d&&t(p),w(m,d)}}}function uRt(L){let g,v,p,m,_;return m=new P({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"),v=o("Examples:"),p=l(),F(m.$$.fragment)},l(d){g=n(d,"P",{});var h=s(g);v=r(h,"Examples:"),h.forEach(t),p=i(d),T(m.$$.fragment,d)},m(d,h){b(d,g,h),e(g,v),b(d,p,h),M(m,d,h),_=!0},p:I,i(d){_||(E(m.$$.fragment,d),_=!0)},o(d){C(m.$$.fragment,d),_=!1},d(d){d&&t(g),d&&t(p),w(m,d)}}}function bRt(L){let g,v,p,m,_;return m=new P({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"),v=o("Examples:"),p=l(),F(m.$$.fragment)},l(d){g=n(d,"P",{});var h=s(g);v=r(h,"Examples:"),h.forEach(t),p=i(d),T(m.$$.fragment,d)},m(d,h){b(d,g,h),e(g,v),b(d,p,h),M(m,d,h),_=!0},p:I,i(d){_||(E(m.$$.fragment,d),_=!0)},o(d){C(m.$$.fragment,d),_=!1},d(d){d&&t(g),d&&t(p),w(m,d)}}}function vRt(L){let g,v,p,m,_;return m=new P({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"),v=o("Examples:"),p=l(),F(m.$$.fragment)},l(d){g=n(d,"P",{});var h=s(g);v=r(h,"Examples:"),h.forEach(t),p=i(d),T(m.$$.fragment,d)},m(d,h){b(d,g,h),e(g,v),b(d,p,h),M(m,d,h),_=!0},p:I,i(d){_||(E(m.$$.fragment,d),_=!0)},o(d){C(m.$$.fragment,d),_=!1},d(d){d&&t(g),d&&t(p),w(m,d)}}}function FRt(L){let g,v,p,m,_;return m=new P({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"),v=o("Examples:"),p=l(),F(m.$$.fragment)},l(d){g=n(d,"P",{});var h=s(g);v=r(h,"Examples:"),h.forEach(t),p=i(d),T(m.$$.fragment,d)},m(d,h){b(d,g,h),e(g,v),b(d,p,h),M(m,d,h),_=!0},p:I,i(d){_||(E(m.$$.fragment,d),_=!0)},o(d){C(m.$$.fragment,d),_=!1},d(d){d&&t(g),d&&t(p),w(m,d)}}}function TRt(L){let g,v,p,m,_;return m=new P({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"),v=o("Examples:"),p=l(),F(m.$$.fragment)},l(d){g=n(d,"P",{});var h=s(g);v=r(h,"Examples:"),h.forEach(t),p=i(d),T(m.$$.fragment,d)},m(d,h){b(d,g,h),e(g,v),b(d,p,h),M(m,d,h),_=!0},p:I,i(d){_||(E(m.$$.fragment,d),_=!0)},o(d){C(m.$$.fragment,d),_=!1},d(d){d&&t(g),d&&t(p),w(m,d)}}}function MRt(L){let g,v,p,m,_;return m=new P({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"),v=o("Examples:"),p=l(),F(m.$$.fragment)},l(d){g=n(d,"P",{});var h=s(g);v=r(h,"Examples:"),h.forEach(t),p=i(d),T(m.$$.fragment,d)},m(d,h){b(d,g,h),e(g,v),b(d,p,h),M(m,d,h),_=!0},p:I,i(d){_||(E(m.$$.fragment,d),_=!0)},o(d){C(m.$$.fragment,d),_=!1},d(d){d&&t(g),d&&t(p),w(m,d)}}}function ERt(L){let g,v,p,m,_;return m=new P({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"),v=o("Examples:"),p=l(),F(m.$$.fragment)},l(d){g=n(d,"P",{});var h=s(g);v=r(h,"Examples:"),h.forEach(t),p=i(d),T(m.$$.fragment,d)},m(d,h){b(d,g,h),e(g,v),b(d,p,h),M(m,d,h),_=!0},p:I,i(d){_||(E(m.$$.fragment,d),_=!0)},o(d){C(m.$$.fragment,d),_=!1},d(d){d&&t(g),d&&t(p),w(m,d)}}}function CRt(L){let g,v,p,m,_;return m=new P({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"),v=o("Examples:"),p=l(),F(m.$$.fragment)},l(d){g=n(d,"P",{});var h=s(g);v=r(h,"Examples:"),h.forEach(t),p=i(d),T(m.$$.fragment,d)},m(d,h){b(d,g,h),e(g,v),b(d,p,h),M(m,d,h),_=!0},p:I,i(d){_||(E(m.$$.fragment,d),_=!0)},o(d){C(m.$$.fragment,d),_=!1},d(d){d&&t(g),d&&t(p),w(m,d)}}}function wRt(L){let g,v,p,m,_;return m=new P({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"),v=o("Examples:"),p=l(),F(m.$$.fragment)},l(d){g=n(d,"P",{});var h=s(g);v=r(h,"Examples:"),h.forEach(t),p=i(d),T(m.$$.fragment,d)},m(d,h){b(d,g,h),e(g,v),b(d,p,h),M(m,d,h),_=!0},p:I,i(d){_||(E(m.$$.fragment,d),_=!0)},o(d){C(m.$$.fragment,d),_=!1},d(d){d&&t(g),d&&t(p),w(m,d)}}}function ARt(L){let g,v,p,m,_;return m=new P({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"),v=o("Examples:"),p=l(),F(m.$$.fragment)},l(d){g=n(d,"P",{});var h=s(g);v=r(h,"Examples:"),h.forEach(t),p=i(d),T(m.$$.fragment,d)},m(d,h){b(d,g,h),e(g,v),b(d,p,h),M(m,d,h),_=!0},p:I,i(d){_||(E(m.$$.fragment,d),_=!0)},o(d){C(m.$$.fragment,d),_=!1},d(d){d&&t(g),d&&t(p),w(m,d)}}}function yRt(L){let g,v,p,m,_;return m=new P({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"),v=o("Examples:"),p=l(),F(m.$$.fragment)},l(d){g=n(d,"P",{});var h=s(g);v=r(h,"Examples:"),h.forEach(t),p=i(d),T(m.$$.fragment,d)},m(d,h){b(d,g,h),e(g,v),b(d,p,h),M(m,d,h),_=!0},p:I,i(d){_||(E(m.$$.fragment,d),_=!0)},o(d){C(m.$$.fragment,d),_=!1},d(d){d&&t(g),d&&t(p),w(m,d)}}}function LRt(L){let g,v,p,m,_;return m=new P({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"),v=o("Examples:"),p=l(),F(m.$$.fragment)},l(d){g=n(d,"P",{});var h=s(g);v=r(h,"Examples:"),h.forEach(t),p=i(d),T(m.$$.fragment,d)},m(d,h){b(d,g,h),e(g,v),b(d,p,h),M(m,d,h),_=!0},p:I,i(d){_||(E(m.$$.fragment,d),_=!0)},o(d){C(m.$$.fragment,d),_=!1},d(d){d&&t(g),d&&t(p),w(m,d)}}}function xRt(L){let g,v,p,m,_;return m=new P({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"),v=o("Examples:"),p=l(),F(m.$$.fragment)},l(d){g=n(d,"P",{});var h=s(g);v=r(h,"Examples:"),h.forEach(t),p=i(d),T(m.$$.fragment,d)},m(d,h){b(d,g,h),e(g,v),b(d,p,h),M(m,d,h),_=!0},p:I,i(d){_||(E(m.$$.fragment,d),_=!0)},o(d){C(m.$$.fragment,d),_=!1},d(d){d&&t(g),d&&t(p),w(m,d)}}}function $Rt(L){let g,v,p,m,_;return m=new P({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"),v=o("Examples:"),p=l(),F(m.$$.fragment)},l(d){g=n(d,"P",{});var h=s(g);v=r(h,"Examples:"),h.forEach(t),p=i(d),T(m.$$.fragment,d)},m(d,h){b(d,g,h),e(g,v),b(d,p,h),M(m,d,h),_=!0},p:I,i(d){_||(E(m.$$.fragment,d),_=!0)},o(d){C(m.$$.fragment,d),_=!1},d(d){d&&t(g),d&&t(p),w(m,d)}}}function kRt(L){let g,v,p,m,_;return m=new P({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"),v=o("Examples:"),p=l(),F(m.$$.fragment)},l(d){g=n(d,"P",{});var h=s(g);v=r(h,"Examples:"),h.forEach(t),p=i(d),T(m.$$.fragment,d)},m(d,h){b(d,g,h),e(g,v),b(d,p,h),M(m,d,h),_=!0},p:I,i(d){_||(E(m.$$.fragment,d),_=!0)},o(d){C(m.$$.fragment,d),_=!1},d(d){d&&t(g),d&&t(p),w(m,d)}}}function SRt(L){let g,v,p,m,_;return m=new P({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"),v=o("Examples:"),p=l(),F(m.$$.fragment)},l(d){g=n(d,"P",{});var h=s(g);v=r(h,"Examples:"),h.forEach(t),p=i(d),T(m.$$.fragment,d)},m(d,h){b(d,g,h),e(g,v),b(d,p,h),M(m,d,h),_=!0},p:I,i(d){_||(E(m.$$.fragment,d),_=!0)},o(d){C(m.$$.fragment,d),_=!1},d(d){d&&t(g),d&&t(p),w(m,d)}}}function RRt(L){let g,v,p,m,_;return m=new P({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"),v=o("Examples:"),p=l(),F(m.$$.fragment)},l(d){g=n(d,"P",{});var h=s(g);v=r(h,"Examples:"),h.forEach(t),p=i(d),T(m.$$.fragment,d)},m(d,h){b(d,g,h),e(g,v),b(d,p,h),M(m,d,h),_=!0},p:I,i(d){_||(E(m.$$.fragment,d),_=!0)},o(d){C(m.$$.fragment,d),_=!1},d(d){d&&t(g),d&&t(p),w(m,d)}}}function PRt(L){let g,v,p,m,_;return m=new P({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"),v=o("Examples:"),p=l(),F(m.$$.fragment)},l(d){g=n(d,"P",{});var h=s(g);v=r(h,"Examples:"),h.forEach(t),p=i(d),T(m.$$.fragment,d)},m(d,h){b(d,g,h),e(g,v),b(d,p,h),M(m,d,h),_=!0},p:I,i(d){_||(E(m.$$.fragment,d),_=!0)},o(d){C(m.$$.fragment,d),_=!1},d(d){d&&t(g),d&&t(p),w(m,d)}}}function BRt(L){let g,v,p,m,_;return m=new P({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"),v=o("Examples:"),p=l(),F(m.$$.fragment)},l(d){g=n(d,"P",{});var h=s(g);v=r(h,"Examples:"),h.forEach(t),p=i(d),T(m.$$.fragment,d)},m(d,h){b(d,g,h),e(g,v),b(d,p,h),M(m,d,h),_=!0},p:I,i(d){_||(E(m.$$.fragment,d),_=!0)},o(d){C(m.$$.fragment,d),_=!1},d(d){d&&t(g),d&&t(p),w(m,d)}}}function IRt(L){let g,v,p,m,_;return m=new P({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"),v=o("Examples:"),p=l(),F(m.$$.fragment)},l(d){g=n(d,"P",{});var h=s(g);v=r(h,"Examples:"),h.forEach(t),p=i(d),T(m.$$.fragment,d)},m(d,h){b(d,g,h),e(g,v),b(d,p,h),M(m,d,h),_=!0},p:I,i(d){_||(E(m.$$.fragment,d),_=!0)},o(d){C(m.$$.fragment,d),_=!1},d(d){d&&t(g),d&&t(p),w(m,d)}}}function NRt(L){let g,v,p,m,_;return m=new P({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"),v=o("Examples:"),p=l(),F(m.$$.fragment)},l(d){g=n(d,"P",{});var h=s(g);v=r(h,"Examples:"),h.forEach(t),p=i(d),T(m.$$.fragment,d)},m(d,h){b(d,g,h),e(g,v),b(d,p,h),M(m,d,h),_=!0},p:I,i(d){_||(E(m.$$.fragment,d),_=!0)},o(d){C(m.$$.fragment,d),_=!1},d(d){d&&t(g),d&&t(p),w(m,d)}}}function qRt(L){let g,v,p,m,_;return m=new P({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"),v=o("Examples:"),p=l(),F(m.$$.fragment)},l(d){g=n(d,"P",{});var h=s(g);v=r(h,"Examples:"),h.forEach(t),p=i(d),T(m.$$.fragment,d)},m(d,h){b(d,g,h),e(g,v),b(d,p,h),M(m,d,h),_=!0},p:I,i(d){_||(E(m.$$.fragment,d),_=!0)},o(d){C(m.$$.fragment,d),_=!1},d(d){d&&t(g),d&&t(p),w(m,d)}}}function jRt(L){let g,v,p,m,_;return m=new P({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"),v=o("Examples:"),p=l(),F(m.$$.fragment)},l(d){g=n(d,"P",{});var h=s(g);v=r(h,"Examples:"),h.forEach(t),p=i(d),T(m.$$.fragment,d)},m(d,h){b(d,g,h),e(g,v),b(d,p,h),M(m,d,h),_=!0},p:I,i(d){_||(E(m.$$.fragment,d),_=!0)},o(d){C(m.$$.fragment,d),_=!1},d(d){d&&t(g),d&&t(p),w(m,d)}}}function DRt(L){let g,v,p,m,_;return m=new P({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"),v=o("Examples:"),p=l(),F(m.$$.fragment)},l(d){g=n(d,"P",{});var h=s(g);v=r(h,"Examples:"),h.forEach(t),p=i(d),T(m.$$.fragment,d)},m(d,h){b(d,g,h),e(g,v),b(d,p,h),M(m,d,h),_=!0},p:I,i(d){_||(E(m.$$.fragment,d),_=!0)},o(d){C(m.$$.fragment,d),_=!1},d(d){d&&t(g),d&&t(p),w(m,d)}}}function GRt(L){let g,v,p,m,_;return m=new P({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"),v=o("Examples:"),p=l(),F(m.$$.fragment)},l(d){g=n(d,"P",{});var h=s(g);v=r(h,"Examples:"),h.forEach(t),p=i(d),T(m.$$.fragment,d)},m(d,h){b(d,g,h),e(g,v),b(d,p,h),M(m,d,h),_=!0},p:I,i(d){_||(E(m.$$.fragment,d),_=!0)},o(d){C(m.$$.fragment,d),_=!1},d(d){d&&t(g),d&&t(p),w(m,d)}}}function ORt(L){let g,v,p,m,_;return m=new P({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"),v=o("Examples:"),p=l(),F(m.$$.fragment)},l(d){g=n(d,"P",{});var h=s(g);v=r(h,"Examples:"),h.forEach(t),p=i(d),T(m.$$.fragment,d)},m(d,h){b(d,g,h),e(g,v),b(d,p,h),M(m,d,h),_=!0},p:I,i(d){_||(E(m.$$.fragment,d),_=!0)},o(d){C(m.$$.fragment,d),_=!1},d(d){d&&t(g),d&&t(p),w(m,d)}}}function VRt(L){let g,v,p,m,_;return m=new P({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"),v=o("Examples:"),p=l(),F(m.$$.fragment)},l(d){g=n(d,"P",{});var h=s(g);v=r(h,"Examples:"),h.forEach(t),p=i(d),T(m.$$.fragment,d)},m(d,h){b(d,g,h),e(g,v),b(d,p,h),M(m,d,h),_=!0},p:I,i(d){_||(E(m.$$.fragment,d),_=!0)},o(d){C(m.$$.fragment,d),_=!1},d(d){d&&t(g),d&&t(p),w(m,d)}}}function XRt(L){let g,v,p,m,_;return m=new P({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"),v=o("Examples:"),p=l(),F(m.$$.fragment)},l(d){g=n(d,"P",{});var h=s(g);v=r(h,"Examples:"),h.forEach(t),p=i(d),T(m.$$.fragment,d)},m(d,h){b(d,g,h),e(g,v),b(d,p,h),M(m,d,h),_=!0},p:I,i(d){_||(E(m.$$.fragment,d),_=!0)},o(d){C(m.$$.fragment,d),_=!1},d(d){d&&t(g),d&&t(p),w(m,d)}}}function zRt(L){let g,v,p,m,_;return m=new P({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"),v=o("Examples:"),p=l(),F(m.$$.fragment)},l(d){g=n(d,"P",{});var h=s(g);v=r(h,"Examples:"),h.forEach(t),p=i(d),T(m.$$.fragment,d)},m(d,h){b(d,g,h),e(g,v),b(d,p,h),M(m,d,h),_=!0},p:I,i(d){_||(E(m.$$.fragment,d),_=!0)},o(d){C(m.$$.fragment,d),_=!1},d(d){d&&t(g),d&&t(p),w(m,d)}}}function WRt(L){let g,v,p,m,_;return m=new P({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"),v=o("Examples:"),p=l(),F(m.$$.fragment)},l(d){g=n(d,"P",{});var h=s(g);v=r(h,"Examples:"),h.forEach(t),p=i(d),T(m.$$.fragment,d)},m(d,h){b(d,g,h),e(g,v),b(d,p,h),M(m,d,h),_=!0},p:I,i(d){_||(E(m.$$.fragment,d),_=!0)},o(d){C(m.$$.fragment,d),_=!1},d(d){d&&t(g),d&&t(p),w(m,d)}}}function QRt(L){let g,v,p,m,_;return m=new P({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"),v=o("Examples:"),p=l(),F(m.$$.fragment)},l(d){g=n(d,"P",{});var h=s(g);v=r(h,"Examples:"),h.forEach(t),p=i(d),T(m.$$.fragment,d)},m(d,h){b(d,g,h),e(g,v),b(d,p,h),M(m,d,h),_=!0},p:I,i(d){_||(E(m.$$.fragment,d),_=!0)},o(d){C(m.$$.fragment,d),_=!1},d(d){d&&t(g),d&&t(p),w(m,d)}}}function HRt(L){let g,v,p,m,_;return m=new P({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"),v=o("Examples:"),p=l(),F(m.$$.fragment)},l(d){g=n(d,"P",{});var h=s(g);v=r(h,"Examples:"),h.forEach(t),p=i(d),T(m.$$.fragment,d)},m(d,h){b(d,g,h),e(g,v),b(d,p,h),M(m,d,h),_=!0},p:I,i(d){_||(E(m.$$.fragment,d),_=!0)},o(d){C(m.$$.fragment,d),_=!1},d(d){d&&t(g),d&&t(p),w(m,d)}}}function URt(L){let g,v,p,m,_;return m=new P({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"),v=o("Examples:"),p=l(),F(m.$$.fragment)},l(d){g=n(d,"P",{});var h=s(g);v=r(h,"Examples:"),h.forEach(t),p=i(d),T(m.$$.fragment,d)},m(d,h){b(d,g,h),e(g,v),b(d,p,h),M(m,d,h),_=!0},p:I,i(d){_||(E(m.$$.fragment,d),_=!0)},o(d){C(m.$$.fragment,d),_=!1},d(d){d&&t(g),d&&t(p),w(m,d)}}}function JRt(L){let g,v,p,m,_;return m=new P({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"),v=o("Examples:"),p=l(),F(m.$$.fragment)},l(d){g=n(d,"P",{});var h=s(g);v=r(h,"Examples:"),h.forEach(t),p=i(d),T(m.$$.fragment,d)},m(d,h){b(d,g,h),e(g,v),b(d,p,h),M(m,d,h),_=!0},p:I,i(d){_||(E(m.$$.fragment,d),_=!0)},o(d){C(m.$$.fragment,d),_=!1},d(d){d&&t(g),d&&t(p),w(m,d)}}}function YRt(L){let g,v,p,m,_;return m=new P({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"),v=o("Examples:"),p=l(),F(m.$$.fragment)},l(d){g=n(d,"P",{});var h=s(g);v=r(h,"Examples:"),h.forEach(t),p=i(d),T(m.$$.fragment,d)},m(d,h){b(d,g,h),e(g,v),b(d,p,h),M(m,d,h),_=!0},p:I,i(d){_||(E(m.$$.fragment,d),_=!0)},o(d){C(m.$$.fragment,d),_=!1},d(d){d&&t(g),d&&t(p),w(m,d)}}}function KRt(L){let g,v,p,m,_;return m=new P({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"),v=o("Examples:"),p=l(),F(m.$$.fragment)},l(d){g=n(d,"P",{});var h=s(g);v=r(h,"Examples:"),h.forEach(t),p=i(d),T(m.$$.fragment,d)},m(d,h){b(d,g,h),e(g,v),b(d,p,h),M(m,d,h),_=!0},p:I,i(d){_||(E(m.$$.fragment,d),_=!0)},o(d){C(m.$$.fragment,d),_=!1},d(d){d&&t(g),d&&t(p),w(m,d)}}}function ZRt(L){let g,v,p,m,_;return m=new P({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"),v=o("Examples:"),p=l(),F(m.$$.fragment)},l(d){g=n(d,"P",{});var h=s(g);v=r(h,"Examples:"),h.forEach(t),p=i(d),T(m.$$.fragment,d)},m(d,h){b(d,g,h),e(g,v),b(d,p,h),M(m,d,h),_=!0},p:I,i(d){_||(E(m.$$.fragment,d),_=!0)},o(d){C(m.$$.fragment,d),_=!1},d(d){d&&t(g),d&&t(p),w(m,d)}}}function ePt(L){let g,v,p,m,_;return m=new P({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"),v=o("Examples:"),p=l(),F(m.$$.fragment)},l(d){g=n(d,"P",{});var h=s(g);v=r(h,"Examples:"),h.forEach(t),p=i(d),T(m.$$.fragment,d)},m(d,h){b(d,g,h),e(g,v),b(d,p,h),M(m,d,h),_=!0},p:I,i(d){_||(E(m.$$.fragment,d),_=!0)},o(d){C(m.$$.fragment,d),_=!1},d(d){d&&t(g),d&&t(p),w(m,d)}}}function oPt(L){let g,v,p,m,_;return m=new P({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"),v=o("Examples:"),p=l(),F(m.$$.fragment)},l(d){g=n(d,"P",{});var h=s(g);v=r(h,"Examples:"),h.forEach(t),p=i(d),T(m.$$.fragment,d)},m(d,h){b(d,g,h),e(g,v),b(d,p,h),M(m,d,h),_=!0},p:I,i(d){_||(E(m.$$.fragment,d),_=!0)},o(d){C(m.$$.fragment,d),_=!1},d(d){d&&t(g),d&&t(p),w(m,d)}}}function rPt(L){let g,v,p,m,_;return m=new P({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"),v=o("Examples:"),p=l(),F(m.$$.fragment)},l(d){g=n(d,"P",{});var h=s(g);v=r(h,"Examples:"),h.forEach(t),p=i(d),T(m.$$.fragment,d)},m(d,h){b(d,g,h),e(g,v),b(d,p,h),M(m,d,h),_=!0},p:I,i(d){_||(E(m.$$.fragment,d),_=!0)},o(d){C(m.$$.fragment,d),_=!1},d(d){d&&t(g),d&&t(p),w(m,d)}}}function tPt(L){let 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,yRr,kC,f7e,LRr,xRr,YY,$Rr,kRr,SRr,SC,m7e,RRr,PRr,KY,BRr,IRr,NRr,RC,g7e,qRr,jRr,ZY,DRr,GRr,ORr,PC,h7e,VRr,XRr,eK,zRr,WRr,QRr,BC,p7e,HRr,URr,oK,JRr,YRr,KRr,IC,FDe,jc,NC,_7e,U9,ZRr,u7e,ePr,TDe,hr,J9,oPr,Dc,rPr,rK,tPr,aPr,tK,nPr,sPr,lPr,Y9,iPr,b7e,dPr,cPr,fPr,Xt,K9,mPr,v7e,gPr,hPr,Gc,pPr,F7e,_Pr,uPr,aK,bPr,vPr,FPr,qC,TPr,Or,Z9,MPr,T7e,EPr,CPr,un,wPr,M7e,APr,yPr,E7e,LPr,xPr,C7e,$Pr,kPr,SPr,xe,jC,w7e,RPr,PPr,nK,BPr,IPr,NPr,DC,A7e,qPr,jPr,sK,DPr,GPr,OPr,GC,y7e,VPr,XPr,lK,zPr,WPr,QPr,OC,L7e,HPr,UPr,iK,JPr,YPr,KPr,VC,x7e,ZPr,eBr,dK,oBr,rBr,tBr,XC,$7e,aBr,nBr,cK,sBr,lBr,iBr,zC,k7e,dBr,cBr,fK,fBr,mBr,gBr,WC,S7e,hBr,pBr,mK,_Br,uBr,bBr,QC,R7e,vBr,FBr,gK,TBr,MBr,EBr,HC,P7e,CBr,wBr,hK,ABr,yBr,LBr,UC,MDe,Oc,JC,B7e,ex,xBr,I7e,$Br,EDe,pr,ox,kBr,Vc,SBr,pK,RBr,PBr,_K,BBr,IBr,NBr,rx,qBr,N7e,jBr,DBr,GBr,zt,tx,OBr,q7e,VBr,XBr,Xc,zBr,j7e,WBr,QBr,uK,HBr,UBr,JBr,YC,YBr,Vr,ax,KBr,D7e,ZBr,eIr,bn,oIr,G7e,rIr,tIr,O7e,aIr,nIr,V7e,sIr,lIr,iIr,Pe,KC,X7e,dIr,cIr,bK,fIr,mIr,gIr,ZC,z7e,hIr,pIr,vK,_Ir,uIr,bIr,e3,W7e,vIr,FIr,FK,TIr,MIr,EIr,o3,Q7e,CIr,wIr,TK,AIr,yIr,LIr,r3,H7e,xIr,$Ir,MK,kIr,SIr,RIr,t3,U7e,PIr,BIr,EK,IIr,NIr,qIr,a3,J7e,jIr,DIr,CK,GIr,OIr,VIr,n3,Y7e,XIr,zIr,wK,WIr,QIr,HIr,s3,K7e,UIr,JIr,AK,YIr,KIr,ZIr,l3,CDe,zc,i3,Z7e,nx,eNr,eMe,oNr,wDe,_r,sx,rNr,Wc,tNr,yK,aNr,nNr,LK,sNr,lNr,iNr,lx,dNr,oMe,cNr,fNr,mNr,Wt,ix,gNr,rMe,hNr,pNr,Qc,_Nr,tMe,uNr,bNr,xK,vNr,FNr,TNr,d3,MNr,Xr,dx,ENr,aMe,CNr,wNr,vn,ANr,nMe,yNr,LNr,sMe,xNr,$Nr,lMe,kNr,SNr,RNr,$e,c3,iMe,PNr,BNr,$K,INr,NNr,qNr,f3,dMe,jNr,DNr,kK,GNr,ONr,VNr,m3,cMe,XNr,zNr,SK,WNr,QNr,HNr,g3,fMe,UNr,JNr,RK,YNr,KNr,ZNr,h3,mMe,eqr,oqr,PK,rqr,tqr,aqr,p3,gMe,nqr,sqr,BK,lqr,iqr,dqr,_3,hMe,cqr,fqr,IK,mqr,gqr,hqr,u3,pMe,pqr,_qr,NK,uqr,bqr,vqr,b3,_Me,Fqr,Tqr,qK,Mqr,Eqr,Cqr,v3,uMe,wqr,Aqr,jK,yqr,Lqr,xqr,F3,ADe,Hc,T3,bMe,cx,$qr,vMe,kqr,yDe,ur,fx,Sqr,Uc,Rqr,DK,Pqr,Bqr,GK,Iqr,Nqr,qqr,mx,jqr,FMe,Dqr,Gqr,Oqr,Qt,gx,Vqr,TMe,Xqr,zqr,Jc,Wqr,MMe,Qqr,Hqr,OK,Uqr,Jqr,Yqr,M3,Kqr,zr,hx,Zqr,EMe,ejr,ojr,Fn,rjr,CMe,tjr,ajr,wMe,njr,sjr,AMe,ljr,ijr,djr,ke,E3,yMe,cjr,fjr,VK,mjr,gjr,hjr,C3,LMe,pjr,_jr,XK,ujr,bjr,vjr,w3,xMe,Fjr,Tjr,zK,Mjr,Ejr,Cjr,A3,$Me,wjr,Ajr,WK,yjr,Ljr,xjr,y3,kMe,$jr,kjr,QK,Sjr,Rjr,Pjr,L3,SMe,Bjr,Ijr,HK,Njr,qjr,jjr,x3,RMe,Djr,Gjr,UK,Ojr,Vjr,Xjr,$3,PMe,zjr,Wjr,JK,Qjr,Hjr,Ujr,k3,BMe,Jjr,Yjr,YK,Kjr,Zjr,eDr,S3,IMe,oDr,rDr,KK,tDr,aDr,nDr,R3,LDe,Yc,P3,NMe,px,sDr,qMe,lDr,xDe,br,_x,iDr,Kc,dDr,ZK,cDr,fDr,eZ,mDr,gDr,hDr,ux,pDr,jMe,_Dr,uDr,bDr,Ht,bx,vDr,DMe,FDr,TDr,Zc,MDr,GMe,EDr,CDr,oZ,wDr,ADr,yDr,B3,LDr,Wr,vx,xDr,OMe,$Dr,kDr,Tn,SDr,VMe,RDr,PDr,XMe,BDr,IDr,zMe,NDr,qDr,jDr,Ge,I3,WMe,DDr,GDr,rZ,ODr,VDr,XDr,N3,QMe,zDr,WDr,tZ,QDr,HDr,UDr,q3,HMe,JDr,YDr,aZ,KDr,ZDr,eGr,j3,UMe,oGr,rGr,nZ,tGr,aGr,nGr,D3,JMe,sGr,lGr,sZ,iGr,dGr,cGr,G3,YMe,fGr,mGr,lZ,gGr,hGr,pGr,O3,KMe,_Gr,uGr,iZ,bGr,vGr,FGr,V3,ZMe,TGr,MGr,dZ,EGr,CGr,wGr,X3,$De,ef,z3,eEe,Fx,AGr,oEe,yGr,kDe,vr,Tx,LGr,of,xGr,cZ,$Gr,kGr,fZ,SGr,RGr,PGr,Mx,BGr,rEe,IGr,NGr,qGr,Ut,Ex,jGr,tEe,DGr,GGr,rf,OGr,aEe,VGr,XGr,mZ,zGr,WGr,QGr,W3,HGr,Qr,Cx,UGr,nEe,JGr,YGr,Mn,KGr,sEe,ZGr,eOr,lEe,oOr,rOr,iEe,tOr,aOr,nOr,Oe,Q3,dEe,sOr,lOr,gZ,iOr,dOr,cOr,H3,cEe,fOr,mOr,hZ,gOr,hOr,pOr,U3,fEe,_Or,uOr,pZ,bOr,vOr,FOr,J3,mEe,TOr,MOr,_Z,EOr,COr,wOr,Y3,gEe,AOr,yOr,uZ,LOr,xOr,$Or,K3,hEe,kOr,SOr,bZ,ROr,POr,BOr,Z3,pEe,IOr,NOr,vZ,qOr,jOr,DOr,e0,_Ee,GOr,OOr,FZ,VOr,XOr,zOr,o0,SDe,tf,r0,uEe,wx,WOr,bEe,QOr,RDe,Fr,Ax,HOr,af,UOr,TZ,JOr,YOr,MZ,KOr,ZOr,eVr,yx,oVr,vEe,rVr,tVr,aVr,Jt,Lx,nVr,FEe,sVr,lVr,nf,iVr,TEe,dVr,cVr,EZ,fVr,mVr,gVr,t0,hVr,Hr,xx,pVr,MEe,_Vr,uVr,En,bVr,EEe,vVr,FVr,CEe,TVr,MVr,wEe,EVr,CVr,wVr,AEe,a0,yEe,AVr,yVr,CZ,LVr,xVr,$Vr,n0,PDe,sf,s0,LEe,$x,kVr,xEe,SVr,BDe,Tr,kx,RVr,lf,PVr,wZ,BVr,IVr,AZ,NVr,qVr,jVr,Sx,DVr,$Ee,GVr,OVr,VVr,Yt,Rx,XVr,kEe,zVr,WVr,df,QVr,SEe,HVr,UVr,yZ,JVr,YVr,KVr,l0,ZVr,Ur,Px,eXr,REe,oXr,rXr,Cn,tXr,PEe,aXr,nXr,BEe,sXr,lXr,IEe,iXr,dXr,cXr,Bx,i0,NEe,fXr,mXr,LZ,gXr,hXr,pXr,d0,qEe,_Xr,uXr,xZ,bXr,vXr,FXr,c0,IDe,cf,f0,jEe,Ix,TXr,DEe,MXr,NDe,Mr,Nx,EXr,ff,CXr,$Z,wXr,AXr,kZ,yXr,LXr,xXr,qx,$Xr,GEe,kXr,SXr,RXr,Kt,jx,PXr,OEe,BXr,IXr,mf,NXr,VEe,qXr,jXr,SZ,DXr,GXr,OXr,m0,VXr,Jr,Dx,XXr,XEe,zXr,WXr,wn,QXr,zEe,HXr,UXr,WEe,JXr,YXr,QEe,KXr,ZXr,ezr,HEe,g0,UEe,ozr,rzr,RZ,tzr,azr,nzr,h0,qDe;return d=new te({}),Ca=new P({props:{code:'model = AutoModel.from_pretrained("bert-base-cased")',highlighted:'model = AutoModel.from_pretrained(<span class="hljs-string">&quot;bert-base-cased&quot;</span>)'}}),IA=new te({}),NA=new P({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)`}}),Tf=new szr({props:{warning:!0,$$slots:{default:[sSt]},$$scope:{ctx:L}}}),qA=new te({}),jA=new R({props:{name:"class transformers.AutoConfig",anchor:"transformers.AutoConfig",parameters:[],source:"https://github.com/huggingface/transformers/blob/vr_17548/src/transformers/models/auto/configuration_auto.py#L587"}}),OA=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/pr_17548/en/main_classes/configuration#transformers.PretrainedConfig.save_pretrained">save_pretrained()</a> method, or the <a href="/docs/transformers/pr_17548/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/vr_17548/src/transformers/models/auto/configuration_auto.py#L610"}}),wg=new B({props:{anchor:"transformers.AutoConfig.from_pretrained.example",$$slots:{default:[lSt]},$$scope:{ctx:L}}}),VA=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/pr_17548/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>) &#x2014; The config to register.',name:"config"}],source:"https://github.com/huggingface/transformers/blob/vr_17548/src/transformers/models/auto/configuration_auto.py#L733"}}),XA=new te({}),zA=new R({props:{name:"class transformers.AutoTokenizer",anchor:"transformers.AutoTokenizer",parameters:[],source:"https://github.com/huggingface/transformers/blob/vr_17548/src/transformers/models/auto/tokenization_auto.py#L390"}}),HA=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/pr_17548/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/pr_17548/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;
Whether or not to try to load the fast version of the tokenizer.`,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/vr_17548/src/transformers/models/auto/tokenization_auto.py#L404"}}),sh=new B({props:{anchor:"transformers.AutoTokenizer.from_pretrained.example",$$slots:{default:[iSt]},$$scope:{ctx:L}}}),UA=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/pr_17548/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/vr_17548/src/transformers/models/auto/tokenization_auto.py#L603"}}),JA=new te({}),YA=new R({props:{name:"class transformers.AutoFeatureExtractor",anchor:"transformers.AutoFeatureExtractor",parameters:[],source:"https://github.com/huggingface/transformers/blob/vr_17548/src/transformers/models/auto/feature_extraction_auto.py#L191"}}),e6=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/pr_17548/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>transformers-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/vr_17548/src/transformers/models/auto/feature_extraction_auto.py#L205"}}),jh=new szr({props:{$$slots:{default:[dSt]},$$scope:{ctx:L}}}),Dh=new B({props:{anchor:"transformers.AutoFeatureExtractor.from_pretrained.example",$$slots:{default:[cSt]},$$scope:{ctx:L}}}),o6=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/pr_17548/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/vr_17548/src/transformers/models/auto/feature_extraction_auto.py#L332"}}),r6=new te({}),t6=new R({props:{name:"class transformers.AutoProcessor",anchor:"transformers.AutoProcessor",parameters:[],source:"https://github.com/huggingface/transformers/blob/vr_17548/src/transformers/models/auto/processing_auto.py#L88"}}),s6=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>transformers-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/vr_17548/src/transformers/models/auto/processing_auto.py#L102"}}),sp=new szr({props:{$$slots:{default:[fSt]},$$scope:{ctx:L}}}),lp=new B({props:{anchor:"transformers.AutoProcessor.from_pretrained.example",$$slots:{default:[mSt]},$$scope:{ctx:L}}}),l6=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/pr_17548/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/vr_17548/src/transformers/models/auto/processing_auto.py#L255"}}),i6=new te({}),d6=new R({props:{name:"class transformers.AutoModel",anchor:"transformers.AutoModel",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/vr_17548/src/transformers/models/auto/modeling_auto.py#L739"}}),f6=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/pr_17548/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/pr_17548/en/model_doc/albert#transformers.AlbertConfig">AlbertConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/albert#transformers.AlbertModel">AlbertModel</a> (ALBERT model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/bart#transformers.BartConfig">BartConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/bart#transformers.BartModel">BartModel</a> (BART model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/beit#transformers.BeitConfig">BeitConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/beit#transformers.BeitModel">BeitModel</a> (BEiT model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/bert#transformers.BertConfig">BertConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/bert#transformers.BertModel">BertModel</a> (BERT model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/bert-generation#transformers.BertGenerationConfig">BertGenerationConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/bert-generation#transformers.BertGenerationEncoder">BertGenerationEncoder</a> (Bert Generation model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/big_bird#transformers.BigBirdConfig">BigBirdConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/big_bird#transformers.BigBirdModel">BigBirdModel</a> (BigBird model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/bigbird_pegasus#transformers.BigBirdPegasusConfig">BigBirdPegasusConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/bigbird_pegasus#transformers.BigBirdPegasusModel">BigBirdPegasusModel</a> (BigBird-Pegasus model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/blenderbot#transformers.BlenderbotConfig">BlenderbotConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/blenderbot#transformers.BlenderbotModel">BlenderbotModel</a> (Blenderbot model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/blenderbot-small#transformers.BlenderbotSmallConfig">BlenderbotSmallConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/blenderbot-small#transformers.BlenderbotSmallModel">BlenderbotSmallModel</a> (BlenderbotSmall model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/clip#transformers.CLIPConfig">CLIPConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/clip#transformers.CLIPModel">CLIPModel</a> (CLIP model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/ctrl#transformers.CTRLConfig">CTRLConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/ctrl#transformers.CTRLModel">CTRLModel</a> (CTRL model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/camembert#transformers.CamembertConfig">CamembertConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/camembert#transformers.CamembertModel">CamembertModel</a> (CamemBERT model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/canine#transformers.CanineConfig">CanineConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/canine#transformers.CanineModel">CanineModel</a> (CANINE model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/convbert#transformers.ConvBertConfig">ConvBertConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/convbert#transformers.ConvBertModel">ConvBertModel</a> (ConvBERT model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/convnext#transformers.ConvNextConfig">ConvNextConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/convnext#transformers.ConvNextModel">ConvNextModel</a> (ConvNeXT model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/cvt#transformers.CvtConfig">CvtConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/cvt#transformers.CvtModel">CvtModel</a> (CvT model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/dpr#transformers.DPRConfig">DPRConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/dpr#transformers.DPRQuestionEncoder">DPRQuestionEncoder</a> (DPR model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/dpt#transformers.DPTConfig">DPTConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/dpt#transformers.DPTModel">DPTModel</a> (DPT model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/data2vec#transformers.Data2VecAudioConfig">Data2VecAudioConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/data2vec#transformers.Data2VecAudioModel">Data2VecAudioModel</a> (Data2VecAudio model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/data2vec#transformers.Data2VecTextConfig">Data2VecTextConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/data2vec#transformers.Data2VecTextModel">Data2VecTextModel</a> (Data2VecText model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/data2vec#transformers.Data2VecVisionConfig">Data2VecVisionConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/data2vec#transformers.Data2VecVisionModel">Data2VecVisionModel</a> (Data2VecVision model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/deberta#transformers.DebertaConfig">DebertaConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/deberta#transformers.DebertaModel">DebertaModel</a> (DeBERTa model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/deberta-v2#transformers.DebertaV2Config">DebertaV2Config</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/deberta-v2#transformers.DebertaV2Model">DebertaV2Model</a> (DeBERTa-v2 model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/decision_transformer#transformers.DecisionTransformerConfig">DecisionTransformerConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/decision_transformer#transformers.DecisionTransformerModel">DecisionTransformerModel</a> (Decision Transformer model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/deit#transformers.DeiTConfig">DeiTConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/deit#transformers.DeiTModel">DeiTModel</a> (DeiT model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/detr#transformers.DetrConfig">DetrConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/detr#transformers.DetrModel">DetrModel</a> (DETR model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/distilbert#transformers.DistilBertConfig">DistilBertConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/distilbert#transformers.DistilBertModel">DistilBertModel</a> (DistilBERT model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/electra#transformers.ElectraConfig">ElectraConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/electra#transformers.ElectraModel">ElectraModel</a> (ELECTRA model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/fnet#transformers.FNetConfig">FNetConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/fnet#transformers.FNetModel">FNetModel</a> (FNet model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/fsmt#transformers.FSMTConfig">FSMTConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/fsmt#transformers.FSMTModel">FSMTModel</a> (FairSeq Machine-Translation model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/flaubert#transformers.FlaubertConfig">FlaubertConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/flaubert#transformers.FlaubertModel">FlaubertModel</a> (FlauBERT model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/flava#transformers.FlavaConfig">FlavaConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/flava#transformers.FlavaModel">FlavaModel</a> (FLAVA model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/funnel#transformers.FunnelConfig">FunnelConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/funnel#transformers.FunnelModel">FunnelModel</a> or <a href="/docs/transformers/pr_17548/en/model_doc/funnel#transformers.FunnelBaseModel">FunnelBaseModel</a> (Funnel Transformer model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/glpn#transformers.GLPNConfig">GLPNConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/glpn#transformers.GLPNModel">GLPNModel</a> (GLPN model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/gpt2#transformers.GPT2Config">GPT2Config</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/gpt2#transformers.GPT2Model">GPT2Model</a> (OpenAI GPT-2 model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/gptj#transformers.GPTJConfig">GPTJConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/gptj#transformers.GPTJModel">GPTJModel</a> (GPT-J model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/gpt_neo#transformers.GPTNeoConfig">GPTNeoConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/gpt_neo#transformers.GPTNeoModel">GPTNeoModel</a> (GPT Neo model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/gpt_neox#transformers.GPTNeoXConfig">GPTNeoXConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/gpt_neox#transformers.GPTNeoXModel">GPTNeoXModel</a> (GPT NeoX model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/hubert#transformers.HubertConfig">HubertConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/hubert#transformers.HubertModel">HubertModel</a> (Hubert model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/ibert#transformers.IBertConfig">IBertConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/ibert#transformers.IBertModel">IBertModel</a> (I-BERT model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/imagegpt#transformers.ImageGPTConfig">ImageGPTConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/imagegpt#transformers.ImageGPTModel">ImageGPTModel</a> (ImageGPT model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/led#transformers.LEDConfig">LEDConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/led#transformers.LEDModel">LEDModel</a> (LED model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/layoutlm#transformers.LayoutLMConfig">LayoutLMConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/layoutlm#transformers.LayoutLMModel">LayoutLMModel</a> (LayoutLM model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/layoutlmv2#transformers.LayoutLMv2Config">LayoutLMv2Config</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/layoutlmv2#transformers.LayoutLMv2Model">LayoutLMv2Model</a> (LayoutLMv2 model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/layoutlmv3#transformers.LayoutLMv3Config">LayoutLMv3Config</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/layoutlmv3#transformers.LayoutLMv3Model">LayoutLMv3Model</a> (LayoutLMv3 model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/levit#transformers.LevitConfig">LevitConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/levit#transformers.LevitModel">LevitModel</a> (LeViT model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/longformer#transformers.LongformerConfig">LongformerConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/longformer#transformers.LongformerModel">LongformerModel</a> (Longformer model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/luke#transformers.LukeConfig">LukeConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/luke#transformers.LukeModel">LukeModel</a> (LUKE model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/lxmert#transformers.LxmertConfig">LxmertConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/lxmert#transformers.LxmertModel">LxmertModel</a> (LXMERT model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/m2m_100#transformers.M2M100Config">M2M100Config</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/m2m_100#transformers.M2M100Model">M2M100Model</a> (M2M100 model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/mbart#transformers.MBartConfig">MBartConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/mbart#transformers.MBartModel">MBartModel</a> (mBART model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/mpnet#transformers.MPNetConfig">MPNetConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/mpnet#transformers.MPNetModel">MPNetModel</a> (MPNet model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/mt5#transformers.MT5Config">MT5Config</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/mt5#transformers.MT5Model">MT5Model</a> (MT5 model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/marian#transformers.MarianConfig">MarianConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/marian#transformers.MarianModel">MarianModel</a> (Marian model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/maskformer#transformers.MaskFormerConfig">MaskFormerConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/maskformer#transformers.MaskFormerModel">MaskFormerModel</a> (MaskFormer model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/megatron-bert#transformers.MegatronBertConfig">MegatronBertConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/megatron-bert#transformers.MegatronBertModel">MegatronBertModel</a> (Megatron-BERT model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/mobilebert#transformers.MobileBertConfig">MobileBertConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/mobilebert#transformers.MobileBertModel">MobileBertModel</a> (MobileBERT model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/nystromformer#transformers.NystromformerConfig">NystromformerConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/nystromformer#transformers.NystromformerModel">NystromformerModel</a> (Nystr&#xF6;mformer model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/opt#transformers.OPTConfig">OPTConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/opt#transformers.OPTModel">OPTModel</a> (OPT model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/openai-gpt#transformers.OpenAIGPTConfig">OpenAIGPTConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/openai-gpt#transformers.OpenAIGPTModel">OpenAIGPTModel</a> (OpenAI GPT model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/plbart#transformers.PLBartConfig">PLBartConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/plbart#transformers.PLBartModel">PLBartModel</a> (PLBart model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/pegasus#transformers.PegasusConfig">PegasusConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/pegasus#transformers.PegasusModel">PegasusModel</a> (Pegasus model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/perceiver#transformers.PerceiverConfig">PerceiverConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/perceiver#transformers.PerceiverModel">PerceiverModel</a> (Perceiver model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/poolformer#transformers.PoolFormerConfig">PoolFormerConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/poolformer#transformers.PoolFormerModel">PoolFormerModel</a> (PoolFormer model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/prophetnet#transformers.ProphetNetConfig">ProphetNetConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/prophetnet#transformers.ProphetNetModel">ProphetNetModel</a> (ProphetNet model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/qdqbert#transformers.QDQBertConfig">QDQBertConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/qdqbert#transformers.QDQBertModel">QDQBertModel</a> (QDQBert model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/reformer#transformers.ReformerConfig">ReformerConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/reformer#transformers.ReformerModel">ReformerModel</a> (Reformer model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/regnet#transformers.RegNetConfig">RegNetConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/regnet#transformers.RegNetModel">RegNetModel</a> (RegNet model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/rembert#transformers.RemBertConfig">RemBertConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/rembert#transformers.RemBertModel">RemBertModel</a> (RemBERT model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/resnet#transformers.ResNetConfig">ResNetConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/resnet#transformers.ResNetModel">ResNetModel</a> (ResNet model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/retribert#transformers.RetriBertConfig">RetriBertConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/retribert#transformers.RetriBertModel">RetriBertModel</a> (RetriBERT model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/roformer#transformers.RoFormerConfig">RoFormerConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/roformer#transformers.RoFormerModel">RoFormerModel</a> (RoFormer model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/roberta#transformers.RobertaConfig">RobertaConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/roberta#transformers.RobertaModel">RobertaModel</a> (RoBERTa model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/sew#transformers.SEWConfig">SEWConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/sew#transformers.SEWModel">SEWModel</a> (SEW model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/sew-d#transformers.SEWDConfig">SEWDConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/sew-d#transformers.SEWDModel">SEWDModel</a> (SEW-D model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/segformer#transformers.SegformerConfig">SegformerConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/segformer#transformers.SegformerModel">SegformerModel</a> (SegFormer model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/speech_to_text#transformers.Speech2TextConfig">Speech2TextConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/speech_to_text#transformers.Speech2TextModel">Speech2TextModel</a> (Speech2Text model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/splinter#transformers.SplinterConfig">SplinterConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/splinter#transformers.SplinterModel">SplinterModel</a> (Splinter model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/squeezebert#transformers.SqueezeBertConfig">SqueezeBertConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/squeezebert#transformers.SqueezeBertModel">SqueezeBertModel</a> (SqueezeBERT model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/swin#transformers.SwinConfig">SwinConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/swin#transformers.SwinModel">SwinModel</a> (Swin Transformer model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/t5#transformers.T5Config">T5Config</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/t5#transformers.T5Model">T5Model</a> (T5 model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/tapas#transformers.TapasConfig">TapasConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/tapas#transformers.TapasModel">TapasModel</a> (TAPAS model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/trajectory_transformer#transformers.TrajectoryTransformerConfig">TrajectoryTransformerConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/trajectory_transformer#transformers.TrajectoryTransformerModel">TrajectoryTransformerModel</a> (Trajectory Transformer model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/transfo-xl#transformers.TransfoXLConfig">TransfoXLConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/transfo-xl#transformers.TransfoXLModel">TransfoXLModel</a> (Transformer-XL model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/unispeech#transformers.UniSpeechConfig">UniSpeechConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/unispeech#transformers.UniSpeechModel">UniSpeechModel</a> (UniSpeech model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/unispeech-sat#transformers.UniSpeechSatConfig">UniSpeechSatConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/unispeech-sat#transformers.UniSpeechSatModel">UniSpeechSatModel</a> (UniSpeechSat model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/van#transformers.VanConfig">VanConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/van#transformers.VanModel">VanModel</a> (VAN model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/vit#transformers.ViTConfig">ViTConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/vit#transformers.ViTModel">ViTModel</a> (ViT model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/vit_mae#transformers.ViTMAEConfig">ViTMAEConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/vit_mae#transformers.ViTMAEModel">ViTMAEModel</a> (ViTMAE model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/vilt#transformers.ViltConfig">ViltConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/vilt#transformers.ViltModel">ViltModel</a> (ViLT model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/vision-text-dual-encoder#transformers.VisionTextDualEncoderConfig">VisionTextDualEncoderConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/vision-text-dual-encoder#transformers.VisionTextDualEncoderModel">VisionTextDualEncoderModel</a> (VisionTextDualEncoder model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/visual_bert#transformers.VisualBertConfig">VisualBertConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/visual_bert#transformers.VisualBertModel">VisualBertModel</a> (VisualBERT model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/wav2vec2#transformers.Wav2Vec2Config">Wav2Vec2Config</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/wav2vec2#transformers.Wav2Vec2Model">Wav2Vec2Model</a> (Wav2Vec2 model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/wav2vec2-conformer#transformers.Wav2Vec2ConformerConfig">Wav2Vec2ConformerConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/wav2vec2-conformer#transformers.Wav2Vec2ConformerModel">Wav2Vec2ConformerModel</a> (Wav2Vec2-Conformer model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/wavlm#transformers.WavLMConfig">WavLMConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/wavlm#transformers.WavLMModel">WavLMModel</a> (WavLM model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/xglm#transformers.XGLMConfig">XGLMConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/xglm#transformers.XGLMModel">XGLMModel</a> (XGLM model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/xlm#transformers.XLMConfig">XLMConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/xlm#transformers.XLMModel">XLMModel</a> (XLM model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/xlm-prophetnet#transformers.XLMProphetNetConfig">XLMProphetNetConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/xlm-prophetnet#transformers.XLMProphetNetModel">XLMProphetNetModel</a> (XLM-ProphetNet model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/xlm-roberta#transformers.XLMRobertaConfig">XLMRobertaConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/xlm-roberta#transformers.XLMRobertaModel">XLMRobertaModel</a> (XLM-RoBERTa model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/xlm-roberta-xl#transformers.XLMRobertaXLConfig">XLMRobertaXLConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/xlm-roberta-xl#transformers.XLMRobertaXLModel">XLMRobertaXLModel</a> (XLM-RoBERTa-XL model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/xlnet#transformers.XLNetConfig">XLNetConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/xlnet#transformers.XLNetModel">XLNetModel</a> (XLNet model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/yolos#transformers.YolosConfig">YolosConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/yolos#transformers.YolosModel">YolosModel</a> (YOLOS model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/yoso#transformers.YosoConfig">YosoConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/yoso#transformers.YosoModel">YosoModel</a> (YOSO model)</li>
</ul>`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/vr_17548/src/transformers/models/auto/auto_factory.py#L389"}}),cp=new B({props:{anchor:"transformers.AutoModel.from_config.example",$$slots:{default:[gSt]},$$scope:{ctx:L}}}),m6=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/pr_17548/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/pr_17548/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/pr_17548/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/pr_17548/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and
<a href="/docs/transformers/pr_17548/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/pr_17548/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/vr_17548/src/transformers/models/auto/auto_factory.py#L417"}}),su=new B({props:{anchor:"transformers.AutoModel.from_pretrained.example",$$slots:{default:[hSt]},$$scope:{ctx:L}}}),g6=new te({}),h6=new R({props:{name:"class transformers.AutoModelForPreTraining",anchor:"transformers.AutoModelForPreTraining",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/vr_17548/src/transformers/models/auto/modeling_auto.py#L746"}}),_6=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/pr_17548/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/pr_17548/en/model_doc/albert#transformers.AlbertConfig">AlbertConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/albert#transformers.AlbertForPreTraining">AlbertForPreTraining</a> (ALBERT model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/bart#transformers.BartConfig">BartConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/bart#transformers.BartForConditionalGeneration">BartForConditionalGeneration</a> (BART model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/bert#transformers.BertConfig">BertConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/bert#transformers.BertForPreTraining">BertForPreTraining</a> (BERT model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/big_bird#transformers.BigBirdConfig">BigBirdConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/big_bird#transformers.BigBirdForPreTraining">BigBirdForPreTraining</a> (BigBird model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/ctrl#transformers.CTRLConfig">CTRLConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/ctrl#transformers.CTRLLMHeadModel">CTRLLMHeadModel</a> (CTRL model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/camembert#transformers.CamembertConfig">CamembertConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/camembert#transformers.CamembertForMaskedLM">CamembertForMaskedLM</a> (CamemBERT model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/data2vec#transformers.Data2VecTextConfig">Data2VecTextConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/data2vec#transformers.Data2VecTextForMaskedLM">Data2VecTextForMaskedLM</a> (Data2VecText model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/deberta#transformers.DebertaConfig">DebertaConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/deberta#transformers.DebertaForMaskedLM">DebertaForMaskedLM</a> (DeBERTa model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/deberta-v2#transformers.DebertaV2Config">DebertaV2Config</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/deberta-v2#transformers.DebertaV2ForMaskedLM">DebertaV2ForMaskedLM</a> (DeBERTa-v2 model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/distilbert#transformers.DistilBertConfig">DistilBertConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/distilbert#transformers.DistilBertForMaskedLM">DistilBertForMaskedLM</a> (DistilBERT model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/electra#transformers.ElectraConfig">ElectraConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/electra#transformers.ElectraForPreTraining">ElectraForPreTraining</a> (ELECTRA model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/fnet#transformers.FNetConfig">FNetConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/fnet#transformers.FNetForPreTraining">FNetForPreTraining</a> (FNet model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/fsmt#transformers.FSMTConfig">FSMTConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/fsmt#transformers.FSMTForConditionalGeneration">FSMTForConditionalGeneration</a> (FairSeq Machine-Translation model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/flaubert#transformers.FlaubertConfig">FlaubertConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/flaubert#transformers.FlaubertWithLMHeadModel">FlaubertWithLMHeadModel</a> (FlauBERT model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/flava#transformers.FlavaConfig">FlavaConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/flava#transformers.FlavaForPreTraining">FlavaForPreTraining</a> (FLAVA model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/funnel#transformers.FunnelConfig">FunnelConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/funnel#transformers.FunnelForPreTraining">FunnelForPreTraining</a> (Funnel Transformer model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/gpt2#transformers.GPT2Config">GPT2Config</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/gpt2#transformers.GPT2LMHeadModel">GPT2LMHeadModel</a> (OpenAI GPT-2 model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/ibert#transformers.IBertConfig">IBertConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/ibert#transformers.IBertForMaskedLM">IBertForMaskedLM</a> (I-BERT model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/layoutlm#transformers.LayoutLMConfig">LayoutLMConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/layoutlm#transformers.LayoutLMForMaskedLM">LayoutLMForMaskedLM</a> (LayoutLM model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/longformer#transformers.LongformerConfig">LongformerConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/longformer#transformers.LongformerForMaskedLM">LongformerForMaskedLM</a> (Longformer model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/lxmert#transformers.LxmertConfig">LxmertConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/lxmert#transformers.LxmertForPreTraining">LxmertForPreTraining</a> (LXMERT model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/mpnet#transformers.MPNetConfig">MPNetConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/mpnet#transformers.MPNetForMaskedLM">MPNetForMaskedLM</a> (MPNet model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/megatron-bert#transformers.MegatronBertConfig">MegatronBertConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/megatron-bert#transformers.MegatronBertForPreTraining">MegatronBertForPreTraining</a> (Megatron-BERT model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/mobilebert#transformers.MobileBertConfig">MobileBertConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/mobilebert#transformers.MobileBertForPreTraining">MobileBertForPreTraining</a> (MobileBERT model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/openai-gpt#transformers.OpenAIGPTConfig">OpenAIGPTConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/openai-gpt#transformers.OpenAIGPTLMHeadModel">OpenAIGPTLMHeadModel</a> (OpenAI GPT model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/retribert#transformers.RetriBertConfig">RetriBertConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/retribert#transformers.RetriBertModel">RetriBertModel</a> (RetriBERT model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/roberta#transformers.RobertaConfig">RobertaConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/roberta#transformers.RobertaForMaskedLM">RobertaForMaskedLM</a> (RoBERTa model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/splinter#transformers.SplinterConfig">SplinterConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/splinter#transformers.SplinterForPreTraining">SplinterForPreTraining</a> (Splinter model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/squeezebert#transformers.SqueezeBertConfig">SqueezeBertConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/squeezebert#transformers.SqueezeBertForMaskedLM">SqueezeBertForMaskedLM</a> (SqueezeBERT model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/t5#transformers.T5Config">T5Config</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/t5#transformers.T5ForConditionalGeneration">T5ForConditionalGeneration</a> (T5 model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/tapas#transformers.TapasConfig">TapasConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/tapas#transformers.TapasForMaskedLM">TapasForMaskedLM</a> (TAPAS model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/transfo-xl#transformers.TransfoXLConfig">TransfoXLConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/transfo-xl#transformers.TransfoXLLMHeadModel">TransfoXLLMHeadModel</a> (Transformer-XL model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/unispeech#transformers.UniSpeechConfig">UniSpeechConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/unispeech#transformers.UniSpeechForPreTraining">UniSpeechForPreTraining</a> (UniSpeech model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/unispeech-sat#transformers.UniSpeechSatConfig">UniSpeechSatConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/unispeech-sat#transformers.UniSpeechSatForPreTraining">UniSpeechSatForPreTraining</a> (UniSpeechSat model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/vit_mae#transformers.ViTMAEConfig">ViTMAEConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/vit_mae#transformers.ViTMAEForPreTraining">ViTMAEForPreTraining</a> (ViTMAE model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/visual_bert#transformers.VisualBertConfig">VisualBertConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/visual_bert#transformers.VisualBertForPreTraining">VisualBertForPreTraining</a> (VisualBERT model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/wav2vec2#transformers.Wav2Vec2Config">Wav2Vec2Config</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/wav2vec2#transformers.Wav2Vec2ForPreTraining">Wav2Vec2ForPreTraining</a> (Wav2Vec2 model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/wav2vec2-conformer#transformers.Wav2Vec2ConformerConfig">Wav2Vec2ConformerConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/wav2vec2-conformer#transformers.Wav2Vec2ConformerForPreTraining">Wav2Vec2ConformerForPreTraining</a> (Wav2Vec2-Conformer model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/xlm#transformers.XLMConfig">XLMConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/xlm#transformers.XLMWithLMHeadModel">XLMWithLMHeadModel</a> (XLM model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/xlm-roberta#transformers.XLMRobertaConfig">XLMRobertaConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/xlm-roberta#transformers.XLMRobertaForMaskedLM">XLMRobertaForMaskedLM</a> (XLM-RoBERTa model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/xlm-roberta-xl#transformers.XLMRobertaXLConfig">XLMRobertaXLConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/xlm-roberta-xl#transformers.XLMRobertaXLForMaskedLM">XLMRobertaXLForMaskedLM</a> (XLM-RoBERTa-XL model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/xlnet#transformers.XLNetConfig">XLNetConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/xlnet#transformers.XLNetLMHeadModel">XLNetLMHeadModel</a> (XLNet model)</li>
</ul>`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/vr_17548/src/transformers/models/auto/auto_factory.py#L389"}}),iu=new B({props:{anchor:"transformers.AutoModelForPreTraining.from_config.example",$$slots:{default:[pSt]},$$scope:{ctx:L}}}),u6=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/pr_17548/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/pr_17548/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/pr_17548/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/pr_17548/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and
<a href="/docs/transformers/pr_17548/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/pr_17548/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/vr_17548/src/transformers/models/auto/auto_factory.py#L417"}}),Ku=new B({props:{anchor:"transformers.AutoModelForPreTraining.from_pretrained.example",$$slots:{default:[_St]},$$scope:{ctx:L}}}),b6=new te({}),v6=new R({props:{name:"class transformers.AutoModelForCausalLM",anchor:"transformers.AutoModelForCausalLM",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/vr_17548/src/transformers/models/auto/modeling_auto.py#L761"}}),T6=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/pr_17548/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/pr_17548/en/model_doc/bart#transformers.BartConfig">BartConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/bart#transformers.BartForCausalLM">BartForCausalLM</a> (BART model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/bert#transformers.BertConfig">BertConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/bert#transformers.BertLMHeadModel">BertLMHeadModel</a> (BERT model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/bert-generation#transformers.BertGenerationConfig">BertGenerationConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/bert-generation#transformers.BertGenerationDecoder">BertGenerationDecoder</a> (Bert Generation model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/big_bird#transformers.BigBirdConfig">BigBirdConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/big_bird#transformers.BigBirdForCausalLM">BigBirdForCausalLM</a> (BigBird model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/bigbird_pegasus#transformers.BigBirdPegasusConfig">BigBirdPegasusConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/bigbird_pegasus#transformers.BigBirdPegasusForCausalLM">BigBirdPegasusForCausalLM</a> (BigBird-Pegasus model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/blenderbot#transformers.BlenderbotConfig">BlenderbotConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/blenderbot#transformers.BlenderbotForCausalLM">BlenderbotForCausalLM</a> (Blenderbot model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/blenderbot-small#transformers.BlenderbotSmallConfig">BlenderbotSmallConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/blenderbot-small#transformers.BlenderbotSmallForCausalLM">BlenderbotSmallForCausalLM</a> (BlenderbotSmall model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/ctrl#transformers.CTRLConfig">CTRLConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/ctrl#transformers.CTRLLMHeadModel">CTRLLMHeadModel</a> (CTRL model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/camembert#transformers.CamembertConfig">CamembertConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/camembert#transformers.CamembertForCausalLM">CamembertForCausalLM</a> (CamemBERT model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/data2vec#transformers.Data2VecTextConfig">Data2VecTextConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/data2vec#transformers.Data2VecTextForCausalLM">Data2VecTextForCausalLM</a> (Data2VecText model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/electra#transformers.ElectraConfig">ElectraConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/electra#transformers.ElectraForCausalLM">ElectraForCausalLM</a> (ELECTRA model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/gpt2#transformers.GPT2Config">GPT2Config</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/gpt2#transformers.GPT2LMHeadModel">GPT2LMHeadModel</a> (OpenAI GPT-2 model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/gptj#transformers.GPTJConfig">GPTJConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/gptj#transformers.GPTJForCausalLM">GPTJForCausalLM</a> (GPT-J model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/gpt_neo#transformers.GPTNeoConfig">GPTNeoConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/gpt_neo#transformers.GPTNeoForCausalLM">GPTNeoForCausalLM</a> (GPT Neo model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/gpt_neox#transformers.GPTNeoXConfig">GPTNeoXConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/gpt_neox#transformers.GPTNeoXForCausalLM">GPTNeoXForCausalLM</a> (GPT NeoX model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/mbart#transformers.MBartConfig">MBartConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/mbart#transformers.MBartForCausalLM">MBartForCausalLM</a> (mBART model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/marian#transformers.MarianConfig">MarianConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/marian#transformers.MarianForCausalLM">MarianForCausalLM</a> (Marian model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/megatron-bert#transformers.MegatronBertConfig">MegatronBertConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/megatron-bert#transformers.MegatronBertForCausalLM">MegatronBertForCausalLM</a> (Megatron-BERT model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/opt#transformers.OPTConfig">OPTConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/opt#transformers.OPTForCausalLM">OPTForCausalLM</a> (OPT model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/openai-gpt#transformers.OpenAIGPTConfig">OpenAIGPTConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/openai-gpt#transformers.OpenAIGPTLMHeadModel">OpenAIGPTLMHeadModel</a> (OpenAI GPT model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/plbart#transformers.PLBartConfig">PLBartConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/plbart#transformers.PLBartForCausalLM">PLBartForCausalLM</a> (PLBart model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/pegasus#transformers.PegasusConfig">PegasusConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/pegasus#transformers.PegasusForCausalLM">PegasusForCausalLM</a> (Pegasus model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/prophetnet#transformers.ProphetNetConfig">ProphetNetConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/prophetnet#transformers.ProphetNetForCausalLM">ProphetNetForCausalLM</a> (ProphetNet model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/qdqbert#transformers.QDQBertConfig">QDQBertConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/qdqbert#transformers.QDQBertLMHeadModel">QDQBertLMHeadModel</a> (QDQBert model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/reformer#transformers.ReformerConfig">ReformerConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/reformer#transformers.ReformerModelWithLMHead">ReformerModelWithLMHead</a> (Reformer model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/rembert#transformers.RemBertConfig">RemBertConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/rembert#transformers.RemBertForCausalLM">RemBertForCausalLM</a> (RemBERT model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/roformer#transformers.RoFormerConfig">RoFormerConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/roformer#transformers.RoFormerForCausalLM">RoFormerForCausalLM</a> (RoFormer model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/roberta#transformers.RobertaConfig">RobertaConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/roberta#transformers.RobertaForCausalLM">RobertaForCausalLM</a> (RoBERTa model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/speech_to_text_2#transformers.Speech2Text2Config">Speech2Text2Config</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/speech_to_text_2#transformers.Speech2Text2ForCausalLM">Speech2Text2ForCausalLM</a> (Speech2Text2 model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/trocr#transformers.TrOCRConfig">TrOCRConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/trocr#transformers.TrOCRForCausalLM">TrOCRForCausalLM</a> (TrOCR model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/transfo-xl#transformers.TransfoXLConfig">TransfoXLConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/transfo-xl#transformers.TransfoXLLMHeadModel">TransfoXLLMHeadModel</a> (Transformer-XL model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/xglm#transformers.XGLMConfig">XGLMConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/xglm#transformers.XGLMForCausalLM">XGLMForCausalLM</a> (XGLM model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/xlm#transformers.XLMConfig">XLMConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/xlm#transformers.XLMWithLMHeadModel">XLMWithLMHeadModel</a> (XLM model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/xlm-prophetnet#transformers.XLMProphetNetConfig">XLMProphetNetConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/xlm-prophetnet#transformers.XLMProphetNetForCausalLM">XLMProphetNetForCausalLM</a> (XLM-ProphetNet model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/xlm-roberta#transformers.XLMRobertaConfig">XLMRobertaConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/xlm-roberta#transformers.XLMRobertaForCausalLM">XLMRobertaForCausalLM</a> (XLM-RoBERTa model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/xlm-roberta-xl#transformers.XLMRobertaXLConfig">XLMRobertaXLConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/xlm-roberta-xl#transformers.XLMRobertaXLForCausalLM">XLMRobertaXLForCausalLM</a> (XLM-RoBERTa-XL model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/xlnet#transformers.XLNetConfig">XLNetConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/xlnet#transformers.XLNetLMHeadModel">XLNetLMHeadModel</a> (XLNet model)</li>
</ul>`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/vr_17548/src/transformers/models/auto/auto_factory.py#L389"}}),e1=new B({props:{anchor:"transformers.AutoModelForCausalLM.from_config.example",$$slots:{default:[uSt]},$$scope:{ctx:L}}}),M6=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/pr_17548/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/pr_17548/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/pr_17548/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/pr_17548/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and
<a href="/docs/transformers/pr_17548/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/pr_17548/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/vr_17548/src/transformers/models/auto/auto_factory.py#L417"}}),j1=new B({props:{anchor:"transformers.AutoModelForCausalLM.from_pretrained.example",$$slots:{default:[bSt]},$$scope:{ctx:L}}}),E6=new te({}),C6=new R({props:{name:"class transformers.AutoModelForMaskedLM",anchor:"transformers.AutoModelForMaskedLM",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/vr_17548/src/transformers/models/auto/modeling_auto.py#L768"}}),A6=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/pr_17548/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/pr_17548/en/model_doc/albert#transformers.AlbertConfig">AlbertConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/albert#transformers.AlbertForMaskedLM">AlbertForMaskedLM</a> (ALBERT model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/bart#transformers.BartConfig">BartConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/bart#transformers.BartForConditionalGeneration">BartForConditionalGeneration</a> (BART model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/bert#transformers.BertConfig">BertConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/bert#transformers.BertForMaskedLM">BertForMaskedLM</a> (BERT model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/big_bird#transformers.BigBirdConfig">BigBirdConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/big_bird#transformers.BigBirdForMaskedLM">BigBirdForMaskedLM</a> (BigBird model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/camembert#transformers.CamembertConfig">CamembertConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/camembert#transformers.CamembertForMaskedLM">CamembertForMaskedLM</a> (CamemBERT model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/convbert#transformers.ConvBertConfig">ConvBertConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/convbert#transformers.ConvBertForMaskedLM">ConvBertForMaskedLM</a> (ConvBERT model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/data2vec#transformers.Data2VecTextConfig">Data2VecTextConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/data2vec#transformers.Data2VecTextForMaskedLM">Data2VecTextForMaskedLM</a> (Data2VecText model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/deberta#transformers.DebertaConfig">DebertaConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/deberta#transformers.DebertaForMaskedLM">DebertaForMaskedLM</a> (DeBERTa model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/deberta-v2#transformers.DebertaV2Config">DebertaV2Config</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/deberta-v2#transformers.DebertaV2ForMaskedLM">DebertaV2ForMaskedLM</a> (DeBERTa-v2 model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/distilbert#transformers.DistilBertConfig">DistilBertConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/distilbert#transformers.DistilBertForMaskedLM">DistilBertForMaskedLM</a> (DistilBERT model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/electra#transformers.ElectraConfig">ElectraConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/electra#transformers.ElectraForMaskedLM">ElectraForMaskedLM</a> (ELECTRA model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/fnet#transformers.FNetConfig">FNetConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/fnet#transformers.FNetForMaskedLM">FNetForMaskedLM</a> (FNet model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/flaubert#transformers.FlaubertConfig">FlaubertConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/flaubert#transformers.FlaubertWithLMHeadModel">FlaubertWithLMHeadModel</a> (FlauBERT model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/funnel#transformers.FunnelConfig">FunnelConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/funnel#transformers.FunnelForMaskedLM">FunnelForMaskedLM</a> (Funnel Transformer model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/ibert#transformers.IBertConfig">IBertConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/ibert#transformers.IBertForMaskedLM">IBertForMaskedLM</a> (I-BERT model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/layoutlm#transformers.LayoutLMConfig">LayoutLMConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/layoutlm#transformers.LayoutLMForMaskedLM">LayoutLMForMaskedLM</a> (LayoutLM model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/longformer#transformers.LongformerConfig">LongformerConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/longformer#transformers.LongformerForMaskedLM">LongformerForMaskedLM</a> (Longformer model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/luke#transformers.LukeConfig">LukeConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/luke#transformers.LukeForMaskedLM">LukeForMaskedLM</a> (LUKE model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/mbart#transformers.MBartConfig">MBartConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/mbart#transformers.MBartForConditionalGeneration">MBartForConditionalGeneration</a> (mBART model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/mpnet#transformers.MPNetConfig">MPNetConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/mpnet#transformers.MPNetForMaskedLM">MPNetForMaskedLM</a> (MPNet model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/megatron-bert#transformers.MegatronBertConfig">MegatronBertConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/megatron-bert#transformers.MegatronBertForMaskedLM">MegatronBertForMaskedLM</a> (Megatron-BERT model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/mobilebert#transformers.MobileBertConfig">MobileBertConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/mobilebert#transformers.MobileBertForMaskedLM">MobileBertForMaskedLM</a> (MobileBERT model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/nystromformer#transformers.NystromformerConfig">NystromformerConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/nystromformer#transformers.NystromformerForMaskedLM">NystromformerForMaskedLM</a> (Nystr&#xF6;mformer model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/perceiver#transformers.PerceiverConfig">PerceiverConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/perceiver#transformers.PerceiverForMaskedLM">PerceiverForMaskedLM</a> (Perceiver model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/qdqbert#transformers.QDQBertConfig">QDQBertConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/qdqbert#transformers.QDQBertForMaskedLM">QDQBertForMaskedLM</a> (QDQBert model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/reformer#transformers.ReformerConfig">ReformerConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/reformer#transformers.ReformerForMaskedLM">ReformerForMaskedLM</a> (Reformer model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/rembert#transformers.RemBertConfig">RemBertConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/rembert#transformers.RemBertForMaskedLM">RemBertForMaskedLM</a> (RemBERT model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/roformer#transformers.RoFormerConfig">RoFormerConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/roformer#transformers.RoFormerForMaskedLM">RoFormerForMaskedLM</a> (RoFormer model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/roberta#transformers.RobertaConfig">RobertaConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/roberta#transformers.RobertaForMaskedLM">RobertaForMaskedLM</a> (RoBERTa model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/squeezebert#transformers.SqueezeBertConfig">SqueezeBertConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/squeezebert#transformers.SqueezeBertForMaskedLM">SqueezeBertForMaskedLM</a> (SqueezeBERT model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/tapas#transformers.TapasConfig">TapasConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/tapas#transformers.TapasForMaskedLM">TapasForMaskedLM</a> (TAPAS model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/wav2vec2#transformers.Wav2Vec2Config">Wav2Vec2Config</a> configuration class: <code>Wav2Vec2ForMaskedLM</code> (Wav2Vec2 model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/xlm#transformers.XLMConfig">XLMConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/xlm#transformers.XLMWithLMHeadModel">XLMWithLMHeadModel</a> (XLM model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/xlm-roberta#transformers.XLMRobertaConfig">XLMRobertaConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/xlm-roberta#transformers.XLMRobertaForMaskedLM">XLMRobertaForMaskedLM</a> (XLM-RoBERTa model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/xlm-roberta-xl#transformers.XLMRobertaXLConfig">XLMRobertaXLConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/xlm-roberta-xl#transformers.XLMRobertaXLForMaskedLM">XLMRobertaXLForMaskedLM</a> (XLM-RoBERTa-XL model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/yoso#transformers.YosoConfig">YosoConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/yoso#transformers.YosoForMaskedLM">YosoForMaskedLM</a> (YOSO model)</li>
</ul>`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/vr_17548/src/transformers/models/auto/auto_factory.py#L389"}}),G1=new B({props:{anchor:"transformers.AutoModelForMaskedLM.from_config.example",$$slots:{default:[vSt]},$$scope:{ctx:L}}}),y6=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/pr_17548/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/pr_17548/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/pr_17548/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/pr_17548/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and
<a href="/docs/transformers/pr_17548/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/pr_17548/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/vr_17548/src/transformers/models/auto/auto_factory.py#L417"}}),wb=new B({props:{anchor:"transformers.AutoModelForMaskedLM.from_pretrained.example",$$slots:{default:[FSt]},$$scope:{ctx:L}}}),L6=new te({}),x6=new R({props:{name:"class transformers.AutoModelForSeq2SeqLM",anchor:"transformers.AutoModelForSeq2SeqLM",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/vr_17548/src/transformers/models/auto/modeling_auto.py#L775"}}),k6=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/pr_17548/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/pr_17548/en/model_doc/bart#transformers.BartConfig">BartConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/bart#transformers.BartForConditionalGeneration">BartForConditionalGeneration</a> (BART model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/bigbird_pegasus#transformers.BigBirdPegasusConfig">BigBirdPegasusConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/bigbird_pegasus#transformers.BigBirdPegasusForConditionalGeneration">BigBirdPegasusForConditionalGeneration</a> (BigBird-Pegasus model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/blenderbot#transformers.BlenderbotConfig">BlenderbotConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/blenderbot#transformers.BlenderbotForConditionalGeneration">BlenderbotForConditionalGeneration</a> (Blenderbot model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/blenderbot-small#transformers.BlenderbotSmallConfig">BlenderbotSmallConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/blenderbot-small#transformers.BlenderbotSmallForConditionalGeneration">BlenderbotSmallForConditionalGeneration</a> (BlenderbotSmall model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/encoder-decoder#transformers.EncoderDecoderConfig">EncoderDecoderConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/encoder-decoder#transformers.EncoderDecoderModel">EncoderDecoderModel</a> (Encoder decoder model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/fsmt#transformers.FSMTConfig">FSMTConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/fsmt#transformers.FSMTForConditionalGeneration">FSMTForConditionalGeneration</a> (FairSeq Machine-Translation model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/led#transformers.LEDConfig">LEDConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/led#transformers.LEDForConditionalGeneration">LEDForConditionalGeneration</a> (LED model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/m2m_100#transformers.M2M100Config">M2M100Config</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/m2m_100#transformers.M2M100ForConditionalGeneration">M2M100ForConditionalGeneration</a> (M2M100 model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/mbart#transformers.MBartConfig">MBartConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/mbart#transformers.MBartForConditionalGeneration">MBartForConditionalGeneration</a> (mBART model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/mt5#transformers.MT5Config">MT5Config</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/mt5#transformers.MT5ForConditionalGeneration">MT5ForConditionalGeneration</a> (MT5 model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/marian#transformers.MarianConfig">MarianConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/marian#transformers.MarianMTModel">MarianMTModel</a> (Marian model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/plbart#transformers.PLBartConfig">PLBartConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/plbart#transformers.PLBartForConditionalGeneration">PLBartForConditionalGeneration</a> (PLBart model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/pegasus#transformers.PegasusConfig">PegasusConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/pegasus#transformers.PegasusForConditionalGeneration">PegasusForConditionalGeneration</a> (Pegasus model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/prophetnet#transformers.ProphetNetConfig">ProphetNetConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/prophetnet#transformers.ProphetNetForConditionalGeneration">ProphetNetForConditionalGeneration</a> (ProphetNet model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/t5#transformers.T5Config">T5Config</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/t5#transformers.T5ForConditionalGeneration">T5ForConditionalGeneration</a> (T5 model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/xlm-prophetnet#transformers.XLMProphetNetConfig">XLMProphetNetConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/xlm-prophetnet#transformers.XLMProphetNetForConditionalGeneration">XLMProphetNetForConditionalGeneration</a> (XLM-ProphetNet model)</li>
</ul>`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/vr_17548/src/transformers/models/auto/auto_factory.py#L389"}}),yb=new B({props:{anchor:"transformers.AutoModelForSeq2SeqLM.from_config.example",$$slots:{default:[TSt]},$$scope:{ctx:L}}}),S6=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/pr_17548/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/pr_17548/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/pr_17548/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/pr_17548/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and
<a href="/docs/transformers/pr_17548/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/pr_17548/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/vr_17548/src/transformers/models/auto/auto_factory.py#L417"}}),zb=new B({props:{anchor:"transformers.AutoModelForSeq2SeqLM.from_pretrained.example",$$slots:{default:[MSt]},$$scope:{ctx:L}}}),R6=new te({}),P6=new R({props:{name:"class transformers.AutoModelForSequenceClassification",anchor:"transformers.AutoModelForSequenceClassification",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/vr_17548/src/transformers/models/auto/modeling_auto.py#L784"}}),I6=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/pr_17548/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/pr_17548/en/model_doc/albert#transformers.AlbertConfig">AlbertConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/albert#transformers.AlbertForSequenceClassification">AlbertForSequenceClassification</a> (ALBERT model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/bart#transformers.BartConfig">BartConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/bart#transformers.BartForSequenceClassification">BartForSequenceClassification</a> (BART model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/bert#transformers.BertConfig">BertConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/bert#transformers.BertForSequenceClassification">BertForSequenceClassification</a> (BERT model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/big_bird#transformers.BigBirdConfig">BigBirdConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/big_bird#transformers.BigBirdForSequenceClassification">BigBirdForSequenceClassification</a> (BigBird model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/bigbird_pegasus#transformers.BigBirdPegasusConfig">BigBirdPegasusConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/bigbird_pegasus#transformers.BigBirdPegasusForSequenceClassification">BigBirdPegasusForSequenceClassification</a> (BigBird-Pegasus model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/ctrl#transformers.CTRLConfig">CTRLConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/ctrl#transformers.CTRLForSequenceClassification">CTRLForSequenceClassification</a> (CTRL model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/camembert#transformers.CamembertConfig">CamembertConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/camembert#transformers.CamembertForSequenceClassification">CamembertForSequenceClassification</a> (CamemBERT model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/canine#transformers.CanineConfig">CanineConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/canine#transformers.CanineForSequenceClassification">CanineForSequenceClassification</a> (CANINE model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/convbert#transformers.ConvBertConfig">ConvBertConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/convbert#transformers.ConvBertForSequenceClassification">ConvBertForSequenceClassification</a> (ConvBERT model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/data2vec#transformers.Data2VecTextConfig">Data2VecTextConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/data2vec#transformers.Data2VecTextForSequenceClassification">Data2VecTextForSequenceClassification</a> (Data2VecText model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/deberta#transformers.DebertaConfig">DebertaConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/deberta#transformers.DebertaForSequenceClassification">DebertaForSequenceClassification</a> (DeBERTa model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/deberta-v2#transformers.DebertaV2Config">DebertaV2Config</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/deberta-v2#transformers.DebertaV2ForSequenceClassification">DebertaV2ForSequenceClassification</a> (DeBERTa-v2 model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/distilbert#transformers.DistilBertConfig">DistilBertConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/distilbert#transformers.DistilBertForSequenceClassification">DistilBertForSequenceClassification</a> (DistilBERT model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/electra#transformers.ElectraConfig">ElectraConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/electra#transformers.ElectraForSequenceClassification">ElectraForSequenceClassification</a> (ELECTRA model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/fnet#transformers.FNetConfig">FNetConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/fnet#transformers.FNetForSequenceClassification">FNetForSequenceClassification</a> (FNet model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/flaubert#transformers.FlaubertConfig">FlaubertConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/flaubert#transformers.FlaubertForSequenceClassification">FlaubertForSequenceClassification</a> (FlauBERT model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/funnel#transformers.FunnelConfig">FunnelConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/funnel#transformers.FunnelForSequenceClassification">FunnelForSequenceClassification</a> (Funnel Transformer model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/gpt2#transformers.GPT2Config">GPT2Config</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/gpt2#transformers.GPT2ForSequenceClassification">GPT2ForSequenceClassification</a> (OpenAI GPT-2 model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/gptj#transformers.GPTJConfig">GPTJConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/gptj#transformers.GPTJForSequenceClassification">GPTJForSequenceClassification</a> (GPT-J model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/gpt_neo#transformers.GPTNeoConfig">GPTNeoConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/gpt_neo#transformers.GPTNeoForSequenceClassification">GPTNeoForSequenceClassification</a> (GPT Neo model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/ibert#transformers.IBertConfig">IBertConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/ibert#transformers.IBertForSequenceClassification">IBertForSequenceClassification</a> (I-BERT model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/led#transformers.LEDConfig">LEDConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/led#transformers.LEDForSequenceClassification">LEDForSequenceClassification</a> (LED model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/layoutlm#transformers.LayoutLMConfig">LayoutLMConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/layoutlm#transformers.LayoutLMForSequenceClassification">LayoutLMForSequenceClassification</a> (LayoutLM model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/layoutlmv2#transformers.LayoutLMv2Config">LayoutLMv2Config</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/layoutlmv2#transformers.LayoutLMv2ForSequenceClassification">LayoutLMv2ForSequenceClassification</a> (LayoutLMv2 model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/layoutlmv3#transformers.LayoutLMv3Config">LayoutLMv3Config</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/layoutlmv3#transformers.LayoutLMv3ForSequenceClassification">LayoutLMv3ForSequenceClassification</a> (LayoutLMv3 model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/longformer#transformers.LongformerConfig">LongformerConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/longformer#transformers.LongformerForSequenceClassification">LongformerForSequenceClassification</a> (Longformer model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/mbart#transformers.MBartConfig">MBartConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/mbart#transformers.MBartForSequenceClassification">MBartForSequenceClassification</a> (mBART model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/mpnet#transformers.MPNetConfig">MPNetConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/mpnet#transformers.MPNetForSequenceClassification">MPNetForSequenceClassification</a> (MPNet model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/megatron-bert#transformers.MegatronBertConfig">MegatronBertConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/megatron-bert#transformers.MegatronBertForSequenceClassification">MegatronBertForSequenceClassification</a> (Megatron-BERT model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/mobilebert#transformers.MobileBertConfig">MobileBertConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/mobilebert#transformers.MobileBertForSequenceClassification">MobileBertForSequenceClassification</a> (MobileBERT model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/nystromformer#transformers.NystromformerConfig">NystromformerConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/nystromformer#transformers.NystromformerForSequenceClassification">NystromformerForSequenceClassification</a> (Nystr&#xF6;mformer model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/openai-gpt#transformers.OpenAIGPTConfig">OpenAIGPTConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/openai-gpt#transformers.OpenAIGPTForSequenceClassification">OpenAIGPTForSequenceClassification</a> (OpenAI GPT model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/plbart#transformers.PLBartConfig">PLBartConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/plbart#transformers.PLBartForSequenceClassification">PLBartForSequenceClassification</a> (PLBart model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/perceiver#transformers.PerceiverConfig">PerceiverConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/perceiver#transformers.PerceiverForSequenceClassification">PerceiverForSequenceClassification</a> (Perceiver model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/qdqbert#transformers.QDQBertConfig">QDQBertConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/qdqbert#transformers.QDQBertForSequenceClassification">QDQBertForSequenceClassification</a> (QDQBert model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/reformer#transformers.ReformerConfig">ReformerConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/reformer#transformers.ReformerForSequenceClassification">ReformerForSequenceClassification</a> (Reformer model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/rembert#transformers.RemBertConfig">RemBertConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/rembert#transformers.RemBertForSequenceClassification">RemBertForSequenceClassification</a> (RemBERT model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/roformer#transformers.RoFormerConfig">RoFormerConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/roformer#transformers.RoFormerForSequenceClassification">RoFormerForSequenceClassification</a> (RoFormer model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/roberta#transformers.RobertaConfig">RobertaConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/roberta#transformers.RobertaForSequenceClassification">RobertaForSequenceClassification</a> (RoBERTa model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/squeezebert#transformers.SqueezeBertConfig">SqueezeBertConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/squeezebert#transformers.SqueezeBertForSequenceClassification">SqueezeBertForSequenceClassification</a> (SqueezeBERT model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/tapas#transformers.TapasConfig">TapasConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/tapas#transformers.TapasForSequenceClassification">TapasForSequenceClassification</a> (TAPAS model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/transfo-xl#transformers.TransfoXLConfig">TransfoXLConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/transfo-xl#transformers.TransfoXLForSequenceClassification">TransfoXLForSequenceClassification</a> (Transformer-XL model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/xlm#transformers.XLMConfig">XLMConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/xlm#transformers.XLMForSequenceClassification">XLMForSequenceClassification</a> (XLM model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/xlm-roberta#transformers.XLMRobertaConfig">XLMRobertaConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/xlm-roberta#transformers.XLMRobertaForSequenceClassification">XLMRobertaForSequenceClassification</a> (XLM-RoBERTa model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/xlm-roberta-xl#transformers.XLMRobertaXLConfig">XLMRobertaXLConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/xlm-roberta-xl#transformers.XLMRobertaXLForSequenceClassification">XLMRobertaXLForSequenceClassification</a> (XLM-RoBERTa-XL model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/xlnet#transformers.XLNetConfig">XLNetConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/xlnet#transformers.XLNetForSequenceClassification">XLNetForSequenceClassification</a> (XLNet model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/yoso#transformers.YosoConfig">YosoConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/yoso#transformers.YosoForSequenceClassification">YosoForSequenceClassification</a> (YOSO model)</li>
</ul>`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/vr_17548/src/transformers/models/auto/auto_factory.py#L389"}}),Qb=new B({props:{anchor:"transformers.AutoModelForSequenceClassification.from_config.example",$$slots:{default:[ESt]},$$scope:{ctx:L}}}),N6=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/pr_17548/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/pr_17548/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/pr_17548/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/pr_17548/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and
<a href="/docs/transformers/pr_17548/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/pr_17548/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/vr_17548/src/transformers/models/auto/auto_factory.py#L417"}}),O2=new B({props:{anchor:"transformers.AutoModelForSequenceClassification.from_pretrained.example",$$slots:{default:[CSt]},$$scope:{ctx:L}}}),q6=new te({}),j6=new R({props:{name:"class transformers.AutoModelForMultipleChoice",anchor:"transformers.AutoModelForMultipleChoice",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/vr_17548/src/transformers/models/auto/modeling_auto.py#L818"}}),G6=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/pr_17548/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/pr_17548/en/model_doc/albert#transformers.AlbertConfig">AlbertConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/albert#transformers.AlbertForMultipleChoice">AlbertForMultipleChoice</a> (ALBERT model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/bert#transformers.BertConfig">BertConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/bert#transformers.BertForMultipleChoice">BertForMultipleChoice</a> (BERT model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/big_bird#transformers.BigBirdConfig">BigBirdConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/big_bird#transformers.BigBirdForMultipleChoice">BigBirdForMultipleChoice</a> (BigBird model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/camembert#transformers.CamembertConfig">CamembertConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/camembert#transformers.CamembertForMultipleChoice">CamembertForMultipleChoice</a> (CamemBERT model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/canine#transformers.CanineConfig">CanineConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/canine#transformers.CanineForMultipleChoice">CanineForMultipleChoice</a> (CANINE model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/convbert#transformers.ConvBertConfig">ConvBertConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/convbert#transformers.ConvBertForMultipleChoice">ConvBertForMultipleChoice</a> (ConvBERT model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/data2vec#transformers.Data2VecTextConfig">Data2VecTextConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/data2vec#transformers.Data2VecTextForMultipleChoice">Data2VecTextForMultipleChoice</a> (Data2VecText model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/deberta-v2#transformers.DebertaV2Config">DebertaV2Config</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/deberta-v2#transformers.DebertaV2ForMultipleChoice">DebertaV2ForMultipleChoice</a> (DeBERTa-v2 model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/distilbert#transformers.DistilBertConfig">DistilBertConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/distilbert#transformers.DistilBertForMultipleChoice">DistilBertForMultipleChoice</a> (DistilBERT model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/electra#transformers.ElectraConfig">ElectraConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/electra#transformers.ElectraForMultipleChoice">ElectraForMultipleChoice</a> (ELECTRA model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/fnet#transformers.FNetConfig">FNetConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/fnet#transformers.FNetForMultipleChoice">FNetForMultipleChoice</a> (FNet model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/flaubert#transformers.FlaubertConfig">FlaubertConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/flaubert#transformers.FlaubertForMultipleChoice">FlaubertForMultipleChoice</a> (FlauBERT model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/funnel#transformers.FunnelConfig">FunnelConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/funnel#transformers.FunnelForMultipleChoice">FunnelForMultipleChoice</a> (Funnel Transformer model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/ibert#transformers.IBertConfig">IBertConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/ibert#transformers.IBertForMultipleChoice">IBertForMultipleChoice</a> (I-BERT model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/longformer#transformers.LongformerConfig">LongformerConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/longformer#transformers.LongformerForMultipleChoice">LongformerForMultipleChoice</a> (Longformer model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/mpnet#transformers.MPNetConfig">MPNetConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/mpnet#transformers.MPNetForMultipleChoice">MPNetForMultipleChoice</a> (MPNet model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/megatron-bert#transformers.MegatronBertConfig">MegatronBertConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/megatron-bert#transformers.MegatronBertForMultipleChoice">MegatronBertForMultipleChoice</a> (Megatron-BERT model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/mobilebert#transformers.MobileBertConfig">MobileBertConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/mobilebert#transformers.MobileBertForMultipleChoice">MobileBertForMultipleChoice</a> (MobileBERT model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/nystromformer#transformers.NystromformerConfig">NystromformerConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/nystromformer#transformers.NystromformerForMultipleChoice">NystromformerForMultipleChoice</a> (Nystr&#xF6;mformer model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/qdqbert#transformers.QDQBertConfig">QDQBertConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/qdqbert#transformers.QDQBertForMultipleChoice">QDQBertForMultipleChoice</a> (QDQBert model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/rembert#transformers.RemBertConfig">RemBertConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/rembert#transformers.RemBertForMultipleChoice">RemBertForMultipleChoice</a> (RemBERT model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/roformer#transformers.RoFormerConfig">RoFormerConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/roformer#transformers.RoFormerForMultipleChoice">RoFormerForMultipleChoice</a> (RoFormer model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/roberta#transformers.RobertaConfig">RobertaConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/roberta#transformers.RobertaForMultipleChoice">RobertaForMultipleChoice</a> (RoBERTa model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/squeezebert#transformers.SqueezeBertConfig">SqueezeBertConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/squeezebert#transformers.SqueezeBertForMultipleChoice">SqueezeBertForMultipleChoice</a> (SqueezeBERT model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/xlm#transformers.XLMConfig">XLMConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/xlm#transformers.XLMForMultipleChoice">XLMForMultipleChoice</a> (XLM model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/xlm-roberta#transformers.XLMRobertaConfig">XLMRobertaConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/xlm-roberta#transformers.XLMRobertaForMultipleChoice">XLMRobertaForMultipleChoice</a> (XLM-RoBERTa model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/xlm-roberta-xl#transformers.XLMRobertaXLConfig">XLMRobertaXLConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/xlm-roberta-xl#transformers.XLMRobertaXLForMultipleChoice">XLMRobertaXLForMultipleChoice</a> (XLM-RoBERTa-XL model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/xlnet#transformers.XLNetConfig">XLNetConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/xlnet#transformers.XLNetForMultipleChoice">XLNetForMultipleChoice</a> (XLNet model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/yoso#transformers.YosoConfig">YosoConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/yoso#transformers.YosoForMultipleChoice">YosoForMultipleChoice</a> (YOSO model)</li>
</ul>`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/vr_17548/src/transformers/models/auto/auto_factory.py#L389"}}),X2=new B({props:{anchor:"transformers.AutoModelForMultipleChoice.from_config.example",$$slots:{default:[wSt]},$$scope:{ctx:L}}}),O6=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/pr_17548/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/pr_17548/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/pr_17548/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/pr_17548/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and
<a href="/docs/transformers/pr_17548/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/pr_17548/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/vr_17548/src/transformers/models/auto/auto_factory.py#L417"}}),T4=new B({props:{anchor:"transformers.AutoModelForMultipleChoice.from_pretrained.example",$$slots:{default:[ASt]},$$scope:{ctx:L}}}),V6=new te({}),X6=new R({props:{name:"class transformers.AutoModelForNextSentencePrediction",anchor:"transformers.AutoModelForNextSentencePrediction",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/vr_17548/src/transformers/models/auto/modeling_auto.py#L825"}}),W6=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/pr_17548/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/pr_17548/en/model_doc/bert#transformers.BertConfig">BertConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/bert#transformers.BertForNextSentencePrediction">BertForNextSentencePrediction</a> (BERT model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/fnet#transformers.FNetConfig">FNetConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/fnet#transformers.FNetForNextSentencePrediction">FNetForNextSentencePrediction</a> (FNet model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/megatron-bert#transformers.MegatronBertConfig">MegatronBertConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/megatron-bert#transformers.MegatronBertForNextSentencePrediction">MegatronBertForNextSentencePrediction</a> (Megatron-BERT model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/mobilebert#transformers.MobileBertConfig">MobileBertConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/mobilebert#transformers.MobileBertForNextSentencePrediction">MobileBertForNextSentencePrediction</a> (MobileBERT model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/qdqbert#transformers.QDQBertConfig">QDQBertConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/qdqbert#transformers.QDQBertForNextSentencePrediction">QDQBertForNextSentencePrediction</a> (QDQBert model)</li>
</ul>`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/vr_17548/src/transformers/models/auto/auto_factory.py#L389"}}),E4=new B({props:{anchor:"transformers.AutoModelForNextSentencePrediction.from_config.example",$$slots:{default:[ySt]},$$scope:{ctx:L}}}),Q6=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/pr_17548/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/pr_17548/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/pr_17548/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/pr_17548/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and
<a href="/docs/transformers/pr_17548/en/main_classes/model#transformers.PreTrainedModel.from_pretrained">from_pretrained()</a> is not a simpler option.`,name:"state_dict"},{anchor:"transformers.AutoModelForNextSentencePrediction.from_pretrained.cache_dir",description:`<strong>cache_dir</strong> (<code>str</code> or <code>os.PathLike</code>, <em>optional</em>) &#x2014;
Path to a directory in which a downloaded pretrained model configuration should be cached if the
standard cache should not be used.`,name:"cache_dir"},{anchor:"transformers.AutoModelForNextSentencePrediction.from_pretrained.from_tf",description:`<strong>from_tf</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Load the model weights from a TensorFlow checkpoint save file (see docstring of
<code>pretrained_model_name_or_path</code> argument).`,name:"from_tf"},{anchor:"transformers.AutoModelForNextSentencePrediction.from_pretrained.force_download",description:`<strong>force_download</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to force the (re-)download of the model weights and configuration files, overriding the
cached versions if they exist.`,name:"force_download"},{anchor:"transformers.AutoModelForNextSentencePrediction.from_pretrained.resume_download",description:`<strong>resume_download</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to delete incompletely received files. Will attempt to resume the download if such a
file exists.`,name:"resume_download"},{anchor:"transformers.AutoModelForNextSentencePrediction.from_pretrained.proxies",description:`<strong>proxies</strong> (<code>Dict[str, str]</code>, <em>optional</em>) &#x2014;
A dictionary of proxy servers to use by protocol or endpoint, e.g., <code>{&apos;http&apos;: &apos;foo.bar:3128&apos;, &apos;http://hostname&apos;: &apos;foo.bar:4012&apos;}</code>. The proxies are used on each request.`,name:"proxies"},{anchor:"transformers.AutoModelForNextSentencePrediction.from_pretrained.output_loading_info(bool,",description:`<strong>output_loading_info(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether ot not to also return a dictionary containing missing keys, unexpected keys and error messages.`,name:"output_loading_info(bool,"},{anchor:"transformers.AutoModelForNextSentencePrediction.from_pretrained.local_files_only(bool,",description:`<strong>local_files_only(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to only look at local files (e.g., not try downloading the model).`,name:"local_files_only(bool,"},{anchor:"transformers.AutoModelForNextSentencePrediction.from_pretrained.revision",description:`<strong>revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a
git-based system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any
identifier allowed by git.`,name:"revision"},{anchor:"transformers.AutoModelForNextSentencePrediction.from_pretrained.trust_remote_code",description:`<strong>trust_remote_code</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to allow for custom models defined on the Hub in their own modeling files. This option
should only be set to <code>True</code> for repositories you trust and in which you have read the code, as it will
execute code present on the Hub on your local machine.`,name:"trust_remote_code"},{anchor:"transformers.AutoModelForNextSentencePrediction.from_pretrained.kwargs",description:`<strong>kwargs</strong> (additional 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/pr_17548/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/vr_17548/src/transformers/models/auto/auto_factory.py#L417"}}),$4=new B({props:{anchor:"transformers.AutoModelForNextSentencePrediction.from_pretrained.example",$$slots:{default:[LSt]},$$scope:{ctx:L}}}),H6=new te({}),U6=new R({props:{name:"class transformers.AutoModelForTokenClassification",anchor:"transformers.AutoModelForTokenClassification",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/vr_17548/src/transformers/models/auto/modeling_auto.py#L811"}}),Y6=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/pr_17548/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/pr_17548/en/model_doc/albert#transformers.AlbertConfig">AlbertConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/albert#transformers.AlbertForTokenClassification">AlbertForTokenClassification</a> (ALBERT model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/bert#transformers.BertConfig">BertConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/bert#transformers.BertForTokenClassification">BertForTokenClassification</a> (BERT model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/big_bird#transformers.BigBirdConfig">BigBirdConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/big_bird#transformers.BigBirdForTokenClassification">BigBirdForTokenClassification</a> (BigBird model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/camembert#transformers.CamembertConfig">CamembertConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/camembert#transformers.CamembertForTokenClassification">CamembertForTokenClassification</a> (CamemBERT model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/canine#transformers.CanineConfig">CanineConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/canine#transformers.CanineForTokenClassification">CanineForTokenClassification</a> (CANINE model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/convbert#transformers.ConvBertConfig">ConvBertConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/convbert#transformers.ConvBertForTokenClassification">ConvBertForTokenClassification</a> (ConvBERT model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/data2vec#transformers.Data2VecTextConfig">Data2VecTextConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/data2vec#transformers.Data2VecTextForTokenClassification">Data2VecTextForTokenClassification</a> (Data2VecText model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/deberta#transformers.DebertaConfig">DebertaConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/deberta#transformers.DebertaForTokenClassification">DebertaForTokenClassification</a> (DeBERTa model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/deberta-v2#transformers.DebertaV2Config">DebertaV2Config</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/deberta-v2#transformers.DebertaV2ForTokenClassification">DebertaV2ForTokenClassification</a> (DeBERTa-v2 model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/distilbert#transformers.DistilBertConfig">DistilBertConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/distilbert#transformers.DistilBertForTokenClassification">DistilBertForTokenClassification</a> (DistilBERT model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/electra#transformers.ElectraConfig">ElectraConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/electra#transformers.ElectraForTokenClassification">ElectraForTokenClassification</a> (ELECTRA model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/fnet#transformers.FNetConfig">FNetConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/fnet#transformers.FNetForTokenClassification">FNetForTokenClassification</a> (FNet model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/flaubert#transformers.FlaubertConfig">FlaubertConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/flaubert#transformers.FlaubertForTokenClassification">FlaubertForTokenClassification</a> (FlauBERT model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/funnel#transformers.FunnelConfig">FunnelConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/funnel#transformers.FunnelForTokenClassification">FunnelForTokenClassification</a> (Funnel Transformer model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/gpt2#transformers.GPT2Config">GPT2Config</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/gpt2#transformers.GPT2ForTokenClassification">GPT2ForTokenClassification</a> (OpenAI GPT-2 model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/ibert#transformers.IBertConfig">IBertConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/ibert#transformers.IBertForTokenClassification">IBertForTokenClassification</a> (I-BERT model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/layoutlm#transformers.LayoutLMConfig">LayoutLMConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/layoutlm#transformers.LayoutLMForTokenClassification">LayoutLMForTokenClassification</a> (LayoutLM model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/layoutlmv2#transformers.LayoutLMv2Config">LayoutLMv2Config</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/layoutlmv2#transformers.LayoutLMv2ForTokenClassification">LayoutLMv2ForTokenClassification</a> (LayoutLMv2 model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/layoutlmv3#transformers.LayoutLMv3Config">LayoutLMv3Config</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/layoutlmv3#transformers.LayoutLMv3ForTokenClassification">LayoutLMv3ForTokenClassification</a> (LayoutLMv3 model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/longformer#transformers.LongformerConfig">LongformerConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/longformer#transformers.LongformerForTokenClassification">LongformerForTokenClassification</a> (Longformer model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/mpnet#transformers.MPNetConfig">MPNetConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/mpnet#transformers.MPNetForTokenClassification">MPNetForTokenClassification</a> (MPNet model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/megatron-bert#transformers.MegatronBertConfig">MegatronBertConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/megatron-bert#transformers.MegatronBertForTokenClassification">MegatronBertForTokenClassification</a> (Megatron-BERT model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/mobilebert#transformers.MobileBertConfig">MobileBertConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/mobilebert#transformers.MobileBertForTokenClassification">MobileBertForTokenClassification</a> (MobileBERT model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/nystromformer#transformers.NystromformerConfig">NystromformerConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/nystromformer#transformers.NystromformerForTokenClassification">NystromformerForTokenClassification</a> (Nystr&#xF6;mformer model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/qdqbert#transformers.QDQBertConfig">QDQBertConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/qdqbert#transformers.QDQBertForTokenClassification">QDQBertForTokenClassification</a> (QDQBert model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/rembert#transformers.RemBertConfig">RemBertConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/rembert#transformers.RemBertForTokenClassification">RemBertForTokenClassification</a> (RemBERT model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/roformer#transformers.RoFormerConfig">RoFormerConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/roformer#transformers.RoFormerForTokenClassification">RoFormerForTokenClassification</a> (RoFormer model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/roberta#transformers.RobertaConfig">RobertaConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/roberta#transformers.RobertaForTokenClassification">RobertaForTokenClassification</a> (RoBERTa model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/squeezebert#transformers.SqueezeBertConfig">SqueezeBertConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/squeezebert#transformers.SqueezeBertForTokenClassification">SqueezeBertForTokenClassification</a> (SqueezeBERT model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/xlm#transformers.XLMConfig">XLMConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/xlm#transformers.XLMForTokenClassification">XLMForTokenClassification</a> (XLM model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/xlm-roberta#transformers.XLMRobertaConfig">XLMRobertaConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/xlm-roberta#transformers.XLMRobertaForTokenClassification">XLMRobertaForTokenClassification</a> (XLM-RoBERTa model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/xlm-roberta-xl#transformers.XLMRobertaXLConfig">XLMRobertaXLConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/xlm-roberta-xl#transformers.XLMRobertaXLForTokenClassification">XLMRobertaXLForTokenClassification</a> (XLM-RoBERTa-XL model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/xlnet#transformers.XLNetConfig">XLNetConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/xlnet#transformers.XLNetForTokenClassification">XLNetForTokenClassification</a> (XLNet model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/yoso#transformers.YosoConfig">YosoConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/yoso#transformers.YosoForTokenClassification">YosoForTokenClassification</a> (YOSO model)</li>
</ul>`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/vr_17548/src/transformers/models/auto/auto_factory.py#L389"}}),S4=new B({props:{anchor:"transformers.AutoModelForTokenClassification.from_config.example",$$slots:{default:[xSt]},$$scope:{ctx:L}}}),K6=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/pr_17548/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/pr_17548/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/pr_17548/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/pr_17548/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and
<a href="/docs/transformers/pr_17548/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/pr_17548/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/vr_17548/src/transformers/models/auto/auto_factory.py#L417"}}),hv=new B({props:{anchor:"transformers.AutoModelForTokenClassification.from_pretrained.example",$$slots:{default:[$St]},$$scope:{ctx:L}}}),Z6=new te({}),ey=new R({props:{name:"class transformers.AutoModelForQuestionAnswering",anchor:"transformers.AutoModelForQuestionAnswering",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/vr_17548/src/transformers/models/auto/modeling_auto.py#L793"}}),ry=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/pr_17548/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/pr_17548/en/model_doc/albert#transformers.AlbertConfig">AlbertConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/albert#transformers.AlbertForQuestionAnswering">AlbertForQuestionAnswering</a> (ALBERT model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/bart#transformers.BartConfig">BartConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/bart#transformers.BartForQuestionAnswering">BartForQuestionAnswering</a> (BART model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/bert#transformers.BertConfig">BertConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/bert#transformers.BertForQuestionAnswering">BertForQuestionAnswering</a> (BERT model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/big_bird#transformers.BigBirdConfig">BigBirdConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/big_bird#transformers.BigBirdForQuestionAnswering">BigBirdForQuestionAnswering</a> (BigBird model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/bigbird_pegasus#transformers.BigBirdPegasusConfig">BigBirdPegasusConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/bigbird_pegasus#transformers.BigBirdPegasusForQuestionAnswering">BigBirdPegasusForQuestionAnswering</a> (BigBird-Pegasus model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/camembert#transformers.CamembertConfig">CamembertConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/camembert#transformers.CamembertForQuestionAnswering">CamembertForQuestionAnswering</a> (CamemBERT model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/canine#transformers.CanineConfig">CanineConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/canine#transformers.CanineForQuestionAnswering">CanineForQuestionAnswering</a> (CANINE model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/convbert#transformers.ConvBertConfig">ConvBertConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/convbert#transformers.ConvBertForQuestionAnswering">ConvBertForQuestionAnswering</a> (ConvBERT model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/data2vec#transformers.Data2VecTextConfig">Data2VecTextConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/data2vec#transformers.Data2VecTextForQuestionAnswering">Data2VecTextForQuestionAnswering</a> (Data2VecText model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/deberta#transformers.DebertaConfig">DebertaConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/deberta#transformers.DebertaForQuestionAnswering">DebertaForQuestionAnswering</a> (DeBERTa model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/deberta-v2#transformers.DebertaV2Config">DebertaV2Config</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/deberta-v2#transformers.DebertaV2ForQuestionAnswering">DebertaV2ForQuestionAnswering</a> (DeBERTa-v2 model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/distilbert#transformers.DistilBertConfig">DistilBertConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/distilbert#transformers.DistilBertForQuestionAnswering">DistilBertForQuestionAnswering</a> (DistilBERT model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/electra#transformers.ElectraConfig">ElectraConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/electra#transformers.ElectraForQuestionAnswering">ElectraForQuestionAnswering</a> (ELECTRA model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/fnet#transformers.FNetConfig">FNetConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/fnet#transformers.FNetForQuestionAnswering">FNetForQuestionAnswering</a> (FNet model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/flaubert#transformers.FlaubertConfig">FlaubertConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/flaubert#transformers.FlaubertForQuestionAnsweringSimple">FlaubertForQuestionAnsweringSimple</a> (FlauBERT model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/funnel#transformers.FunnelConfig">FunnelConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/funnel#transformers.FunnelForQuestionAnswering">FunnelForQuestionAnswering</a> (Funnel Transformer model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/gptj#transformers.GPTJConfig">GPTJConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/gptj#transformers.GPTJForQuestionAnswering">GPTJForQuestionAnswering</a> (GPT-J model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/ibert#transformers.IBertConfig">IBertConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/ibert#transformers.IBertForQuestionAnswering">IBertForQuestionAnswering</a> (I-BERT model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/led#transformers.LEDConfig">LEDConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/led#transformers.LEDForQuestionAnswering">LEDForQuestionAnswering</a> (LED model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/layoutlmv2#transformers.LayoutLMv2Config">LayoutLMv2Config</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/layoutlmv2#transformers.LayoutLMv2ForQuestionAnswering">LayoutLMv2ForQuestionAnswering</a> (LayoutLMv2 model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/layoutlmv3#transformers.LayoutLMv3Config">LayoutLMv3Config</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/layoutlmv3#transformers.LayoutLMv3ForQuestionAnswering">LayoutLMv3ForQuestionAnswering</a> (LayoutLMv3 model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/longformer#transformers.LongformerConfig">LongformerConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/longformer#transformers.LongformerForQuestionAnswering">LongformerForQuestionAnswering</a> (Longformer model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/lxmert#transformers.LxmertConfig">LxmertConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/lxmert#transformers.LxmertForQuestionAnswering">LxmertForQuestionAnswering</a> (LXMERT model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/mbart#transformers.MBartConfig">MBartConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/mbart#transformers.MBartForQuestionAnswering">MBartForQuestionAnswering</a> (mBART model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/mpnet#transformers.MPNetConfig">MPNetConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/mpnet#transformers.MPNetForQuestionAnswering">MPNetForQuestionAnswering</a> (MPNet model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/megatron-bert#transformers.MegatronBertConfig">MegatronBertConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/megatron-bert#transformers.MegatronBertForQuestionAnswering">MegatronBertForQuestionAnswering</a> (Megatron-BERT model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/mobilebert#transformers.MobileBertConfig">MobileBertConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/mobilebert#transformers.MobileBertForQuestionAnswering">MobileBertForQuestionAnswering</a> (MobileBERT model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/nystromformer#transformers.NystromformerConfig">NystromformerConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/nystromformer#transformers.NystromformerForQuestionAnswering">NystromformerForQuestionAnswering</a> (Nystr&#xF6;mformer model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/qdqbert#transformers.QDQBertConfig">QDQBertConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/qdqbert#transformers.QDQBertForQuestionAnswering">QDQBertForQuestionAnswering</a> (QDQBert model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/reformer#transformers.ReformerConfig">ReformerConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/reformer#transformers.ReformerForQuestionAnswering">ReformerForQuestionAnswering</a> (Reformer model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/rembert#transformers.RemBertConfig">RemBertConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/rembert#transformers.RemBertForQuestionAnswering">RemBertForQuestionAnswering</a> (RemBERT model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/roformer#transformers.RoFormerConfig">RoFormerConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/roformer#transformers.RoFormerForQuestionAnswering">RoFormerForQuestionAnswering</a> (RoFormer model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/roberta#transformers.RobertaConfig">RobertaConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/roberta#transformers.RobertaForQuestionAnswering">RobertaForQuestionAnswering</a> (RoBERTa model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/splinter#transformers.SplinterConfig">SplinterConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/splinter#transformers.SplinterForQuestionAnswering">SplinterForQuestionAnswering</a> (Splinter model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/squeezebert#transformers.SqueezeBertConfig">SqueezeBertConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/squeezebert#transformers.SqueezeBertForQuestionAnswering">SqueezeBertForQuestionAnswering</a> (SqueezeBERT model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/xlm#transformers.XLMConfig">XLMConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/xlm#transformers.XLMForQuestionAnsweringSimple">XLMForQuestionAnsweringSimple</a> (XLM model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/xlm-roberta#transformers.XLMRobertaConfig">XLMRobertaConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/xlm-roberta#transformers.XLMRobertaForQuestionAnswering">XLMRobertaForQuestionAnswering</a> (XLM-RoBERTa model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/xlm-roberta-xl#transformers.XLMRobertaXLConfig">XLMRobertaXLConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/xlm-roberta-xl#transformers.XLMRobertaXLForQuestionAnswering">XLMRobertaXLForQuestionAnswering</a> (XLM-RoBERTa-XL model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/xlnet#transformers.XLNetConfig">XLNetConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/xlnet#transformers.XLNetForQuestionAnsweringSimple">XLNetForQuestionAnsweringSimple</a> (XLNet model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/yoso#transformers.YosoConfig">YosoConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/yoso#transformers.YosoForQuestionAnswering">YosoForQuestionAnswering</a> (YOSO model)</li>
</ul>`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/vr_17548/src/transformers/models/auto/auto_factory.py#L389"}}),_v=new B({props:{anchor:"transformers.AutoModelForQuestionAnswering.from_config.example",$$slots:{default:[kSt]},$$scope:{ctx:L}}}),ty=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/pr_17548/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/pr_17548/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/pr_17548/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/pr_17548/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and
<a href="/docs/transformers/pr_17548/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/pr_17548/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/vr_17548/src/transformers/models/auto/auto_factory.py#L417"}}),a5=new B({props:{anchor:"transformers.AutoModelForQuestionAnswering.from_pretrained.example",$$slots:{default:[SSt]},$$scope:{ctx:L}}}),ay=new te({}),ny=new R({props:{name:"class transformers.AutoModelForTableQuestionAnswering",anchor:"transformers.AutoModelForTableQuestionAnswering",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/vr_17548/src/transformers/models/auto/modeling_auto.py#L800"}}),ly=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/pr_17548/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/pr_17548/en/model_doc/tapas#transformers.TapasConfig">TapasConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/tapas#transformers.TapasForQuestionAnswering">TapasForQuestionAnswering</a> (TAPAS model)</li>
</ul>`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/vr_17548/src/transformers/models/auto/auto_factory.py#L389"}}),s5=new B({props:{anchor:"transformers.AutoModelForTableQuestionAnswering.from_config.example",$$slots:{default:[RSt]},$$scope:{ctx:L}}}),iy=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/pr_17548/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/pr_17548/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/pr_17548/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/pr_17548/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and
<a href="/docs/transformers/pr_17548/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/pr_17548/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/vr_17548/src/transformers/models/auto/auto_factory.py#L417"}}),d5=new B({props:{anchor:"transformers.AutoModelForTableQuestionAnswering.from_pretrained.example",$$slots:{default:[PSt]},$$scope:{ctx:L}}}),dy=new te({}),cy=new R({props:{name:"class transformers.AutoModelForImageClassification",anchor:"transformers.AutoModelForImageClassification",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/vr_17548/src/transformers/models/auto/modeling_auto.py#L834"}}),my=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/pr_17548/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/pr_17548/en/model_doc/beit#transformers.BeitConfig">BeitConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/beit#transformers.BeitForImageClassification">BeitForImageClassification</a> (BEiT model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/convnext#transformers.ConvNextConfig">ConvNextConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/convnext#transformers.ConvNextForImageClassification">ConvNextForImageClassification</a> (ConvNeXT model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/cvt#transformers.CvtConfig">CvtConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/cvt#transformers.CvtForImageClassification">CvtForImageClassification</a> (CvT model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/data2vec#transformers.Data2VecVisionConfig">Data2VecVisionConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/data2vec#transformers.Data2VecVisionForImageClassification">Data2VecVisionForImageClassification</a> (Data2VecVision model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/deit#transformers.DeiTConfig">DeiTConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/deit#transformers.DeiTForImageClassification">DeiTForImageClassification</a> or <a href="/docs/transformers/pr_17548/en/model_doc/deit#transformers.DeiTForImageClassificationWithTeacher">DeiTForImageClassificationWithTeacher</a> (DeiT model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/imagegpt#transformers.ImageGPTConfig">ImageGPTConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/imagegpt#transformers.ImageGPTForImageClassification">ImageGPTForImageClassification</a> (ImageGPT model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/levit#transformers.LevitConfig">LevitConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/levit#transformers.LevitForImageClassification">LevitForImageClassification</a> or <a href="/docs/transformers/pr_17548/en/model_doc/levit#transformers.LevitForImageClassificationWithTeacher">LevitForImageClassificationWithTeacher</a> (LeViT model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/perceiver#transformers.PerceiverConfig">PerceiverConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/perceiver#transformers.PerceiverForImageClassificationLearned">PerceiverForImageClassificationLearned</a> or <a href="/docs/transformers/pr_17548/en/model_doc/perceiver#transformers.PerceiverForImageClassificationFourier">PerceiverForImageClassificationFourier</a> or <a href="/docs/transformers/pr_17548/en/model_doc/perceiver#transformers.PerceiverForImageClassificationConvProcessing">PerceiverForImageClassificationConvProcessing</a> (Perceiver model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/poolformer#transformers.PoolFormerConfig">PoolFormerConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/poolformer#transformers.PoolFormerForImageClassification">PoolFormerForImageClassification</a> (PoolFormer model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/regnet#transformers.RegNetConfig">RegNetConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/regnet#transformers.RegNetForImageClassification">RegNetForImageClassification</a> (RegNet model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/resnet#transformers.ResNetConfig">ResNetConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/resnet#transformers.ResNetForImageClassification">ResNetForImageClassification</a> (ResNet model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/segformer#transformers.SegformerConfig">SegformerConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/segformer#transformers.SegformerForImageClassification">SegformerForImageClassification</a> (SegFormer model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/swin#transformers.SwinConfig">SwinConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/swin#transformers.SwinForImageClassification">SwinForImageClassification</a> (Swin Transformer model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/van#transformers.VanConfig">VanConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/van#transformers.VanForImageClassification">VanForImageClassification</a> (VAN model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/vit#transformers.ViTConfig">ViTConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/vit#transformers.ViTForImageClassification">ViTForImageClassification</a> (ViT model)</li>
</ul>`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/vr_17548/src/transformers/models/auto/auto_factory.py#L389"}}),f5=new B({props:{anchor:"transformers.AutoModelForImageClassification.from_config.example",$$slots:{default:[BSt]},$$scope:{ctx:L}}}),gy=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/pr_17548/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/pr_17548/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/pr_17548/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/pr_17548/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and
<a href="/docs/transformers/pr_17548/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/pr_17548/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/vr_17548/src/transformers/models/auto/auto_factory.py#L417"}}),w5=new B({props:{anchor:"transformers.AutoModelForImageClassification.from_pretrained.example",$$slots:{default:[ISt]},$$scope:{ctx:L}}}),hy=new te({}),py=new R({props:{name:"class transformers.AutoModelForVision2Seq",anchor:"transformers.AutoModelForVision2Seq",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/vr_17548/src/transformers/models/auto/modeling_auto.py#L873"}}),uy=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/pr_17548/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/pr_17548/en/model_doc/vision-encoder-decoder#transformers.VisionEncoderDecoderConfig">VisionEncoderDecoderConfig</a> configuration class: <a href="/docs/transformers/pr_17548/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/vr_17548/src/transformers/models/auto/auto_factory.py#L389"}}),y5=new B({props:{anchor:"transformers.AutoModelForVision2Seq.from_config.example",$$slots:{default:[NSt]},$$scope:{ctx:L}}}),by=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/pr_17548/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/pr_17548/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/pr_17548/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/pr_17548/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and
<a href="/docs/transformers/pr_17548/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/pr_17548/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/vr_17548/src/transformers/models/auto/auto_factory.py#L417"}}),$5=new B({props:{anchor:"transformers.AutoModelForVision2Seq.from_pretrained.example",$$slots:{default:[qSt]},$$scope:{ctx:L}}}),vy=new te({}),Fy=new R({props:{name:"class transformers.AutoModelForAudioClassification",anchor:"transformers.AutoModelForAudioClassification",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/vr_17548/src/transformers/models/auto/modeling_auto.py#L880"}}),My=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/pr_17548/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/pr_17548/en/model_doc/data2vec#transformers.Data2VecAudioConfig">Data2VecAudioConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/data2vec#transformers.Data2VecAudioForSequenceClassification">Data2VecAudioForSequenceClassification</a> (Data2VecAudio model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/hubert#transformers.HubertConfig">HubertConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/hubert#transformers.HubertForSequenceClassification">HubertForSequenceClassification</a> (Hubert model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/sew#transformers.SEWConfig">SEWConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/sew#transformers.SEWForSequenceClassification">SEWForSequenceClassification</a> (SEW model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/sew-d#transformers.SEWDConfig">SEWDConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/sew-d#transformers.SEWDForSequenceClassification">SEWDForSequenceClassification</a> (SEW-D model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/unispeech#transformers.UniSpeechConfig">UniSpeechConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/unispeech#transformers.UniSpeechForSequenceClassification">UniSpeechForSequenceClassification</a> (UniSpeech model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/unispeech-sat#transformers.UniSpeechSatConfig">UniSpeechSatConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/unispeech-sat#transformers.UniSpeechSatForSequenceClassification">UniSpeechSatForSequenceClassification</a> (UniSpeechSat model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/wav2vec2#transformers.Wav2Vec2Config">Wav2Vec2Config</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/wav2vec2#transformers.Wav2Vec2ForSequenceClassification">Wav2Vec2ForSequenceClassification</a> (Wav2Vec2 model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/wav2vec2-conformer#transformers.Wav2Vec2ConformerConfig">Wav2Vec2ConformerConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/wav2vec2-conformer#transformers.Wav2Vec2ConformerForSequenceClassification">Wav2Vec2ConformerForSequenceClassification</a> (Wav2Vec2-Conformer model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/wavlm#transformers.WavLMConfig">WavLMConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/wavlm#transformers.WavLMForSequenceClassification">WavLMForSequenceClassification</a> (WavLM model)</li>
</ul>`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/vr_17548/src/transformers/models/auto/auto_factory.py#L389"}}),S5=new B({props:{anchor:"transformers.AutoModelForAudioClassification.from_config.example",$$slots:{default:[jSt]},$$scope:{ctx:L}}}),Ey=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/pr_17548/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/pr_17548/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/pr_17548/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/pr_17548/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and
<a href="/docs/transformers/pr_17548/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/pr_17548/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/vr_17548/src/transformers/models/auto/auto_factory.py#L417"}}),V5=new B({props:{anchor:"transformers.AutoModelForAudioClassification.from_pretrained.example",$$slots:{default:[DSt]},$$scope:{ctx:L}}}),Cy=new te({}),wy=new R({props:{name:"class transformers.AutoModelForAudioFrameClassification",anchor:"transformers.AutoModelForAudioFrameClassification",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/vr_17548/src/transformers/models/auto/modeling_auto.py#L903"}}),yy=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/pr_17548/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/pr_17548/en/model_doc/data2vec#transformers.Data2VecAudioConfig">Data2VecAudioConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/data2vec#transformers.Data2VecAudioForAudioFrameClassification">Data2VecAudioForAudioFrameClassification</a> (Data2VecAudio model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/unispeech-sat#transformers.UniSpeechSatConfig">UniSpeechSatConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/unispeech-sat#transformers.UniSpeechSatForAudioFrameClassification">UniSpeechSatForAudioFrameClassification</a> (UniSpeechSat model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/wav2vec2#transformers.Wav2Vec2Config">Wav2Vec2Config</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/wav2vec2#transformers.Wav2Vec2ForAudioFrameClassification">Wav2Vec2ForAudioFrameClassification</a> (Wav2Vec2 model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/wav2vec2-conformer#transformers.Wav2Vec2ConformerConfig">Wav2Vec2ConformerConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/wav2vec2-conformer#transformers.Wav2Vec2ConformerForAudioFrameClassification">Wav2Vec2ConformerForAudioFrameClassification</a> (Wav2Vec2-Conformer model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/wavlm#transformers.WavLMConfig">WavLMConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/wavlm#transformers.WavLMForAudioFrameClassification">WavLMForAudioFrameClassification</a> (WavLM model)</li>
</ul>`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/vr_17548/src/transformers/models/auto/auto_factory.py#L389"}}),z5=new B({props:{anchor:"transformers.AutoModelForAudioFrameClassification.from_config.example",$$slots:{default:[GSt]},$$scope:{ctx:L}}}),Ly=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/pr_17548/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/pr_17548/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/pr_17548/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/pr_17548/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and
<a href="/docs/transformers/pr_17548/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/pr_17548/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/vr_17548/src/transformers/models/auto/auto_factory.py#L417"}}),K5=new B({props:{anchor:"transformers.AutoModelForAudioFrameClassification.from_pretrained.example",$$slots:{default:[OSt]},$$scope:{ctx:L}}}),xy=new te({}),$y=new R({props:{name:"class transformers.AutoModelForCTC",anchor:"transformers.AutoModelForCTC",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/vr_17548/src/transformers/models/auto/modeling_auto.py#L887"}}),Sy=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/pr_17548/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/pr_17548/en/model_doc/data2vec#transformers.Data2VecAudioConfig">Data2VecAudioConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/data2vec#transformers.Data2VecAudioForCTC">Data2VecAudioForCTC</a> (Data2VecAudio model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/hubert#transformers.HubertConfig">HubertConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/hubert#transformers.HubertForCTC">HubertForCTC</a> (Hubert model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/sew#transformers.SEWConfig">SEWConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/sew#transformers.SEWForCTC">SEWForCTC</a> (SEW model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/sew-d#transformers.SEWDConfig">SEWDConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/sew-d#transformers.SEWDForCTC">SEWDForCTC</a> (SEW-D model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/unispeech#transformers.UniSpeechConfig">UniSpeechConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/unispeech#transformers.UniSpeechForCTC">UniSpeechForCTC</a> (UniSpeech model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/unispeech-sat#transformers.UniSpeechSatConfig">UniSpeechSatConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/unispeech-sat#transformers.UniSpeechSatForCTC">UniSpeechSatForCTC</a> (UniSpeechSat model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/wav2vec2#transformers.Wav2Vec2Config">Wav2Vec2Config</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/wav2vec2#transformers.Wav2Vec2ForCTC">Wav2Vec2ForCTC</a> (Wav2Vec2 model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/wav2vec2-conformer#transformers.Wav2Vec2ConformerConfig">Wav2Vec2ConformerConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/wav2vec2-conformer#transformers.Wav2Vec2ConformerForCTC">Wav2Vec2ConformerForCTC</a> (Wav2Vec2-Conformer model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/wavlm#transformers.WavLMConfig">WavLMConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/wavlm#transformers.WavLMForCTC">WavLMForCTC</a> (WavLM model)</li>
</ul>`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/vr_17548/src/transformers/models/auto/auto_factory.py#L389"}}),eF=new B({props:{anchor:"transformers.AutoModelForCTC.from_config.example",$$slots:{default:[VSt]},$$scope:{ctx:L}}}),Ry=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/pr_17548/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/pr_17548/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/pr_17548/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/pr_17548/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and
<a href="/docs/transformers/pr_17548/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/pr_17548/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/vr_17548/src/transformers/models/auto/auto_factory.py#L417"}}),fF=new B({props:{anchor:"transformers.AutoModelForCTC.from_pretrained.example",$$slots:{default:[XSt]},$$scope:{ctx:L}}}),Py=new te({}),By=new R({props:{name:"class transformers.AutoModelForSpeechSeq2Seq",anchor:"transformers.AutoModelForSpeechSeq2Seq",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/vr_17548/src/transformers/models/auto/modeling_auto.py#L894"}}),Ny=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/pr_17548/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/pr_17548/en/model_doc/speech_to_text#transformers.Speech2TextConfig">Speech2TextConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/speech_to_text#transformers.Speech2TextForConditionalGeneration">Speech2TextForConditionalGeneration</a> (Speech2Text model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/speech-encoder-decoder#transformers.SpeechEncoderDecoderConfig">SpeechEncoderDecoderConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/speech-encoder-decoder#transformers.SpeechEncoderDecoderModel">SpeechEncoderDecoderModel</a> (Speech Encoder decoder model)</li>
</ul>`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/vr_17548/src/transformers/models/auto/auto_factory.py#L389"}}),gF=new B({props:{anchor:"transformers.AutoModelForSpeechSeq2Seq.from_config.example",$$slots:{default:[zSt]},$$scope:{ctx:L}}}),qy=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/pr_17548/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/pr_17548/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/pr_17548/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/pr_17548/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and
<a href="/docs/transformers/pr_17548/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/pr_17548/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/vr_17548/src/transformers/models/auto/auto_factory.py#L417"}}),uF=new B({props:{anchor:"transformers.AutoModelForSpeechSeq2Seq.from_pretrained.example",$$slots:{default:[WSt]},$$scope:{ctx:L}}}),Dy=new te({}),Gy=new R({props:{name:"class transformers.AutoModelForAudioXVector",anchor:"transformers.AutoModelForAudioXVector",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/vr_17548/src/transformers/models/auto/modeling_auto.py#L912"}}),Vy=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/pr_17548/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/pr_17548/en/model_doc/data2vec#transformers.Data2VecAudioConfig">Data2VecAudioConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/data2vec#transformers.Data2VecAudioForXVector">Data2VecAudioForXVector</a> (Data2VecAudio model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/unispeech-sat#transformers.UniSpeechSatConfig">UniSpeechSatConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/unispeech-sat#transformers.UniSpeechSatForXVector">UniSpeechSatForXVector</a> (UniSpeechSat model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/wav2vec2#transformers.Wav2Vec2Config">Wav2Vec2Config</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/wav2vec2#transformers.Wav2Vec2ForXVector">Wav2Vec2ForXVector</a> (Wav2Vec2 model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/wav2vec2-conformer#transformers.Wav2Vec2ConformerConfig">Wav2Vec2ConformerConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/wav2vec2-conformer#transformers.Wav2Vec2ConformerForXVector">Wav2Vec2ConformerForXVector</a> (Wav2Vec2-Conformer model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/wavlm#transformers.WavLMConfig">WavLMConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/wavlm#transformers.WavLMForXVector">WavLMForXVector</a> (WavLM model)</li>
</ul>`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/vr_17548/src/transformers/models/auto/auto_factory.py#L389"}}),vF=new B({props:{anchor:"transformers.AutoModelForAudioXVector.from_config.example",$$slots:{default:[QSt]},$$scope:{ctx:L}}}),Xy=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/pr_17548/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/pr_17548/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/pr_17548/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/pr_17548/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and
<a href="/docs/transformers/pr_17548/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/pr_17548/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/vr_17548/src/transformers/models/auto/auto_factory.py#L417"}}),AF=new B({props:{anchor:"transformers.AutoModelForAudioXVector.from_pretrained.example",$$slots:{default:[HSt]},$$scope:{ctx:L}}}),zy=new te({}),Wy=new R({props:{name:"class transformers.AutoModelForMaskedImageModeling",anchor:"transformers.AutoModelForMaskedImageModeling",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/vr_17548/src/transformers/models/auto/modeling_auto.py#L919"}}),Hy=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/pr_17548/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/pr_17548/en/model_doc/deit#transformers.DeiTConfig">DeiTConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/deit#transformers.DeiTForMaskedImageModeling">DeiTForMaskedImageModeling</a> (DeiT model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/swin#transformers.SwinConfig">SwinConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/swin#transformers.SwinForMaskedImageModeling">SwinForMaskedImageModeling</a> (Swin Transformer model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/vit#transformers.ViTConfig">ViTConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/vit#transformers.ViTForMaskedImageModeling">ViTForMaskedImageModeling</a> (ViT model)</li>
</ul>`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/vr_17548/src/transformers/models/auto/auto_factory.py#L389"}}),LF=new B({props:{anchor:"transformers.AutoModelForMaskedImageModeling.from_config.example",$$slots:{default:[USt]},$$scope:{ctx:L}}}),Uy=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/pr_17548/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/pr_17548/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/pr_17548/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/pr_17548/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and
<a href="/docs/transformers/pr_17548/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/pr_17548/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/vr_17548/src/transformers/models/auto/auto_factory.py#L417"}}),RF=new B({props:{anchor:"transformers.AutoModelForMaskedImageModeling.from_pretrained.example",$$slots:{default:[JSt]},$$scope:{ctx:L}}}),Jy=new te({}),Yy=new R({props:{name:"class transformers.AutoModelForObjectDetection",anchor:"transformers.AutoModelForObjectDetection",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/vr_17548/src/transformers/models/auto/modeling_auto.py#L866"}}),Zy=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/pr_17548/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/pr_17548/en/model_doc/detr#transformers.DetrConfig">DetrConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/detr#transformers.DetrForObjectDetection">DetrForObjectDetection</a> (DETR model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/yolos#transformers.YolosConfig">YolosConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/yolos#transformers.YolosForObjectDetection">YolosForObjectDetection</a> (YOLOS model)</li>
</ul>`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/vr_17548/src/transformers/models/auto/auto_factory.py#L389"}}),BF=new B({props:{anchor:"transformers.AutoModelForObjectDetection.from_config.example",$$slots:{default:[YSt]},$$scope:{ctx:L}}}),eL=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/pr_17548/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;
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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/pr_17548/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.AutoModelForObjectDetection.from_pretrained.state_dict",description:`<strong>state_dict</strong> (<em>Dict[str, torch.Tensor]</em>, <em>optional</em>) &#x2014;
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<p>This option can be used if you want to create a model from a pretrained configuration but load your own
weights. In this case though, you should check if using <a href="/docs/transformers/pr_17548/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and
<a href="/docs/transformers/pr_17548/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
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Whether or not to force the (re-)download of the model weights and configuration files, overriding the
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Whether or not to delete incompletely received files. Will attempt to resume the download if such a
file exists.`,name:"resume_download"},{anchor:"transformers.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
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Whether or not to allow for custom models defined on the Hub in their own modeling files. This option
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execute code present on the Hub on your local machine.`,name:"trust_remote_code"},{anchor:"transformers.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/pr_17548/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/vr_17548/src/transformers/models/auto/auto_factory.py#L417"}}),jF=new B({props:{anchor:"transformers.AutoModelForObjectDetection.from_pretrained.example",$$slots:{default:[KSt]},$$scope:{ctx:L}}}),rL=new te({}),tL=new R({props:{name:"class transformers.AutoModelForImageSegmentation",anchor:"transformers.AutoModelForImageSegmentation",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/vr_17548/src/transformers/models/auto/modeling_auto.py#L841"}}),nL=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/pr_17548/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/pr_17548/en/model_doc/detr#transformers.DetrConfig">DetrConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/detr#transformers.DetrForSegmentation">DetrForSegmentation</a> (DETR model)</li>
</ul>`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/vr_17548/src/transformers/models/auto/auto_factory.py#L389"}}),GF=new B({props:{anchor:"transformers.AutoModelForImageSegmentation.from_config.example",$$slots:{default:[ZSt]},$$scope:{ctx:L}}}),sL=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/pr_17548/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/pr_17548/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/pr_17548/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/pr_17548/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and
<a href="/docs/transformers/pr_17548/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
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Whether or not to delete incompletely received files. Will attempt to resume the download if such a
file exists.`,name:"resume_download"},{anchor:"transformers.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
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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/pr_17548/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/pr_17548/en/model_doc/beit#transformers.BeitConfig">BeitConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/beit#transformers.BeitForSemanticSegmentation">BeitForSemanticSegmentation</a> (BEiT model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/dpt#transformers.DPTConfig">DPTConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/dpt#transformers.DPTForSemanticSegmentation">DPTForSemanticSegmentation</a> (DPT model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/data2vec#transformers.Data2VecVisionConfig">Data2VecVisionConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/data2vec#transformers.Data2VecVisionForSemanticSegmentation">Data2VecVisionForSemanticSegmentation</a> (Data2VecVision model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/segformer#transformers.SegformerConfig">SegformerConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/segformer#transformers.SegformerForSemanticSegmentation">SegformerForSemanticSegmentation</a> (SegFormer model)</li>
</ul>`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/vr_17548/src/transformers/models/auto/auto_factory.py#L389"}}),WF=new B({props:{anchor:"transformers.AutoModelForSemanticSegmentation.from_config.example",$$slots:{default:[oRt]},$$scope:{ctx:L}}}),fL=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/pr_17548/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a>, e.g., <code>./my_model_directory/</code>.</li>
<li>A path or url to a <em>tensorflow index checkpoint file</em> (e.g, <code>./tf_model/model.ckpt.index</code>). In
this case, <code>from_tf</code> should be set to <code>True</code> and a configuration object should be provided as
<code>config</code> argument. This loading path is slower than converting the TensorFlow checkpoint in a
PyTorch model using the provided conversion scripts and loading the PyTorch model afterwards.</li>
</ul>`,name:"pretrained_model_name_or_path"},{anchor:"transformers.AutoModelForSemanticSegmentation.from_pretrained.model_args",description:`<strong>model_args</strong> (additional positional arguments, <em>optional</em>) &#x2014;
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Configuration for the model to use instead of an automatically loaded configuration. Configuration can
be automatically loaded when:</p>
<ul>
<li>The model is a model provided by the library (loaded with the <em>model id</em> string of a pretrained
model).</li>
<li>The model was saved using <a href="/docs/transformers/pr_17548/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/pr_17548/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and
<a href="/docs/transformers/pr_17548/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/pr_17548/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/vr_17548/src/transformers/models/auto/auto_factory.py#L417"}}),KF=new B({props:{anchor:"transformers.AutoModelForSemanticSegmentation.from_pretrained.example",$$slots:{default:[rRt]},$$scope:{ctx:L}}}),mL=new te({}),gL=new R({props:{name:"class transformers.AutoModelForInstanceSegmentation",anchor:"transformers.AutoModelForInstanceSegmentation",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/vr_17548/src/transformers/models/auto/modeling_auto.py#L857"}}),pL=new R({props:{name:"from_config",anchor:"transformers.AutoModelForInstanceSegmentation.from_config",parameters:[{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.AutoModelForInstanceSegmentation.from_config.config",description:`<strong>config</strong> (<a href="/docs/transformers/pr_17548/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/pr_17548/en/model_doc/maskformer#transformers.MaskFormerConfig">MaskFormerConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/maskformer#transformers.MaskFormerForInstanceSegmentation">MaskFormerForInstanceSegmentation</a> (MaskFormer model)</li>
</ul>`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/vr_17548/src/transformers/models/auto/auto_factory.py#L389"}}),eT=new B({props:{anchor:"transformers.AutoModelForInstanceSegmentation.from_config.example",$$slots:{default:[tRt]},$$scope:{ctx:L}}}),_L=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/pr_17548/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a>, e.g., <code>./my_model_directory/</code>.</li>
<li>A path or url to a <em>tensorflow index checkpoint file</em> (e.g, <code>./tf_model/model.ckpt.index</code>). In
this case, <code>from_tf</code> should be set to <code>True</code> and a configuration object should be provided as
<code>config</code> argument. This loading path is slower than converting the TensorFlow checkpoint in a
PyTorch model using the provided conversion scripts and loading the PyTorch model afterwards.</li>
</ul>`,name:"pretrained_model_name_or_path"},{anchor:"transformers.AutoModelForInstanceSegmentation.from_pretrained.model_args",description:`<strong>model_args</strong> (additional positional arguments, <em>optional</em>) &#x2014;
Will be passed along to the underlying model <code>__init__()</code> method.`,name:"model_args"},{anchor:"transformers.AutoModelForInstanceSegmentation.from_pretrained.config",description:`<strong>config</strong> (<a href="/docs/transformers/pr_17548/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/pr_17548/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/pr_17548/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and
<a href="/docs/transformers/pr_17548/en/main_classes/model#transformers.PreTrainedModel.from_pretrained">from_pretrained()</a> is not a simpler option.`,name:"state_dict"},{anchor:"transformers.AutoModelForInstanceSegmentation.from_pretrained.cache_dir",description:`<strong>cache_dir</strong> (<code>str</code> or <code>os.PathLike</code>, <em>optional</em>) &#x2014;
Path to a directory in which a downloaded pretrained model configuration should be cached if the
standard cache should not be used.`,name:"cache_dir"},{anchor:"transformers.AutoModelForInstanceSegmentation.from_pretrained.from_tf",description:`<strong>from_tf</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Load the model weights from a TensorFlow checkpoint save file (see docstring of
<code>pretrained_model_name_or_path</code> argument).`,name:"from_tf"},{anchor:"transformers.AutoModelForInstanceSegmentation.from_pretrained.force_download",description:`<strong>force_download</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to force the (re-)download of the model weights and configuration files, overriding the
cached versions if they exist.`,name:"force_download"},{anchor:"transformers.AutoModelForInstanceSegmentation.from_pretrained.resume_download",description:`<strong>resume_download</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to delete incompletely received files. Will attempt to resume the download if such a
file exists.`,name:"resume_download"},{anchor:"transformers.AutoModelForInstanceSegmentation.from_pretrained.proxies",description:`<strong>proxies</strong> (<code>Dict[str, str]</code>, <em>optional</em>) &#x2014;
A dictionary of proxy servers to use by protocol or endpoint, e.g., <code>{&apos;http&apos;: &apos;foo.bar:3128&apos;, &apos;http://hostname&apos;: &apos;foo.bar:4012&apos;}</code>. The proxies are used on each request.`,name:"proxies"},{anchor:"transformers.AutoModelForInstanceSegmentation.from_pretrained.output_loading_info(bool,",description:`<strong>output_loading_info(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether ot not to also return a dictionary containing missing keys, unexpected keys and error messages.`,name:"output_loading_info(bool,"},{anchor:"transformers.AutoModelForInstanceSegmentation.from_pretrained.local_files_only(bool,",description:`<strong>local_files_only(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
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
should only be set to <code>True</code> for repositories you trust and in which you have read the code, as it will
execute code present on the Hub on your local machine.`,name:"trust_remote_code"},{anchor:"transformers.AutoModelForInstanceSegmentation.from_pretrained.kwargs",description:`<strong>kwargs</strong> (additional 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/pr_17548/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/vr_17548/src/transformers/models/auto/auto_factory.py#L417"}}),tT=new B({props:{anchor:"transformers.AutoModelForInstanceSegmentation.from_pretrained.example",$$slots:{default:[aRt]},$$scope:{ctx:L}}}),uL=new te({}),bL=new R({props:{name:"class transformers.TFAutoModel",anchor:"transformers.TFAutoModel",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/vr_17548/src/transformers/models/auto/modeling_tf_auto.py#L396"}}),FL=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/pr_17548/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/pr_17548/en/model_doc/albert#transformers.AlbertConfig">AlbertConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/albert#transformers.TFAlbertModel">TFAlbertModel</a> (ALBERT model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/bart#transformers.BartConfig">BartConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/bart#transformers.TFBartModel">TFBartModel</a> (BART model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/bert#transformers.BertConfig">BertConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/bert#transformers.TFBertModel">TFBertModel</a> (BERT model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/blenderbot#transformers.BlenderbotConfig">BlenderbotConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/blenderbot#transformers.TFBlenderbotModel">TFBlenderbotModel</a> (Blenderbot model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/blenderbot-small#transformers.BlenderbotSmallConfig">BlenderbotSmallConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/blenderbot-small#transformers.TFBlenderbotSmallModel">TFBlenderbotSmallModel</a> (BlenderbotSmall model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/clip#transformers.CLIPConfig">CLIPConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/clip#transformers.TFCLIPModel">TFCLIPModel</a> (CLIP model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/ctrl#transformers.CTRLConfig">CTRLConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/ctrl#transformers.TFCTRLModel">TFCTRLModel</a> (CTRL model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/camembert#transformers.CamembertConfig">CamembertConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/camembert#transformers.TFCamembertModel">TFCamembertModel</a> (CamemBERT model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/convbert#transformers.ConvBertConfig">ConvBertConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/convbert#transformers.TFConvBertModel">TFConvBertModel</a> (ConvBERT model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/convnext#transformers.ConvNextConfig">ConvNextConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/convnext#transformers.TFConvNextModel">TFConvNextModel</a> (ConvNeXT model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/dpr#transformers.DPRConfig">DPRConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/dpr#transformers.TFDPRQuestionEncoder">TFDPRQuestionEncoder</a> (DPR model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/data2vec#transformers.Data2VecVisionConfig">Data2VecVisionConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/data2vec#transformers.TFData2VecVisionModel">TFData2VecVisionModel</a> (Data2VecVision model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/deberta#transformers.DebertaConfig">DebertaConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/deberta#transformers.TFDebertaModel">TFDebertaModel</a> (DeBERTa model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/deberta-v2#transformers.DebertaV2Config">DebertaV2Config</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/deberta-v2#transformers.TFDebertaV2Model">TFDebertaV2Model</a> (DeBERTa-v2 model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/distilbert#transformers.DistilBertConfig">DistilBertConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/distilbert#transformers.TFDistilBertModel">TFDistilBertModel</a> (DistilBERT model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/electra#transformers.ElectraConfig">ElectraConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/electra#transformers.TFElectraModel">TFElectraModel</a> (ELECTRA model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/flaubert#transformers.FlaubertConfig">FlaubertConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/flaubert#transformers.TFFlaubertModel">TFFlaubertModel</a> (FlauBERT model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/funnel#transformers.FunnelConfig">FunnelConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/funnel#transformers.TFFunnelModel">TFFunnelModel</a> or <a href="/docs/transformers/pr_17548/en/model_doc/funnel#transformers.TFFunnelBaseModel">TFFunnelBaseModel</a> (Funnel Transformer model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/gpt2#transformers.GPT2Config">GPT2Config</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/gpt2#transformers.TFGPT2Model">TFGPT2Model</a> (OpenAI GPT-2 model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/gptj#transformers.GPTJConfig">GPTJConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/gptj#transformers.TFGPTJModel">TFGPTJModel</a> (GPT-J model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/hubert#transformers.HubertConfig">HubertConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/hubert#transformers.TFHubertModel">TFHubertModel</a> (Hubert model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/led#transformers.LEDConfig">LEDConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/led#transformers.TFLEDModel">TFLEDModel</a> (LED model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/layoutlm#transformers.LayoutLMConfig">LayoutLMConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/layoutlm#transformers.TFLayoutLMModel">TFLayoutLMModel</a> (LayoutLM model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/longformer#transformers.LongformerConfig">LongformerConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/longformer#transformers.TFLongformerModel">TFLongformerModel</a> (Longformer model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/lxmert#transformers.LxmertConfig">LxmertConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/lxmert#transformers.TFLxmertModel">TFLxmertModel</a> (LXMERT model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/mbart#transformers.MBartConfig">MBartConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/mbart#transformers.TFMBartModel">TFMBartModel</a> (mBART model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/mpnet#transformers.MPNetConfig">MPNetConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/mpnet#transformers.TFMPNetModel">TFMPNetModel</a> (MPNet model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/mt5#transformers.MT5Config">MT5Config</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/mt5#transformers.TFMT5Model">TFMT5Model</a> (MT5 model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/marian#transformers.MarianConfig">MarianConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/marian#transformers.TFMarianModel">TFMarianModel</a> (Marian model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/mobilebert#transformers.MobileBertConfig">MobileBertConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/mobilebert#transformers.TFMobileBertModel">TFMobileBertModel</a> (MobileBERT model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/opt#transformers.OPTConfig">OPTConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/opt#transformers.TFOPTModel">TFOPTModel</a> (OPT model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/openai-gpt#transformers.OpenAIGPTConfig">OpenAIGPTConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/openai-gpt#transformers.TFOpenAIGPTModel">TFOpenAIGPTModel</a> (OpenAI GPT model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/pegasus#transformers.PegasusConfig">PegasusConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/pegasus#transformers.TFPegasusModel">TFPegasusModel</a> (Pegasus model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/rembert#transformers.RemBertConfig">RemBertConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/rembert#transformers.TFRemBertModel">TFRemBertModel</a> (RemBERT model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/roformer#transformers.RoFormerConfig">RoFormerConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/roformer#transformers.TFRoFormerModel">TFRoFormerModel</a> (RoFormer model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/roberta#transformers.RobertaConfig">RobertaConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/roberta#transformers.TFRobertaModel">TFRobertaModel</a> (RoBERTa model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/speech_to_text#transformers.Speech2TextConfig">Speech2TextConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/speech_to_text#transformers.TFSpeech2TextModel">TFSpeech2TextModel</a> (Speech2Text model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/swin#transformers.SwinConfig">SwinConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/swin#transformers.TFSwinModel">TFSwinModel</a> (Swin Transformer model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/t5#transformers.T5Config">T5Config</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/t5#transformers.TFT5Model">TFT5Model</a> (T5 model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/tapas#transformers.TapasConfig">TapasConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/tapas#transformers.TFTapasModel">TFTapasModel</a> (TAPAS model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/transfo-xl#transformers.TransfoXLConfig">TransfoXLConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/transfo-xl#transformers.TFTransfoXLModel">TFTransfoXLModel</a> (Transformer-XL model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/vit#transformers.ViTConfig">ViTConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/vit#transformers.TFViTModel">TFViTModel</a> (ViT model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/vit_mae#transformers.ViTMAEConfig">ViTMAEConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/vit_mae#transformers.TFViTMAEModel">TFViTMAEModel</a> (ViTMAE model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/wav2vec2#transformers.Wav2Vec2Config">Wav2Vec2Config</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/wav2vec2#transformers.TFWav2Vec2Model">TFWav2Vec2Model</a> (Wav2Vec2 model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/xlm#transformers.XLMConfig">XLMConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/xlm#transformers.TFXLMModel">TFXLMModel</a> (XLM model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/xlm-roberta#transformers.XLMRobertaConfig">XLMRobertaConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/xlm-roberta#transformers.TFXLMRobertaModel">TFXLMRobertaModel</a> (XLM-RoBERTa model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/xlnet#transformers.XLNetConfig">XLNetConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/xlnet#transformers.TFXLNetModel">TFXLNetModel</a> (XLNet model)</li>
</ul>`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/vr_17548/src/transformers/models/auto/auto_factory.py#L389"}}),nT=new B({props:{anchor:"transformers.TFAutoModel.from_config.example",$$slots:{default:[nRt]},$$scope:{ctx:L}}}),TL=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/pr_17548/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/pr_17548/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/pr_17548/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/pr_17548/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/vr_17548/src/transformers/models/auto/auto_factory.py#L417"}}),KT=new B({props:{anchor:"transformers.TFAutoModel.from_pretrained.example",$$slots:{default:[sRt]},$$scope:{ctx:L}}}),ML=new te({}),EL=new R({props:{name:"class transformers.TFAutoModelForPreTraining",anchor:"transformers.TFAutoModelForPreTraining",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/vr_17548/src/transformers/models/auto/modeling_tf_auto.py#L403"}}),wL=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/pr_17548/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/pr_17548/en/model_doc/albert#transformers.AlbertConfig">AlbertConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/albert#transformers.TFAlbertForPreTraining">TFAlbertForPreTraining</a> (ALBERT model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/bart#transformers.BartConfig">BartConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/bart#transformers.TFBartForConditionalGeneration">TFBartForConditionalGeneration</a> (BART model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/bert#transformers.BertConfig">BertConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/bert#transformers.TFBertForPreTraining">TFBertForPreTraining</a> (BERT model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/ctrl#transformers.CTRLConfig">CTRLConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/ctrl#transformers.TFCTRLLMHeadModel">TFCTRLLMHeadModel</a> (CTRL model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/camembert#transformers.CamembertConfig">CamembertConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/camembert#transformers.TFCamembertForMaskedLM">TFCamembertForMaskedLM</a> (CamemBERT model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/distilbert#transformers.DistilBertConfig">DistilBertConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/distilbert#transformers.TFDistilBertForMaskedLM">TFDistilBertForMaskedLM</a> (DistilBERT model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/electra#transformers.ElectraConfig">ElectraConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/electra#transformers.TFElectraForPreTraining">TFElectraForPreTraining</a> (ELECTRA model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/flaubert#transformers.FlaubertConfig">FlaubertConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/flaubert#transformers.TFFlaubertWithLMHeadModel">TFFlaubertWithLMHeadModel</a> (FlauBERT model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/funnel#transformers.FunnelConfig">FunnelConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/funnel#transformers.TFFunnelForPreTraining">TFFunnelForPreTraining</a> (Funnel Transformer model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/gpt2#transformers.GPT2Config">GPT2Config</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/gpt2#transformers.TFGPT2LMHeadModel">TFGPT2LMHeadModel</a> (OpenAI GPT-2 model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/layoutlm#transformers.LayoutLMConfig">LayoutLMConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/layoutlm#transformers.TFLayoutLMForMaskedLM">TFLayoutLMForMaskedLM</a> (LayoutLM model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/lxmert#transformers.LxmertConfig">LxmertConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/lxmert#transformers.TFLxmertForPreTraining">TFLxmertForPreTraining</a> (LXMERT model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/mpnet#transformers.MPNetConfig">MPNetConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/mpnet#transformers.TFMPNetForMaskedLM">TFMPNetForMaskedLM</a> (MPNet model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/mobilebert#transformers.MobileBertConfig">MobileBertConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/mobilebert#transformers.TFMobileBertForPreTraining">TFMobileBertForPreTraining</a> (MobileBERT model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/openai-gpt#transformers.OpenAIGPTConfig">OpenAIGPTConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/openai-gpt#transformers.TFOpenAIGPTLMHeadModel">TFOpenAIGPTLMHeadModel</a> (OpenAI GPT model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/roberta#transformers.RobertaConfig">RobertaConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/roberta#transformers.TFRobertaForMaskedLM">TFRobertaForMaskedLM</a> (RoBERTa model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/t5#transformers.T5Config">T5Config</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/t5#transformers.TFT5ForConditionalGeneration">TFT5ForConditionalGeneration</a> (T5 model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/tapas#transformers.TapasConfig">TapasConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/tapas#transformers.TFTapasForMaskedLM">TFTapasForMaskedLM</a> (TAPAS model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/transfo-xl#transformers.TransfoXLConfig">TransfoXLConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/transfo-xl#transformers.TFTransfoXLLMHeadModel">TFTransfoXLLMHeadModel</a> (Transformer-XL model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/vit_mae#transformers.ViTMAEConfig">ViTMAEConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/vit_mae#transformers.TFViTMAEForPreTraining">TFViTMAEForPreTraining</a> (ViTMAE model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/xlm#transformers.XLMConfig">XLMConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/xlm#transformers.TFXLMWithLMHeadModel">TFXLMWithLMHeadModel</a> (XLM model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/xlm-roberta#transformers.XLMRobertaConfig">XLMRobertaConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/xlm-roberta#transformers.TFXLMRobertaForMaskedLM">TFXLMRobertaForMaskedLM</a> (XLM-RoBERTa model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/xlnet#transformers.XLNetConfig">XLNetConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/xlnet#transformers.TFXLNetLMHeadModel">TFXLNetLMHeadModel</a> (XLNet model)</li>
</ul>`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/vr_17548/src/transformers/models/auto/auto_factory.py#L389"}}),e8=new B({props:{anchor:"transformers.TFAutoModelForPreTraining.from_config.example",$$slots:{default:[lRt]},$$scope:{ctx:L}}}),AL=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/pr_17548/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/pr_17548/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/pr_17548/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/pr_17548/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/vr_17548/src/transformers/models/auto/auto_factory.py#L417"}}),C8=new B({props:{anchor:"transformers.TFAutoModelForPreTraining.from_pretrained.example",$$slots:{default:[iRt]},$$scope:{ctx:L}}}),yL=new te({}),LL=new R({props:{name:"class transformers.TFAutoModelForCausalLM",anchor:"transformers.TFAutoModelForCausalLM",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/vr_17548/src/transformers/models/auto/modeling_tf_auto.py#L418"}}),$L=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/pr_17548/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/pr_17548/en/model_doc/bert#transformers.BertConfig">BertConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/bert#transformers.TFBertLMHeadModel">TFBertLMHeadModel</a> (BERT model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/ctrl#transformers.CTRLConfig">CTRLConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/ctrl#transformers.TFCTRLLMHeadModel">TFCTRLLMHeadModel</a> (CTRL model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/camembert#transformers.CamembertConfig">CamembertConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/camembert#transformers.TFCamembertForCausalLM">TFCamembertForCausalLM</a> (CamemBERT model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/gpt2#transformers.GPT2Config">GPT2Config</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/gpt2#transformers.TFGPT2LMHeadModel">TFGPT2LMHeadModel</a> (OpenAI GPT-2 model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/gptj#transformers.GPTJConfig">GPTJConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/gptj#transformers.TFGPTJForCausalLM">TFGPTJForCausalLM</a> (GPT-J model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/opt#transformers.OPTConfig">OPTConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/opt#transformers.TFOPTForCausalLM">TFOPTForCausalLM</a> (OPT model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/openai-gpt#transformers.OpenAIGPTConfig">OpenAIGPTConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/openai-gpt#transformers.TFOpenAIGPTLMHeadModel">TFOpenAIGPTLMHeadModel</a> (OpenAI GPT model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/rembert#transformers.RemBertConfig">RemBertConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/rembert#transformers.TFRemBertForCausalLM">TFRemBertForCausalLM</a> (RemBERT model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/roformer#transformers.RoFormerConfig">RoFormerConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/roformer#transformers.TFRoFormerForCausalLM">TFRoFormerForCausalLM</a> (RoFormer model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/roberta#transformers.RobertaConfig">RobertaConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/roberta#transformers.TFRobertaForCausalLM">TFRobertaForCausalLM</a> (RoBERTa model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/transfo-xl#transformers.TransfoXLConfig">TransfoXLConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/transfo-xl#transformers.TFTransfoXLLMHeadModel">TFTransfoXLLMHeadModel</a> (Transformer-XL model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/xlm#transformers.XLMConfig">XLMConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/xlm#transformers.TFXLMWithLMHeadModel">TFXLMWithLMHeadModel</a> (XLM model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/xlnet#transformers.XLNetConfig">XLNetConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/xlnet#transformers.TFXLNetLMHeadModel">TFXLNetLMHeadModel</a> (XLNet model)</li>
</ul>`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/vr_17548/src/transformers/models/auto/auto_factory.py#L389"}}),A8=new B({props:{anchor:"transformers.TFAutoModelForCausalLM.from_config.example",$$slots:{default:[dRt]},$$scope:{ctx:L}}}),kL=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/pr_17548/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/pr_17548/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/pr_17548/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/pr_17548/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/vr_17548/src/transformers/models/auto/auto_factory.py#L417"}}),D8=new B({props:{anchor:"transformers.TFAutoModelForCausalLM.from_pretrained.example",$$slots:{default:[cRt]},$$scope:{ctx:L}}}),SL=new te({}),RL=new R({props:{name:"class transformers.TFAutoModelForImageClassification",anchor:"transformers.TFAutoModelForImageClassification",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/vr_17548/src/transformers/models/auto/modeling_tf_auto.py#L434"}}),BL=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/pr_17548/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/pr_17548/en/model_doc/convnext#transformers.ConvNextConfig">ConvNextConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/convnext#transformers.TFConvNextForImageClassification">TFConvNextForImageClassification</a> (ConvNeXT model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/data2vec#transformers.Data2VecVisionConfig">Data2VecVisionConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/data2vec#transformers.TFData2VecVisionForImageClassification">TFData2VecVisionForImageClassification</a> (Data2VecVision model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/swin#transformers.SwinConfig">SwinConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/swin#transformers.TFSwinForImageClassification">TFSwinForImageClassification</a> (Swin Transformer model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/vit#transformers.ViTConfig">ViTConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/vit#transformers.TFViTForImageClassification">TFViTForImageClassification</a> (ViT model)</li>
</ul>`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/vr_17548/src/transformers/models/auto/auto_factory.py#L389"}}),O8=new B({props:{anchor:"transformers.TFAutoModelForImageClassification.from_config.example",$$slots:{default:[fRt]},$$scope:{ctx:L}}}),IL=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/pr_17548/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/pr_17548/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/pr_17548/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/pr_17548/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/vr_17548/src/transformers/models/auto/auto_factory.py#L417"}}),Q8=new B({props:{anchor:"transformers.TFAutoModelForImageClassification.from_pretrained.example",$$slots:{default:[mRt]},$$scope:{ctx:L}}}),NL=new te({}),qL=new R({props:{name:"class transformers.TFAutoModelForMaskedLM",anchor:"transformers.TFAutoModelForMaskedLM",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/vr_17548/src/transformers/models/auto/modeling_tf_auto.py#L450"}}),DL=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/pr_17548/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/pr_17548/en/model_doc/albert#transformers.AlbertConfig">AlbertConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/albert#transformers.TFAlbertForMaskedLM">TFAlbertForMaskedLM</a> (ALBERT model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/bert#transformers.BertConfig">BertConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/bert#transformers.TFBertForMaskedLM">TFBertForMaskedLM</a> (BERT model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/camembert#transformers.CamembertConfig">CamembertConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/camembert#transformers.TFCamembertForMaskedLM">TFCamembertForMaskedLM</a> (CamemBERT model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/convbert#transformers.ConvBertConfig">ConvBertConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/convbert#transformers.TFConvBertForMaskedLM">TFConvBertForMaskedLM</a> (ConvBERT model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/deberta#transformers.DebertaConfig">DebertaConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/deberta#transformers.TFDebertaForMaskedLM">TFDebertaForMaskedLM</a> (DeBERTa model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/deberta-v2#transformers.DebertaV2Config">DebertaV2Config</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/deberta-v2#transformers.TFDebertaV2ForMaskedLM">TFDebertaV2ForMaskedLM</a> (DeBERTa-v2 model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/distilbert#transformers.DistilBertConfig">DistilBertConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/distilbert#transformers.TFDistilBertForMaskedLM">TFDistilBertForMaskedLM</a> (DistilBERT model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/electra#transformers.ElectraConfig">ElectraConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/electra#transformers.TFElectraForMaskedLM">TFElectraForMaskedLM</a> (ELECTRA model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/flaubert#transformers.FlaubertConfig">FlaubertConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/flaubert#transformers.TFFlaubertWithLMHeadModel">TFFlaubertWithLMHeadModel</a> (FlauBERT model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/funnel#transformers.FunnelConfig">FunnelConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/funnel#transformers.TFFunnelForMaskedLM">TFFunnelForMaskedLM</a> (Funnel Transformer model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/layoutlm#transformers.LayoutLMConfig">LayoutLMConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/layoutlm#transformers.TFLayoutLMForMaskedLM">TFLayoutLMForMaskedLM</a> (LayoutLM model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/longformer#transformers.LongformerConfig">LongformerConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/longformer#transformers.TFLongformerForMaskedLM">TFLongformerForMaskedLM</a> (Longformer model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/mpnet#transformers.MPNetConfig">MPNetConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/mpnet#transformers.TFMPNetForMaskedLM">TFMPNetForMaskedLM</a> (MPNet model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/mobilebert#transformers.MobileBertConfig">MobileBertConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/mobilebert#transformers.TFMobileBertForMaskedLM">TFMobileBertForMaskedLM</a> (MobileBERT model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/rembert#transformers.RemBertConfig">RemBertConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/rembert#transformers.TFRemBertForMaskedLM">TFRemBertForMaskedLM</a> (RemBERT model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/roformer#transformers.RoFormerConfig">RoFormerConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/roformer#transformers.TFRoFormerForMaskedLM">TFRoFormerForMaskedLM</a> (RoFormer model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/roberta#transformers.RobertaConfig">RobertaConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/roberta#transformers.TFRobertaForMaskedLM">TFRobertaForMaskedLM</a> (RoBERTa model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/tapas#transformers.TapasConfig">TapasConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/tapas#transformers.TFTapasForMaskedLM">TFTapasForMaskedLM</a> (TAPAS model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/xlm#transformers.XLMConfig">XLMConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/xlm#transformers.TFXLMWithLMHeadModel">TFXLMWithLMHeadModel</a> (XLM model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/xlm-roberta#transformers.XLMRobertaConfig">XLMRobertaConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/xlm-roberta#transformers.TFXLMRobertaForMaskedLM">TFXLMRobertaForMaskedLM</a> (XLM-RoBERTa model)</li>
</ul>`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/vr_17548/src/transformers/models/auto/auto_factory.py#L389"}}),U8=new B({props:{anchor:"transformers.TFAutoModelForMaskedLM.from_config.example",$$slots:{default:[gRt]},$$scope:{ctx:L}}}),GL=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/pr_17548/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/pr_17548/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/pr_17548/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/pr_17548/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/vr_17548/src/transformers/models/auto/auto_factory.py#L417"}}),_7=new B({props:{anchor:"transformers.TFAutoModelForMaskedLM.from_pretrained.example",$$slots:{default:[hRt]},$$scope:{ctx:L}}}),OL=new te({}),VL=new R({props:{name:"class transformers.TFAutoModelForSeq2SeqLM",anchor:"transformers.TFAutoModelForSeq2SeqLM",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/vr_17548/src/transformers/models/auto/modeling_tf_auto.py#L457"}}),zL=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/pr_17548/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/pr_17548/en/model_doc/bart#transformers.BartConfig">BartConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/bart#transformers.TFBartForConditionalGeneration">TFBartForConditionalGeneration</a> (BART model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/blenderbot#transformers.BlenderbotConfig">BlenderbotConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/blenderbot#transformers.TFBlenderbotForConditionalGeneration">TFBlenderbotForConditionalGeneration</a> (Blenderbot model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/blenderbot-small#transformers.BlenderbotSmallConfig">BlenderbotSmallConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/blenderbot-small#transformers.TFBlenderbotSmallForConditionalGeneration">TFBlenderbotSmallForConditionalGeneration</a> (BlenderbotSmall model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/encoder-decoder#transformers.EncoderDecoderConfig">EncoderDecoderConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/encoder-decoder#transformers.TFEncoderDecoderModel">TFEncoderDecoderModel</a> (Encoder decoder model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/led#transformers.LEDConfig">LEDConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/led#transformers.TFLEDForConditionalGeneration">TFLEDForConditionalGeneration</a> (LED model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/mbart#transformers.MBartConfig">MBartConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/mbart#transformers.TFMBartForConditionalGeneration">TFMBartForConditionalGeneration</a> (mBART model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/mt5#transformers.MT5Config">MT5Config</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/mt5#transformers.TFMT5ForConditionalGeneration">TFMT5ForConditionalGeneration</a> (MT5 model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/marian#transformers.MarianConfig">MarianConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/marian#transformers.TFMarianMTModel">TFMarianMTModel</a> (Marian model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/pegasus#transformers.PegasusConfig">PegasusConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/pegasus#transformers.TFPegasusForConditionalGeneration">TFPegasusForConditionalGeneration</a> (Pegasus model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/t5#transformers.T5Config">T5Config</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/t5#transformers.TFT5ForConditionalGeneration">TFT5ForConditionalGeneration</a> (T5 model)</li>
</ul>`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/vr_17548/src/transformers/models/auto/auto_factory.py#L389"}}),b7=new B({props:{anchor:"transformers.TFAutoModelForSeq2SeqLM.from_config.example",$$slots:{default:[pRt]},$$scope:{ctx:L}}}),WL=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/pr_17548/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/pr_17548/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/pr_17548/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/pr_17548/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/vr_17548/src/transformers/models/auto/auto_factory.py#L417"}}),x7=new B({props:{anchor:"transformers.TFAutoModelForSeq2SeqLM.from_pretrained.example",$$slots:{default:[_Rt]},$$scope:{ctx:L}}}),QL=new te({}),HL=new R({props:{name:"class transformers.TFAutoModelForSequenceClassification",anchor:"transformers.TFAutoModelForSequenceClassification",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/vr_17548/src/transformers/models/auto/modeling_tf_auto.py#L466"}}),JL=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/pr_17548/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/pr_17548/en/model_doc/albert#transformers.AlbertConfig">AlbertConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/albert#transformers.TFAlbertForSequenceClassification">TFAlbertForSequenceClassification</a> (ALBERT model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/bert#transformers.BertConfig">BertConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/bert#transformers.TFBertForSequenceClassification">TFBertForSequenceClassification</a> (BERT model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/ctrl#transformers.CTRLConfig">CTRLConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/ctrl#transformers.TFCTRLForSequenceClassification">TFCTRLForSequenceClassification</a> (CTRL model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/camembert#transformers.CamembertConfig">CamembertConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/camembert#transformers.TFCamembertForSequenceClassification">TFCamembertForSequenceClassification</a> (CamemBERT model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/convbert#transformers.ConvBertConfig">ConvBertConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/convbert#transformers.TFConvBertForSequenceClassification">TFConvBertForSequenceClassification</a> (ConvBERT model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/deberta#transformers.DebertaConfig">DebertaConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/deberta#transformers.TFDebertaForSequenceClassification">TFDebertaForSequenceClassification</a> (DeBERTa model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/deberta-v2#transformers.DebertaV2Config">DebertaV2Config</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/deberta-v2#transformers.TFDebertaV2ForSequenceClassification">TFDebertaV2ForSequenceClassification</a> (DeBERTa-v2 model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/distilbert#transformers.DistilBertConfig">DistilBertConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/distilbert#transformers.TFDistilBertForSequenceClassification">TFDistilBertForSequenceClassification</a> (DistilBERT model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/electra#transformers.ElectraConfig">ElectraConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/electra#transformers.TFElectraForSequenceClassification">TFElectraForSequenceClassification</a> (ELECTRA model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/flaubert#transformers.FlaubertConfig">FlaubertConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/flaubert#transformers.TFFlaubertForSequenceClassification">TFFlaubertForSequenceClassification</a> (FlauBERT model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/funnel#transformers.FunnelConfig">FunnelConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/funnel#transformers.TFFunnelForSequenceClassification">TFFunnelForSequenceClassification</a> (Funnel Transformer model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/gpt2#transformers.GPT2Config">GPT2Config</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/gpt2#transformers.TFGPT2ForSequenceClassification">TFGPT2ForSequenceClassification</a> (OpenAI GPT-2 model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/gptj#transformers.GPTJConfig">GPTJConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/gptj#transformers.TFGPTJForSequenceClassification">TFGPTJForSequenceClassification</a> (GPT-J model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/layoutlm#transformers.LayoutLMConfig">LayoutLMConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/layoutlm#transformers.TFLayoutLMForSequenceClassification">TFLayoutLMForSequenceClassification</a> (LayoutLM model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/longformer#transformers.LongformerConfig">LongformerConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/longformer#transformers.TFLongformerForSequenceClassification">TFLongformerForSequenceClassification</a> (Longformer model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/mpnet#transformers.MPNetConfig">MPNetConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/mpnet#transformers.TFMPNetForSequenceClassification">TFMPNetForSequenceClassification</a> (MPNet model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/mobilebert#transformers.MobileBertConfig">MobileBertConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/mobilebert#transformers.TFMobileBertForSequenceClassification">TFMobileBertForSequenceClassification</a> (MobileBERT model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/openai-gpt#transformers.OpenAIGPTConfig">OpenAIGPTConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/openai-gpt#transformers.TFOpenAIGPTForSequenceClassification">TFOpenAIGPTForSequenceClassification</a> (OpenAI GPT model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/rembert#transformers.RemBertConfig">RemBertConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/rembert#transformers.TFRemBertForSequenceClassification">TFRemBertForSequenceClassification</a> (RemBERT model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/roformer#transformers.RoFormerConfig">RoFormerConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/roformer#transformers.TFRoFormerForSequenceClassification">TFRoFormerForSequenceClassification</a> (RoFormer model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/roberta#transformers.RobertaConfig">RobertaConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/roberta#transformers.TFRobertaForSequenceClassification">TFRobertaForSequenceClassification</a> (RoBERTa model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/tapas#transformers.TapasConfig">TapasConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/tapas#transformers.TFTapasForSequenceClassification">TFTapasForSequenceClassification</a> (TAPAS model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/transfo-xl#transformers.TransfoXLConfig">TransfoXLConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/transfo-xl#transformers.TFTransfoXLForSequenceClassification">TFTransfoXLForSequenceClassification</a> (Transformer-XL model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/xlm#transformers.XLMConfig">XLMConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/xlm#transformers.TFXLMForSequenceClassification">TFXLMForSequenceClassification</a> (XLM model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/xlm-roberta#transformers.XLMRobertaConfig">XLMRobertaConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/xlm-roberta#transformers.TFXLMRobertaForSequenceClassification">TFXLMRobertaForSequenceClassification</a> (XLM-RoBERTa model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/xlnet#transformers.XLNetConfig">XLNetConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/xlnet#transformers.TFXLNetForSequenceClassification">TFXLNetForSequenceClassification</a> (XLNet model)</li>
</ul>`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/vr_17548/src/transformers/models/auto/auto_factory.py#L389"}}),k7=new B({props:{anchor:"transformers.TFAutoModelForSequenceClassification.from_config.example",$$slots:{default:[uRt]},$$scope:{ctx:L}}}),YL=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/pr_17548/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/pr_17548/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/pr_17548/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/pr_17548/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/vr_17548/src/transformers/models/auto/auto_factory.py#L417"}}),aM=new B({props:{anchor:"transformers.TFAutoModelForSequenceClassification.from_pretrained.example",$$slots:{default:[bRt]},$$scope:{ctx:L}}}),KL=new te({}),ZL=new R({props:{name:"class transformers.TFAutoModelForMultipleChoice",anchor:"transformers.TFAutoModelForMultipleChoice",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/vr_17548/src/transformers/models/auto/modeling_tf_auto.py#L502"}}),o9=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/pr_17548/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/pr_17548/en/model_doc/albert#transformers.AlbertConfig">AlbertConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/albert#transformers.TFAlbertForMultipleChoice">TFAlbertForMultipleChoice</a> (ALBERT model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/bert#transformers.BertConfig">BertConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/bert#transformers.TFBertForMultipleChoice">TFBertForMultipleChoice</a> (BERT model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/camembert#transformers.CamembertConfig">CamembertConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/camembert#transformers.TFCamembertForMultipleChoice">TFCamembertForMultipleChoice</a> (CamemBERT model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/convbert#transformers.ConvBertConfig">ConvBertConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/convbert#transformers.TFConvBertForMultipleChoice">TFConvBertForMultipleChoice</a> (ConvBERT model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/distilbert#transformers.DistilBertConfig">DistilBertConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/distilbert#transformers.TFDistilBertForMultipleChoice">TFDistilBertForMultipleChoice</a> (DistilBERT model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/electra#transformers.ElectraConfig">ElectraConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/electra#transformers.TFElectraForMultipleChoice">TFElectraForMultipleChoice</a> (ELECTRA model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/flaubert#transformers.FlaubertConfig">FlaubertConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/flaubert#transformers.TFFlaubertForMultipleChoice">TFFlaubertForMultipleChoice</a> (FlauBERT model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/funnel#transformers.FunnelConfig">FunnelConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/funnel#transformers.TFFunnelForMultipleChoice">TFFunnelForMultipleChoice</a> (Funnel Transformer model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/longformer#transformers.LongformerConfig">LongformerConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/longformer#transformers.TFLongformerForMultipleChoice">TFLongformerForMultipleChoice</a> (Longformer model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/mpnet#transformers.MPNetConfig">MPNetConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/mpnet#transformers.TFMPNetForMultipleChoice">TFMPNetForMultipleChoice</a> (MPNet model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/mobilebert#transformers.MobileBertConfig">MobileBertConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/mobilebert#transformers.TFMobileBertForMultipleChoice">TFMobileBertForMultipleChoice</a> (MobileBERT model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/rembert#transformers.RemBertConfig">RemBertConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/rembert#transformers.TFRemBertForMultipleChoice">TFRemBertForMultipleChoice</a> (RemBERT model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/roformer#transformers.RoFormerConfig">RoFormerConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/roformer#transformers.TFRoFormerForMultipleChoice">TFRoFormerForMultipleChoice</a> (RoFormer model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/roberta#transformers.RobertaConfig">RobertaConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/roberta#transformers.TFRobertaForMultipleChoice">TFRobertaForMultipleChoice</a> (RoBERTa model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/xlm#transformers.XLMConfig">XLMConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/xlm#transformers.TFXLMForMultipleChoice">TFXLMForMultipleChoice</a> (XLM model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/xlm-roberta#transformers.XLMRobertaConfig">XLMRobertaConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/xlm-roberta#transformers.TFXLMRobertaForMultipleChoice">TFXLMRobertaForMultipleChoice</a> (XLM-RoBERTa model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/xlnet#transformers.XLNetConfig">XLNetConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/xlnet#transformers.TFXLNetForMultipleChoice">TFXLNetForMultipleChoice</a> (XLNet model)</li>
</ul>`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/vr_17548/src/transformers/models/auto/auto_factory.py#L389"}}),sM=new B({props:{anchor:"transformers.TFAutoModelForMultipleChoice.from_config.example",$$slots:{default:[vRt]},$$scope:{ctx:L}}}),r9=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/pr_17548/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/pr_17548/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/pr_17548/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/pr_17548/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/vr_17548/src/transformers/models/auto/auto_factory.py#L417"}}),CM=new B({props:{anchor:"transformers.TFAutoModelForMultipleChoice.from_pretrained.example",$$slots:{default:[FRt]},$$scope:{ctx:L}}}),t9=new te({}),a9=new R({props:{name:"class transformers.TFAutoModelForNextSentencePrediction",anchor:"transformers.TFAutoModelForNextSentencePrediction",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/vr_17548/src/transformers/models/auto/modeling_tf_auto.py#L509"}}),s9=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/pr_17548/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/pr_17548/en/model_doc/bert#transformers.BertConfig">BertConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/bert#transformers.TFBertForNextSentencePrediction">TFBertForNextSentencePrediction</a> (BERT model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/mobilebert#transformers.MobileBertConfig">MobileBertConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/mobilebert#transformers.TFMobileBertForNextSentencePrediction">TFMobileBertForNextSentencePrediction</a> (MobileBERT model)</li>
</ul>`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/vr_17548/src/transformers/models/auto/auto_factory.py#L389"}}),AM=new B({props:{anchor:"transformers.TFAutoModelForNextSentencePrediction.from_config.example",$$slots:{default:[TRt]},$$scope:{ctx:L}}}),l9=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/pr_17548/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/pr_17548/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/pr_17548/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/pr_17548/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/vr_17548/src/transformers/models/auto/auto_factory.py#L417"}}),xM=new B({props:{anchor:"transformers.TFAutoModelForNextSentencePrediction.from_pretrained.example",$$slots:{default:[MRt]},$$scope:{ctx:L}}}),d9=new te({}),c9=new R({props:{name:"class transformers.TFAutoModelForTableQuestionAnswering",anchor:"transformers.TFAutoModelForTableQuestionAnswering",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/vr_17548/src/transformers/models/auto/modeling_tf_auto.py#L482"}}),m9=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/pr_17548/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/pr_17548/en/model_doc/tapas#transformers.TapasConfig">TapasConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/tapas#transformers.TFTapasForQuestionAnswering">TFTapasForQuestionAnswering</a> (TAPAS model)</li>
</ul>`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/vr_17548/src/transformers/models/auto/auto_factory.py#L389"}}),kM=new B({props:{anchor:"transformers.TFAutoModelForTableQuestionAnswering.from_config.example",$$slots:{default:[ERt]},$$scope:{ctx:L}}}),g9=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/pr_17548/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/pr_17548/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/pr_17548/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/pr_17548/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/vr_17548/src/transformers/models/auto/auto_factory.py#L417"}}),RM=new B({props:{anchor:"transformers.TFAutoModelForTableQuestionAnswering.from_pretrained.example",$$slots:{default:[CRt]},$$scope:{ctx:L}}}),h9=new te({}),p9=new R({props:{name:"class transformers.TFAutoModelForTokenClassification",anchor:"transformers.TFAutoModelForTokenClassification",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/vr_17548/src/transformers/models/auto/modeling_tf_auto.py#L493"}}),u9=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/pr_17548/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/pr_17548/en/model_doc/albert#transformers.AlbertConfig">AlbertConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/albert#transformers.TFAlbertForTokenClassification">TFAlbertForTokenClassification</a> (ALBERT model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/bert#transformers.BertConfig">BertConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/bert#transformers.TFBertForTokenClassification">TFBertForTokenClassification</a> (BERT model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/camembert#transformers.CamembertConfig">CamembertConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/camembert#transformers.TFCamembertForTokenClassification">TFCamembertForTokenClassification</a> (CamemBERT model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/convbert#transformers.ConvBertConfig">ConvBertConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/convbert#transformers.TFConvBertForTokenClassification">TFConvBertForTokenClassification</a> (ConvBERT model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/deberta#transformers.DebertaConfig">DebertaConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/deberta#transformers.TFDebertaForTokenClassification">TFDebertaForTokenClassification</a> (DeBERTa model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/deberta-v2#transformers.DebertaV2Config">DebertaV2Config</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/deberta-v2#transformers.TFDebertaV2ForTokenClassification">TFDebertaV2ForTokenClassification</a> (DeBERTa-v2 model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/distilbert#transformers.DistilBertConfig">DistilBertConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/distilbert#transformers.TFDistilBertForTokenClassification">TFDistilBertForTokenClassification</a> (DistilBERT model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/electra#transformers.ElectraConfig">ElectraConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/electra#transformers.TFElectraForTokenClassification">TFElectraForTokenClassification</a> (ELECTRA model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/flaubert#transformers.FlaubertConfig">FlaubertConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/flaubert#transformers.TFFlaubertForTokenClassification">TFFlaubertForTokenClassification</a> (FlauBERT model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/funnel#transformers.FunnelConfig">FunnelConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/funnel#transformers.TFFunnelForTokenClassification">TFFunnelForTokenClassification</a> (Funnel Transformer model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/layoutlm#transformers.LayoutLMConfig">LayoutLMConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/layoutlm#transformers.TFLayoutLMForTokenClassification">TFLayoutLMForTokenClassification</a> (LayoutLM model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/longformer#transformers.LongformerConfig">LongformerConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/longformer#transformers.TFLongformerForTokenClassification">TFLongformerForTokenClassification</a> (Longformer model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/mpnet#transformers.MPNetConfig">MPNetConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/mpnet#transformers.TFMPNetForTokenClassification">TFMPNetForTokenClassification</a> (MPNet model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/mobilebert#transformers.MobileBertConfig">MobileBertConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/mobilebert#transformers.TFMobileBertForTokenClassification">TFMobileBertForTokenClassification</a> (MobileBERT model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/rembert#transformers.RemBertConfig">RemBertConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/rembert#transformers.TFRemBertForTokenClassification">TFRemBertForTokenClassification</a> (RemBERT model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/roformer#transformers.RoFormerConfig">RoFormerConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/roformer#transformers.TFRoFormerForTokenClassification">TFRoFormerForTokenClassification</a> (RoFormer model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/roberta#transformers.RobertaConfig">RobertaConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/roberta#transformers.TFRobertaForTokenClassification">TFRobertaForTokenClassification</a> (RoBERTa model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/xlm#transformers.XLMConfig">XLMConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/xlm#transformers.TFXLMForTokenClassification">TFXLMForTokenClassification</a> (XLM model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/xlm-roberta#transformers.XLMRobertaConfig">XLMRobertaConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/xlm-roberta#transformers.TFXLMRobertaForTokenClassification">TFXLMRobertaForTokenClassification</a> (XLM-RoBERTa model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/xlnet#transformers.XLNetConfig">XLNetConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/xlnet#transformers.TFXLNetForTokenClassification">TFXLNetForTokenClassification</a> (XLNet model)</li>
</ul>`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/vr_17548/src/transformers/models/auto/auto_factory.py#L389"}}),BM=new B({props:{anchor:"transformers.TFAutoModelForTokenClassification.from_config.example",$$slots:{default:[wRt]},$$scope:{ctx:L}}}),b9=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/pr_17548/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/pr_17548/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/pr_17548/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/pr_17548/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/vr_17548/src/transformers/models/auto/auto_factory.py#L417"}}),rE=new B({props:{anchor:"transformers.TFAutoModelForTokenClassification.from_pretrained.example",$$slots:{default:[ARt]},$$scope:{ctx:L}}}),v9=new te({}),F9=new R({props:{name:"class transformers.TFAutoModelForQuestionAnswering",anchor:"transformers.TFAutoModelForQuestionAnswering",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/vr_17548/src/transformers/models/auto/modeling_tf_auto.py#L475"}}),M9=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/pr_17548/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/pr_17548/en/model_doc/albert#transformers.AlbertConfig">AlbertConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/albert#transformers.TFAlbertForQuestionAnswering">TFAlbertForQuestionAnswering</a> (ALBERT model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/bert#transformers.BertConfig">BertConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/bert#transformers.TFBertForQuestionAnswering">TFBertForQuestionAnswering</a> (BERT model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/camembert#transformers.CamembertConfig">CamembertConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/camembert#transformers.TFCamembertForQuestionAnswering">TFCamembertForQuestionAnswering</a> (CamemBERT model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/convbert#transformers.ConvBertConfig">ConvBertConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/convbert#transformers.TFConvBertForQuestionAnswering">TFConvBertForQuestionAnswering</a> (ConvBERT model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/deberta#transformers.DebertaConfig">DebertaConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/deberta#transformers.TFDebertaForQuestionAnswering">TFDebertaForQuestionAnswering</a> (DeBERTa model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/deberta-v2#transformers.DebertaV2Config">DebertaV2Config</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/deberta-v2#transformers.TFDebertaV2ForQuestionAnswering">TFDebertaV2ForQuestionAnswering</a> (DeBERTa-v2 model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/distilbert#transformers.DistilBertConfig">DistilBertConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/distilbert#transformers.TFDistilBertForQuestionAnswering">TFDistilBertForQuestionAnswering</a> (DistilBERT model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/electra#transformers.ElectraConfig">ElectraConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/electra#transformers.TFElectraForQuestionAnswering">TFElectraForQuestionAnswering</a> (ELECTRA model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/flaubert#transformers.FlaubertConfig">FlaubertConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/flaubert#transformers.TFFlaubertForQuestionAnsweringSimple">TFFlaubertForQuestionAnsweringSimple</a> (FlauBERT model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/funnel#transformers.FunnelConfig">FunnelConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/funnel#transformers.TFFunnelForQuestionAnswering">TFFunnelForQuestionAnswering</a> (Funnel Transformer model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/gptj#transformers.GPTJConfig">GPTJConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/gptj#transformers.TFGPTJForQuestionAnswering">TFGPTJForQuestionAnswering</a> (GPT-J model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/longformer#transformers.LongformerConfig">LongformerConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/longformer#transformers.TFLongformerForQuestionAnswering">TFLongformerForQuestionAnswering</a> (Longformer model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/mpnet#transformers.MPNetConfig">MPNetConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/mpnet#transformers.TFMPNetForQuestionAnswering">TFMPNetForQuestionAnswering</a> (MPNet model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/mobilebert#transformers.MobileBertConfig">MobileBertConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/mobilebert#transformers.TFMobileBertForQuestionAnswering">TFMobileBertForQuestionAnswering</a> (MobileBERT model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/rembert#transformers.RemBertConfig">RemBertConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/rembert#transformers.TFRemBertForQuestionAnswering">TFRemBertForQuestionAnswering</a> (RemBERT model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/roformer#transformers.RoFormerConfig">RoFormerConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/roformer#transformers.TFRoFormerForQuestionAnswering">TFRoFormerForQuestionAnswering</a> (RoFormer model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/roberta#transformers.RobertaConfig">RobertaConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/roberta#transformers.TFRobertaForQuestionAnswering">TFRobertaForQuestionAnswering</a> (RoBERTa model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/xlm#transformers.XLMConfig">XLMConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/xlm#transformers.TFXLMForQuestionAnsweringSimple">TFXLMForQuestionAnsweringSimple</a> (XLM model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/xlm-roberta#transformers.XLMRobertaConfig">XLMRobertaConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/xlm-roberta#transformers.TFXLMRobertaForQuestionAnswering">TFXLMRobertaForQuestionAnswering</a> (XLM-RoBERTa model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/xlnet#transformers.XLNetConfig">XLNetConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/xlnet#transformers.TFXLNetForQuestionAnsweringSimple">TFXLNetForQuestionAnsweringSimple</a> (XLNet model)</li>
</ul>`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/vr_17548/src/transformers/models/auto/auto_factory.py#L389"}}),aE=new B({props:{anchor:"transformers.TFAutoModelForQuestionAnswering.from_config.example",$$slots:{default:[yRt]},$$scope:{ctx:L}}}),E9=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/pr_17548/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/pr_17548/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/pr_17548/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/pr_17548/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/vr_17548/src/transformers/models/auto/auto_factory.py#L417"}}),wE=new B({props:{anchor:"transformers.TFAutoModelForQuestionAnswering.from_pretrained.example",$$slots:{default:[LRt]},$$scope:{ctx:L}}}),C9=new te({}),w9=new R({props:{name:"class transformers.TFAutoModelForVision2Seq",anchor:"transformers.TFAutoModelForVision2Seq",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/vr_17548/src/transformers/models/auto/modeling_tf_auto.py#L443"}}),y9=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/pr_17548/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/pr_17548/en/model_doc/vision-encoder-decoder#transformers.VisionEncoderDecoderConfig">VisionEncoderDecoderConfig</a> configuration class: <a href="/docs/transformers/pr_17548/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/vr_17548/src/transformers/models/auto/auto_factory.py#L389"}}),yE=new B({props:{anchor:"transformers.TFAutoModelForVision2Seq.from_config.example",$$slots:{default:[xRt]},$$scope:{ctx:L}}}),L9=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
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/pr_17548/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a>, e.g., <code>./my_model_directory/</code>.</li>
<li>A path or url to a <em>PyTorch state_dict save file</em> (e.g, <code>./pt_model/pytorch_model.bin</code>). In this
case, <code>from_pt</code> should be set to <code>True</code> and a configuration object should be provided as <code>config</code>
argument. This loading path is slower than converting the PyTorch model in a TensorFlow model
using the provided conversion scripts and loading the TensorFlow model afterwards.</li>
</ul>`,name:"pretrained_model_name_or_path"},{anchor:"transformers.TFAutoModelForVision2Seq.from_pretrained.model_args",description:`<strong>model_args</strong> (additional positional arguments, <em>optional</em>) &#x2014;
Will be passed along to the underlying model <code>__init__()</code> method.`,name:"model_args"},{anchor:"transformers.TFAutoModelForVision2Seq.from_pretrained.config",description:`<strong>config</strong> (<a href="/docs/transformers/pr_17548/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/pr_17548/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and is reloaded by supplying the
save directory.</li>
<li>The model is loaded by supplying a local directory as <code>pretrained_model_name_or_path</code> and a
configuration JSON file named <em>config.json</em> is found in the directory.</li>
</ul>`,name:"config"},{anchor:"transformers.TFAutoModelForVision2Seq.from_pretrained.cache_dir",description:`<strong>cache_dir</strong> (<code>str</code> or <code>os.PathLike</code>, <em>optional</em>) &#x2014;
Path to a directory in which a downloaded pretrained model configuration should be cached if the
standard cache should not be used.`,name:"cache_dir"},{anchor:"transformers.TFAutoModelForVision2Seq.from_pretrained.from_pt",description:`<strong>from_pt</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Load the model weights from a PyTorch checkpoint save file (see docstring of
<code>pretrained_model_name_or_path</code> argument).`,name:"from_pt"},{anchor:"transformers.TFAutoModelForVision2Seq.from_pretrained.force_download",description:`<strong>force_download</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to force the (re-)download of the model weights and configuration files, overriding the
cached versions if they exist.`,name:"force_download"},{anchor:"transformers.TFAutoModelForVision2Seq.from_pretrained.resume_download",description:`<strong>resume_download</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to delete incompletely received files. Will attempt to resume the download if such a
file exists.`,name:"resume_download"},{anchor:"transformers.TFAutoModelForVision2Seq.from_pretrained.proxies",description:`<strong>proxies</strong> (<code>Dict[str, str]</code>, <em>optional</em>) &#x2014;
A dictionary of proxy servers to use by protocol or endpoint, e.g., <code>{&apos;http&apos;: &apos;foo.bar:3128&apos;, &apos;http://hostname&apos;: &apos;foo.bar:4012&apos;}</code>. The proxies are used on each request.`,name:"proxies"},{anchor:"transformers.TFAutoModelForVision2Seq.from_pretrained.output_loading_info(bool,",description:`<strong>output_loading_info(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether ot not to also return a dictionary containing missing keys, unexpected keys and error messages.`,name:"output_loading_info(bool,"},{anchor:"transformers.TFAutoModelForVision2Seq.from_pretrained.local_files_only(bool,",description:`<strong>local_files_only(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to only look at local files (e.g., not try downloading the model).`,name:"local_files_only(bool,"},{anchor:"transformers.TFAutoModelForVision2Seq.from_pretrained.revision",description:`<strong>revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a
git-based system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any
identifier allowed by git.`,name:"revision"},{anchor:"transformers.TFAutoModelForVision2Seq.from_pretrained.trust_remote_code",description:`<strong>trust_remote_code</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to allow for custom models defined on the Hub in their own modeling files. This option
should only be set to <code>True</code> for repositories you trust and in which you have read the code, as it will
execute code present on the Hub on your local machine.`,name:"trust_remote_code"},{anchor:"transformers.TFAutoModelForVision2Seq.from_pretrained.kwargs",description:`<strong>kwargs</strong> (additional 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/pr_17548/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/vr_17548/src/transformers/models/auto/auto_factory.py#L417"}}),xE=new B({props:{anchor:"transformers.TFAutoModelForVision2Seq.from_pretrained.example",$$slots:{default:[$Rt]},$$scope:{ctx:L}}}),x9=new te({}),$9=new R({props:{name:"class transformers.TFAutoModelForSpeechSeq2Seq",anchor:"transformers.TFAutoModelForSpeechSeq2Seq",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/vr_17548/src/transformers/models/auto/modeling_tf_auto.py#L518"}}),S9=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/pr_17548/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/pr_17548/en/model_doc/speech_to_text#transformers.Speech2TextConfig">Speech2TextConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/speech_to_text#transformers.TFSpeech2TextForConditionalGeneration">TFSpeech2TextForConditionalGeneration</a> (Speech2Text model)</li>
</ul>`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/vr_17548/src/transformers/models/auto/auto_factory.py#L389"}}),kE=new B({props:{anchor:"transformers.TFAutoModelForSpeechSeq2Seq.from_config.example",$$slots:{default:[kRt]},$$scope:{ctx:L}}}),R9=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/pr_17548/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/pr_17548/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/pr_17548/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/pr_17548/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/vr_17548/src/transformers/models/auto/auto_factory.py#L417"}}),RE=new B({props:{anchor:"transformers.TFAutoModelForSpeechSeq2Seq.from_pretrained.example",$$slots:{default:[SRt]},$$scope:{ctx:L}}}),P9=new te({}),B9=new R({props:{name:"class transformers.FlaxAutoModel",anchor:"transformers.FlaxAutoModel",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/vr_17548/src/transformers/models/auto/modeling_flax_auto.py#L243"}}),N9=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/pr_17548/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/pr_17548/en/model_doc/albert#transformers.AlbertConfig">AlbertConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/albert#transformers.FlaxAlbertModel">FlaxAlbertModel</a> (ALBERT model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/bart#transformers.BartConfig">BartConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/bart#transformers.FlaxBartModel">FlaxBartModel</a> (BART model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/beit#transformers.BeitConfig">BeitConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/beit#transformers.FlaxBeitModel">FlaxBeitModel</a> (BEiT model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/bert#transformers.BertConfig">BertConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/bert#transformers.FlaxBertModel">FlaxBertModel</a> (BERT model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/big_bird#transformers.BigBirdConfig">BigBirdConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/big_bird#transformers.FlaxBigBirdModel">FlaxBigBirdModel</a> (BigBird model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/blenderbot#transformers.BlenderbotConfig">BlenderbotConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/blenderbot#transformers.FlaxBlenderbotModel">FlaxBlenderbotModel</a> (Blenderbot model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/blenderbot-small#transformers.BlenderbotSmallConfig">BlenderbotSmallConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/blenderbot-small#transformers.FlaxBlenderbotSmallModel">FlaxBlenderbotSmallModel</a> (BlenderbotSmall model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/clip#transformers.CLIPConfig">CLIPConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/clip#transformers.FlaxCLIPModel">FlaxCLIPModel</a> (CLIP model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/distilbert#transformers.DistilBertConfig">DistilBertConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/distilbert#transformers.FlaxDistilBertModel">FlaxDistilBertModel</a> (DistilBERT model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/electra#transformers.ElectraConfig">ElectraConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/electra#transformers.FlaxElectraModel">FlaxElectraModel</a> (ELECTRA model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/gpt2#transformers.GPT2Config">GPT2Config</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/gpt2#transformers.FlaxGPT2Model">FlaxGPT2Model</a> (OpenAI GPT-2 model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/gptj#transformers.GPTJConfig">GPTJConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/gptj#transformers.FlaxGPTJModel">FlaxGPTJModel</a> (GPT-J model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/gpt_neo#transformers.GPTNeoConfig">GPTNeoConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/gpt_neo#transformers.FlaxGPTNeoModel">FlaxGPTNeoModel</a> (GPT Neo model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/mbart#transformers.MBartConfig">MBartConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/mbart#transformers.FlaxMBartModel">FlaxMBartModel</a> (mBART model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/mt5#transformers.MT5Config">MT5Config</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/mt5#transformers.FlaxMT5Model">FlaxMT5Model</a> (MT5 model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/marian#transformers.MarianConfig">MarianConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/marian#transformers.FlaxMarianModel">FlaxMarianModel</a> (Marian model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/opt#transformers.OPTConfig">OPTConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/opt#transformers.FlaxOPTModel">FlaxOPTModel</a> (OPT model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/pegasus#transformers.PegasusConfig">PegasusConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/pegasus#transformers.FlaxPegasusModel">FlaxPegasusModel</a> (Pegasus model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/roformer#transformers.RoFormerConfig">RoFormerConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/roformer#transformers.FlaxRoFormerModel">FlaxRoFormerModel</a> (RoFormer model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/roberta#transformers.RobertaConfig">RobertaConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/roberta#transformers.FlaxRobertaModel">FlaxRobertaModel</a> (RoBERTa model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/t5#transformers.T5Config">T5Config</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/t5#transformers.FlaxT5Model">FlaxT5Model</a> (T5 model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/vit#transformers.ViTConfig">ViTConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/vit#transformers.FlaxViTModel">FlaxViTModel</a> (ViT model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/vision-text-dual-encoder#transformers.VisionTextDualEncoderConfig">VisionTextDualEncoderConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/vision-text-dual-encoder#transformers.FlaxVisionTextDualEncoderModel">FlaxVisionTextDualEncoderModel</a> (VisionTextDualEncoder model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/wav2vec2#transformers.Wav2Vec2Config">Wav2Vec2Config</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/wav2vec2#transformers.FlaxWav2Vec2Model">FlaxWav2Vec2Model</a> (Wav2Vec2 model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/xglm#transformers.XGLMConfig">XGLMConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/xglm#transformers.FlaxXGLMModel">FlaxXGLMModel</a> (XGLM model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/xlm-roberta#transformers.XLMRobertaConfig">XLMRobertaConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/xlm-roberta#transformers.FlaxXLMRobertaModel">FlaxXLMRobertaModel</a> (XLM-RoBERTa model)</li>
</ul>`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/vr_17548/src/transformers/models/auto/auto_factory.py#L389"}}),BE=new B({props:{anchor:"transformers.FlaxAutoModel.from_config.example",$$slots:{default:[RRt]},$$scope:{ctx:L}}}),q9=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/pr_17548/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/pr_17548/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/pr_17548/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/pr_17548/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/vr_17548/src/transformers/models/auto/auto_factory.py#L417"}}),iC=new B({props:{anchor:"transformers.FlaxAutoModel.from_pretrained.example",$$slots:{default:[PRt]},$$scope:{ctx:L}}}),j9=new te({}),D9=new R({props:{name:"class transformers.FlaxAutoModelForCausalLM",anchor:"transformers.FlaxAutoModelForCausalLM",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/vr_17548/src/transformers/models/auto/modeling_flax_auto.py#L257"}}),O9=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/pr_17548/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/pr_17548/en/model_doc/bart#transformers.BartConfig">BartConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/bart#transformers.FlaxBartForCausalLM">FlaxBartForCausalLM</a> (BART model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/bert#transformers.BertConfig">BertConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/bert#transformers.FlaxBertForCausalLM">FlaxBertForCausalLM</a> (BERT model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/big_bird#transformers.BigBirdConfig">BigBirdConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/big_bird#transformers.FlaxBigBirdForCausalLM">FlaxBigBirdForCausalLM</a> (BigBird model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/electra#transformers.ElectraConfig">ElectraConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/electra#transformers.FlaxElectraForCausalLM">FlaxElectraForCausalLM</a> (ELECTRA model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/gpt2#transformers.GPT2Config">GPT2Config</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/gpt2#transformers.FlaxGPT2LMHeadModel">FlaxGPT2LMHeadModel</a> (OpenAI GPT-2 model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/gptj#transformers.GPTJConfig">GPTJConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/gptj#transformers.FlaxGPTJForCausalLM">FlaxGPTJForCausalLM</a> (GPT-J model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/gpt_neo#transformers.GPTNeoConfig">GPTNeoConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/gpt_neo#transformers.FlaxGPTNeoForCausalLM">FlaxGPTNeoForCausalLM</a> (GPT Neo model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/opt#transformers.OPTConfig">OPTConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/opt#transformers.FlaxOPTForCausalLM">FlaxOPTForCausalLM</a> (OPT model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/roberta#transformers.RobertaConfig">RobertaConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/roberta#transformers.FlaxRobertaForCausalLM">FlaxRobertaForCausalLM</a> (RoBERTa model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/xglm#transformers.XGLMConfig">XGLMConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/xglm#transformers.FlaxXGLMForCausalLM">FlaxXGLMForCausalLM</a> (XGLM model)</li>
</ul>`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/vr_17548/src/transformers/models/auto/auto_factory.py#L389"}}),cC=new B({props:{anchor:"transformers.FlaxAutoModelForCausalLM.from_config.example",$$slots:{default:[BRt]},$$scope:{ctx:L}}}),V9=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/pr_17548/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/pr_17548/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/pr_17548/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/pr_17548/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/vr_17548/src/transformers/models/auto/auto_factory.py#L417"}}),TC=new B({props:{anchor:"transformers.FlaxAutoModelForCausalLM.from_pretrained.example",$$slots:{default:[IRt]},$$scope:{ctx:L}}}),X9=new te({}),z9=new R({props:{name:"class transformers.FlaxAutoModelForPreTraining",anchor:"transformers.FlaxAutoModelForPreTraining",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/vr_17548/src/transformers/models/auto/modeling_flax_auto.py#L250"}}),Q9=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/pr_17548/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/pr_17548/en/model_doc/albert#transformers.AlbertConfig">AlbertConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/albert#transformers.FlaxAlbertForPreTraining">FlaxAlbertForPreTraining</a> (ALBERT model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/bart#transformers.BartConfig">BartConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/bart#transformers.FlaxBartForConditionalGeneration">FlaxBartForConditionalGeneration</a> (BART model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/bert#transformers.BertConfig">BertConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/bert#transformers.FlaxBertForPreTraining">FlaxBertForPreTraining</a> (BERT model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/big_bird#transformers.BigBirdConfig">BigBirdConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/big_bird#transformers.FlaxBigBirdForPreTraining">FlaxBigBirdForPreTraining</a> (BigBird model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/electra#transformers.ElectraConfig">ElectraConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/electra#transformers.FlaxElectraForPreTraining">FlaxElectraForPreTraining</a> (ELECTRA model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/mbart#transformers.MBartConfig">MBartConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/mbart#transformers.FlaxMBartForConditionalGeneration">FlaxMBartForConditionalGeneration</a> (mBART model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/mt5#transformers.MT5Config">MT5Config</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/mt5#transformers.FlaxMT5ForConditionalGeneration">FlaxMT5ForConditionalGeneration</a> (MT5 model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/roformer#transformers.RoFormerConfig">RoFormerConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/roformer#transformers.FlaxRoFormerForMaskedLM">FlaxRoFormerForMaskedLM</a> (RoFormer model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/roberta#transformers.RobertaConfig">RobertaConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/roberta#transformers.FlaxRobertaForMaskedLM">FlaxRobertaForMaskedLM</a> (RoBERTa model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/t5#transformers.T5Config">T5Config</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/t5#transformers.FlaxT5ForConditionalGeneration">FlaxT5ForConditionalGeneration</a> (T5 model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/wav2vec2#transformers.Wav2Vec2Config">Wav2Vec2Config</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/wav2vec2#transformers.FlaxWav2Vec2ForPreTraining">FlaxWav2Vec2ForPreTraining</a> (Wav2Vec2 model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/xlm-roberta#transformers.XLMRobertaConfig">XLMRobertaConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/xlm-roberta#transformers.FlaxXLMRobertaForMaskedLM">FlaxXLMRobertaForMaskedLM</a> (XLM-RoBERTa model)</li>
</ul>`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/vr_17548/src/transformers/models/auto/auto_factory.py#L389"}}),EC=new B({props:{anchor:"transformers.FlaxAutoModelForPreTraining.from_config.example",$$slots:{default:[NRt]},$$scope:{ctx:L}}}),H9=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/pr_17548/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/pr_17548/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/pr_17548/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/pr_17548/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/vr_17548/src/transformers/models/auto/auto_factory.py#L417"}}),IC=new B({props:{anchor:"transformers.FlaxAutoModelForPreTraining.from_pretrained.example",$$slots:{default:[qRt]},$$scope:{ctx:L}}}),U9=new te({}),J9=new R({props:{name:"class transformers.FlaxAutoModelForMaskedLM",anchor:"transformers.FlaxAutoModelForMaskedLM",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/vr_17548/src/transformers/models/auto/modeling_flax_auto.py#L264"}}),K9=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/pr_17548/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/pr_17548/en/model_doc/albert#transformers.AlbertConfig">AlbertConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/albert#transformers.FlaxAlbertForMaskedLM">FlaxAlbertForMaskedLM</a> (ALBERT model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/bart#transformers.BartConfig">BartConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/bart#transformers.FlaxBartForConditionalGeneration">FlaxBartForConditionalGeneration</a> (BART model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/bert#transformers.BertConfig">BertConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/bert#transformers.FlaxBertForMaskedLM">FlaxBertForMaskedLM</a> (BERT model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/big_bird#transformers.BigBirdConfig">BigBirdConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/big_bird#transformers.FlaxBigBirdForMaskedLM">FlaxBigBirdForMaskedLM</a> (BigBird model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/distilbert#transformers.DistilBertConfig">DistilBertConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/distilbert#transformers.FlaxDistilBertForMaskedLM">FlaxDistilBertForMaskedLM</a> (DistilBERT model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/electra#transformers.ElectraConfig">ElectraConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/electra#transformers.FlaxElectraForMaskedLM">FlaxElectraForMaskedLM</a> (ELECTRA model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/mbart#transformers.MBartConfig">MBartConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/mbart#transformers.FlaxMBartForConditionalGeneration">FlaxMBartForConditionalGeneration</a> (mBART model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/roformer#transformers.RoFormerConfig">RoFormerConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/roformer#transformers.FlaxRoFormerForMaskedLM">FlaxRoFormerForMaskedLM</a> (RoFormer model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/roberta#transformers.RobertaConfig">RobertaConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/roberta#transformers.FlaxRobertaForMaskedLM">FlaxRobertaForMaskedLM</a> (RoBERTa model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/xlm-roberta#transformers.XLMRobertaConfig">XLMRobertaConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/xlm-roberta#transformers.FlaxXLMRobertaForMaskedLM">FlaxXLMRobertaForMaskedLM</a> (XLM-RoBERTa model)</li>
</ul>`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/vr_17548/src/transformers/models/auto/auto_factory.py#L389"}}),qC=new B({props:{anchor:"transformers.FlaxAutoModelForMaskedLM.from_config.example",$$slots:{default:[jRt]},$$scope:{ctx:L}}}),Z9=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/pr_17548/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/pr_17548/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/pr_17548/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/pr_17548/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/vr_17548/src/transformers/models/auto/auto_factory.py#L417"}}),UC=new B({props:{anchor:"transformers.FlaxAutoModelForMaskedLM.from_pretrained.example",$$slots:{default:[DRt]},$$scope:{ctx:L}}}),ex=new te({}),ox=new R({props:{name:"class transformers.FlaxAutoModelForSeq2SeqLM",anchor:"transformers.FlaxAutoModelForSeq2SeqLM",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/vr_17548/src/transformers/models/auto/modeling_flax_auto.py#L271"}}),tx=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/pr_17548/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/pr_17548/en/model_doc/bart#transformers.BartConfig">BartConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/bart#transformers.FlaxBartForConditionalGeneration">FlaxBartForConditionalGeneration</a> (BART model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/blenderbot#transformers.BlenderbotConfig">BlenderbotConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/blenderbot#transformers.FlaxBlenderbotForConditionalGeneration">FlaxBlenderbotForConditionalGeneration</a> (Blenderbot model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/blenderbot-small#transformers.BlenderbotSmallConfig">BlenderbotSmallConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/blenderbot-small#transformers.FlaxBlenderbotSmallForConditionalGeneration">FlaxBlenderbotSmallForConditionalGeneration</a> (BlenderbotSmall model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/encoder-decoder#transformers.EncoderDecoderConfig">EncoderDecoderConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/encoder-decoder#transformers.FlaxEncoderDecoderModel">FlaxEncoderDecoderModel</a> (Encoder decoder model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/mbart#transformers.MBartConfig">MBartConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/mbart#transformers.FlaxMBartForConditionalGeneration">FlaxMBartForConditionalGeneration</a> (mBART model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/mt5#transformers.MT5Config">MT5Config</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/mt5#transformers.FlaxMT5ForConditionalGeneration">FlaxMT5ForConditionalGeneration</a> (MT5 model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/marian#transformers.MarianConfig">MarianConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/marian#transformers.FlaxMarianMTModel">FlaxMarianMTModel</a> (Marian model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/pegasus#transformers.PegasusConfig">PegasusConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/pegasus#transformers.FlaxPegasusForConditionalGeneration">FlaxPegasusForConditionalGeneration</a> (Pegasus model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/t5#transformers.T5Config">T5Config</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/t5#transformers.FlaxT5ForConditionalGeneration">FlaxT5ForConditionalGeneration</a> (T5 model)</li>
</ul>`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/vr_17548/src/transformers/models/auto/auto_factory.py#L389"}}),YC=new B({props:{anchor:"transformers.FlaxAutoModelForSeq2SeqLM.from_config.example",$$slots:{default:[GRt]},$$scope:{ctx:L}}}),ax=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/pr_17548/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/pr_17548/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/pr_17548/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
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Whether or not to delete incompletely received files. Will attempt to resume the download if such a
file exists.`,name:"resume_download"},{anchor:"transformers.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;
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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.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/pr_17548/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/vr_17548/src/transformers/models/auto/auto_factory.py#L417"}}),l3=new B({props:{anchor:"transformers.FlaxAutoModelForSeq2SeqLM.from_pretrained.example",$$slots:{default:[ORt]},$$scope:{ctx:L}}}),nx=new te({}),sx=new R({props:{name:"class transformers.FlaxAutoModelForSequenceClassification",anchor:"transformers.FlaxAutoModelForSequenceClassification",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/vr_17548/src/transformers/models/auto/modeling_flax_auto.py#L280"}}),ix=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/pr_17548/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/pr_17548/en/model_doc/albert#transformers.AlbertConfig">AlbertConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/albert#transformers.FlaxAlbertForSequenceClassification">FlaxAlbertForSequenceClassification</a> (ALBERT model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/bart#transformers.BartConfig">BartConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/bart#transformers.FlaxBartForSequenceClassification">FlaxBartForSequenceClassification</a> (BART model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/bert#transformers.BertConfig">BertConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/bert#transformers.FlaxBertForSequenceClassification">FlaxBertForSequenceClassification</a> (BERT model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/big_bird#transformers.BigBirdConfig">BigBirdConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/big_bird#transformers.FlaxBigBirdForSequenceClassification">FlaxBigBirdForSequenceClassification</a> (BigBird model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/distilbert#transformers.DistilBertConfig">DistilBertConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/distilbert#transformers.FlaxDistilBertForSequenceClassification">FlaxDistilBertForSequenceClassification</a> (DistilBERT model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/electra#transformers.ElectraConfig">ElectraConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/electra#transformers.FlaxElectraForSequenceClassification">FlaxElectraForSequenceClassification</a> (ELECTRA model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/mbart#transformers.MBartConfig">MBartConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/mbart#transformers.FlaxMBartForSequenceClassification">FlaxMBartForSequenceClassification</a> (mBART model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/roformer#transformers.RoFormerConfig">RoFormerConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/roformer#transformers.FlaxRoFormerForSequenceClassification">FlaxRoFormerForSequenceClassification</a> (RoFormer model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/roberta#transformers.RobertaConfig">RobertaConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/roberta#transformers.FlaxRobertaForSequenceClassification">FlaxRobertaForSequenceClassification</a> (RoBERTa model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/xlm-roberta#transformers.XLMRobertaConfig">XLMRobertaConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/xlm-roberta#transformers.FlaxXLMRobertaForSequenceClassification">FlaxXLMRobertaForSequenceClassification</a> (XLM-RoBERTa model)</li>
</ul>`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/vr_17548/src/transformers/models/auto/auto_factory.py#L389"}}),d3=new B({props:{anchor:"transformers.FlaxAutoModelForSequenceClassification.from_config.example",$$slots:{default:[VRt]},$$scope:{ctx:L}}}),dx=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/pr_17548/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/pr_17548/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/pr_17548/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/pr_17548/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/vr_17548/src/transformers/models/auto/auto_factory.py#L417"}}),F3=new B({props:{anchor:"transformers.FlaxAutoModelForSequenceClassification.from_pretrained.example",$$slots:{default:[XRt]},$$scope:{ctx:L}}}),cx=new te({}),fx=new R({props:{name:"class transformers.FlaxAutoModelForQuestionAnswering",anchor:"transformers.FlaxAutoModelForQuestionAnswering",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/vr_17548/src/transformers/models/auto/modeling_flax_auto.py#L289"}}),gx=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/pr_17548/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/pr_17548/en/model_doc/albert#transformers.AlbertConfig">AlbertConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/albert#transformers.FlaxAlbertForQuestionAnswering">FlaxAlbertForQuestionAnswering</a> (ALBERT model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/bart#transformers.BartConfig">BartConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/bart#transformers.FlaxBartForQuestionAnswering">FlaxBartForQuestionAnswering</a> (BART model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/bert#transformers.BertConfig">BertConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/bert#transformers.FlaxBertForQuestionAnswering">FlaxBertForQuestionAnswering</a> (BERT model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/big_bird#transformers.BigBirdConfig">BigBirdConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/big_bird#transformers.FlaxBigBirdForQuestionAnswering">FlaxBigBirdForQuestionAnswering</a> (BigBird model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/distilbert#transformers.DistilBertConfig">DistilBertConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/distilbert#transformers.FlaxDistilBertForQuestionAnswering">FlaxDistilBertForQuestionAnswering</a> (DistilBERT model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/electra#transformers.ElectraConfig">ElectraConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/electra#transformers.FlaxElectraForQuestionAnswering">FlaxElectraForQuestionAnswering</a> (ELECTRA model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/mbart#transformers.MBartConfig">MBartConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/mbart#transformers.FlaxMBartForQuestionAnswering">FlaxMBartForQuestionAnswering</a> (mBART model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/roformer#transformers.RoFormerConfig">RoFormerConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/roformer#transformers.FlaxRoFormerForQuestionAnswering">FlaxRoFormerForQuestionAnswering</a> (RoFormer model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/roberta#transformers.RobertaConfig">RobertaConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/roberta#transformers.FlaxRobertaForQuestionAnswering">FlaxRobertaForQuestionAnswering</a> (RoBERTa model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/xlm-roberta#transformers.XLMRobertaConfig">XLMRobertaConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/xlm-roberta#transformers.FlaxXLMRobertaForQuestionAnswering">FlaxXLMRobertaForQuestionAnswering</a> (XLM-RoBERTa model)</li>
</ul>`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/vr_17548/src/transformers/models/auto/auto_factory.py#L389"}}),M3=new B({props:{anchor:"transformers.FlaxAutoModelForQuestionAnswering.from_config.example",$$slots:{default:[zRt]},$$scope:{ctx:L}}}),hx=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/pr_17548/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/pr_17548/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/pr_17548/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/pr_17548/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/vr_17548/src/transformers/models/auto/auto_factory.py#L417"}}),R3=new B({props:{anchor:"transformers.FlaxAutoModelForQuestionAnswering.from_pretrained.example",$$slots:{default:[WRt]},$$scope:{ctx:L}}}),px=new te({}),_x=new R({props:{name:"class transformers.FlaxAutoModelForTokenClassification",anchor:"transformers.FlaxAutoModelForTokenClassification",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/vr_17548/src/transformers/models/auto/modeling_flax_auto.py#L296"}}),bx=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/pr_17548/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/pr_17548/en/model_doc/albert#transformers.AlbertConfig">AlbertConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/albert#transformers.FlaxAlbertForTokenClassification">FlaxAlbertForTokenClassification</a> (ALBERT model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/bert#transformers.BertConfig">BertConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/bert#transformers.FlaxBertForTokenClassification">FlaxBertForTokenClassification</a> (BERT model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/big_bird#transformers.BigBirdConfig">BigBirdConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/big_bird#transformers.FlaxBigBirdForTokenClassification">FlaxBigBirdForTokenClassification</a> (BigBird model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/distilbert#transformers.DistilBertConfig">DistilBertConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/distilbert#transformers.FlaxDistilBertForTokenClassification">FlaxDistilBertForTokenClassification</a> (DistilBERT model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/electra#transformers.ElectraConfig">ElectraConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/electra#transformers.FlaxElectraForTokenClassification">FlaxElectraForTokenClassification</a> (ELECTRA model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/roformer#transformers.RoFormerConfig">RoFormerConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/roformer#transformers.FlaxRoFormerForTokenClassification">FlaxRoFormerForTokenClassification</a> (RoFormer model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/roberta#transformers.RobertaConfig">RobertaConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/roberta#transformers.FlaxRobertaForTokenClassification">FlaxRobertaForTokenClassification</a> (RoBERTa model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/xlm-roberta#transformers.XLMRobertaConfig">XLMRobertaConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/xlm-roberta#transformers.FlaxXLMRobertaForTokenClassification">FlaxXLMRobertaForTokenClassification</a> (XLM-RoBERTa model)</li>
</ul>`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/vr_17548/src/transformers/models/auto/auto_factory.py#L389"}}),B3=new B({props:{anchor:"transformers.FlaxAutoModelForTokenClassification.from_config.example",$$slots:{default:[QRt]},$$scope:{ctx:L}}}),vx=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/pr_17548/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/pr_17548/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/pr_17548/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/pr_17548/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/vr_17548/src/transformers/models/auto/auto_factory.py#L417"}}),X3=new B({props:{anchor:"transformers.FlaxAutoModelForTokenClassification.from_pretrained.example",$$slots:{default:[HRt]},$$scope:{ctx:L}}}),Fx=new te({}),Tx=new R({props:{name:"class transformers.FlaxAutoModelForMultipleChoice",anchor:"transformers.FlaxAutoModelForMultipleChoice",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/vr_17548/src/transformers/models/auto/modeling_flax_auto.py#L305"}}),Ex=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/pr_17548/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/pr_17548/en/model_doc/albert#transformers.AlbertConfig">AlbertConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/albert#transformers.FlaxAlbertForMultipleChoice">FlaxAlbertForMultipleChoice</a> (ALBERT model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/bert#transformers.BertConfig">BertConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/bert#transformers.FlaxBertForMultipleChoice">FlaxBertForMultipleChoice</a> (BERT model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/big_bird#transformers.BigBirdConfig">BigBirdConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/big_bird#transformers.FlaxBigBirdForMultipleChoice">FlaxBigBirdForMultipleChoice</a> (BigBird model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/distilbert#transformers.DistilBertConfig">DistilBertConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/distilbert#transformers.FlaxDistilBertForMultipleChoice">FlaxDistilBertForMultipleChoice</a> (DistilBERT model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/electra#transformers.ElectraConfig">ElectraConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/electra#transformers.FlaxElectraForMultipleChoice">FlaxElectraForMultipleChoice</a> (ELECTRA model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/roformer#transformers.RoFormerConfig">RoFormerConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/roformer#transformers.FlaxRoFormerForMultipleChoice">FlaxRoFormerForMultipleChoice</a> (RoFormer model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/roberta#transformers.RobertaConfig">RobertaConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/roberta#transformers.FlaxRobertaForMultipleChoice">FlaxRobertaForMultipleChoice</a> (RoBERTa model)</li>
<li><a href="/docs/transformers/pr_17548/en/model_doc/xlm-roberta#transformers.XLMRobertaConfig">XLMRobertaConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/xlm-roberta#transformers.FlaxXLMRobertaForMultipleChoice">FlaxXLMRobertaForMultipleChoice</a> (XLM-RoBERTa model)</li>
</ul>`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/vr_17548/src/transformers/models/auto/auto_factory.py#L389"}}),W3=new B({props:{anchor:"transformers.FlaxAutoModelForMultipleChoice.from_config.example",$$slots:{default:[URt]},$$scope:{ctx:L}}}),Cx=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/pr_17548/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/pr_17548/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/pr_17548/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/pr_17548/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/vr_17548/src/transformers/models/auto/auto_factory.py#L417"}}),o0=new B({props:{anchor:"transformers.FlaxAutoModelForMultipleChoice.from_pretrained.example",$$slots:{default:[JRt]},$$scope:{ctx:L}}}),wx=new te({}),Ax=new R({props:{name:"class transformers.FlaxAutoModelForNextSentencePrediction",anchor:"transformers.FlaxAutoModelForNextSentencePrediction",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/vr_17548/src/transformers/models/auto/modeling_flax_auto.py#L312"}}),Lx=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/pr_17548/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/pr_17548/en/model_doc/bert#transformers.BertConfig">BertConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/bert#transformers.FlaxBertForNextSentencePrediction">FlaxBertForNextSentencePrediction</a> (BERT model)</li>
</ul>`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/vr_17548/src/transformers/models/auto/auto_factory.py#L389"}}),t0=new B({props:{anchor:"transformers.FlaxAutoModelForNextSentencePrediction.from_config.example",$$slots:{default:[YRt]},$$scope:{ctx:L}}}),xx=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
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<li><a href="/docs/transformers/pr_17548/en/model_doc/vision-encoder-decoder#transformers.VisionEncoderDecoderConfig">VisionEncoderDecoderConfig</a> configuration class: <a href="/docs/transformers/pr_17548/en/model_doc/vision-encoder-decoder#transformers.FlaxVisionEncoderDecoderModel">FlaxVisionEncoderDecoderModel</a> (Vision Encoder decoder model)</li>
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falling back to using pattern matching on `),Jie=a("code"),w8o=o("pretrained_model_name_or_path"),A8o=o(":"),y8o=l(),G=a("ul"),du=a("li"),Yie=a("strong"),L8o=o("albert"),x8o=o(" \u2014 "),Vq=a("a"),$8o=o("AlbertForPreTraining"),k8o=o(" (ALBERT model)"),S8o=l(),cu=a("li"),Kie=a("strong"),R8o=o("bart"),P8o=o(" \u2014 "),Xq=a("a"),B8o=o("BartForConditionalGeneration"),I8o=o(" (BART model)"),N8o=l(),fu=a("li"),Zie=a("strong"),q8o=o("bert"),j8o=o(" \u2014 "),zq=a("a"),D8o=o("BertForPreTraining"),G8o=o(" (BERT model)"),O8o=l(),mu=a("li"),ede=a("strong"),V8o=o("big_bird"),X8o=o(" \u2014 "),Wq=a("a"),z8o=o("BigBirdForPreTraining"),W8o=o(" (BigBird model)"),Q8o=l(),gu=a("li"),ode=a("strong"),H8o=o("camembert"),U8o=o(" \u2014 "),Qq=a("a"),J8o=o("CamembertForMaskedLM"),Y8o=o(" (CamemBERT model)"),K8o=l(),hu=a("li"),rde=a("strong"),Z8o=o("ctrl"),e7o=o(" \u2014 "),Hq=a("a"),o7o=o("CTRLLMHeadModel"),r7o=o(" (CTRL model)"),t7o=l(),pu=a("li"),tde=a("strong"),a7o=o("data2vec-text"),n7o=o(" \u2014 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deactivated). To train the model, you should first set it back in training mode with `),Dde=a("code"),wCo=o("model.train()"),ACo=l(),F(Ku.$$.fragment),Kqe=l(),Si=a("h2"),Zu=a("a"),Gde=a("span"),F(b6.$$.fragment),yCo=l(),Ode=a("span"),LCo=o("AutoModelForCausalLM"),Zqe=l(),$o=a("div"),F(v6.$$.fragment),xCo=l(),Ri=a("p"),$Co=o(`This is a generic model class that will be instantiated as one of the model classes of the library (with a causal language modeling head) when created
with the `),kj=a("a"),kCo=o("from_pretrained()"),SCo=o(" class method or the "),Sj=a("a"),RCo=o("from_config()"),PCo=o(` class
method.`),BCo=l(),F6=a("p"),ICo=o("This class cannot be instantiated directly using "),Vde=a("code"),NCo=o("__init__()"),qCo=o(" (throws an error)."),jCo=l(),nt=a("div"),F(T6.$$.fragment),DCo=l(),Xde=a("p"),GCo=o("Instantiates one of the model classes of the library (with a causal language modeling head) from a configuration."),OCo=l(),Pi=a("p"),VCo=o(`Note:
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model\u2019s configuration. Use `),Rj=a("a"),WCo=o("from_pretrained()"),QCo=o(" to load the model weights."),HCo=l(),F(e1.$$.fragment),UCo=l(),Ke=a("div"),F(M6.$$.fragment),JCo=l(),Wde=a("p"),YCo=o("Instantiate one of the model classes of the library (with a causal language modeling head) from a pretrained model."),KCo=l(),$a=a("p"),ZCo=o("The model class to instantiate is selected based on the "),Qde=a("code"),e3o=o("model_type"),o3o=o(` property of the config object (either
passed as an argument or loaded from `),Hde=a("code"),r3o=o("pretrained_model_name_or_path"),t3o=o(` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),Ude=a("code"),a3o=o("pretrained_model_name_or_path"),n3o=o(":"),s3o=l(),z=a("ul"),o1=a("li"),Jde=a("strong"),l3o=o("bart"),i3o=o(" \u2014 "),Pj=a("a"),d3o=o("BartForCausalLM"),c3o=o(" (BART model)"),f3o=l(),r1=a("li"),Yde=a("strong"),m3o=o("bert"),g3o=o(" \u2014 "),Bj=a("a"),h3o=o("BertLMHeadModel"),p3o=o(" (BERT model)"),_3o=l(),t1=a("li"),Kde=a("strong"),u3o=o("bert-generation"),b3o=o(" \u2014 "),Ij=a("a"),v3o=o("BertGenerationDecoder"),F3o=o(" (Bert Generation model)"),T3o=l(),a1=a("li"),Zde=a("strong"),M3o=o("big_bird"),E3o=o(" \u2014 "),Nj=a("a"),C3o=o("BigBirdForCausalLM"),w3o=o(" (BigBird model)"),A3o=l(),n1=a("li"),ece=a("strong"),y3o=o("bigbird_pegasus"),L3o=o(" \u2014 "),qj=a("a"),x3o=o("BigBirdPegasusForCausalLM"),$3o=o(" (BigBird-Pegasus model)"),k3o=l(),s1=a("li"),oce=a("strong"),S3o=o("blenderbot"),R3o=o(" \u2014 "),jj=a("a"),P3o=o("BlenderbotForCausalLM"),B3o=o(" (Blenderbot 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with the `),uD=a("a"),jAo=o("from_pretrained()"),DAo=o(" class method or the "),bD=a("a"),GAo=o("from_config()"),OAo=o(` class
method.`),VAo=l(),w6=a("p"),XAo=o("This class cannot be instantiated directly using "),Nce=a("code"),zAo=o("__init__()"),WAo=o(" (throws an error)."),QAo=l(),st=a("div"),F(A6.$$.fragment),HAo=l(),qce=a("p"),UAo=o("Instantiates one of the model classes of the library (with a masked language modeling head) from a configuration."),JAo=l(),Ni=a("p"),YAo=o(`Note:
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passed as an argument or loaded from `),Oce=a("code"),c6o=o("pretrained_model_name_or_path"),f6o=o(` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),Vce=a("code"),m6o=o("pretrained_model_name_or_path"),g6o=o(":"),h6o=l(),W=a("ul"),O1=a("li"),Xce=a("strong"),p6o=o("albert"),_6o=o(" \u2014 "),FD=a("a"),u6o=o("AlbertForMaskedLM"),b6o=o(" (ALBERT model)"),v6o=l(),V1=a("li"),zce=a("strong"),F6o=o("bart"),T6o=o(" \u2014 "),TD=a("a"),M6o=o("BartForConditionalGeneration"),E6o=o(" (BART model)"),C6o=l(),X1=a("li"),Wce=a("strong"),w6o=o("bert"),A6o=o(" \u2014 "),MD=a("a"),y6o=o("BertForMaskedLM"),L6o=o(" (BERT model)"),x6o=l(),z1=a("li"),Qce=a("strong"),$6o=o("big_bird"),k6o=o(" \u2014 "),ED=a("a"),S6o=o("BigBirdForMaskedLM"),R6o=o(" (BigBird model)"),P6o=l(),W1=a("li"),Hce=a("strong"),B6o=o("camembert"),I6o=o(" \u2014 "),CD=a("a"),N6o=o("CamembertForMaskedLM"),q6o=o(" (CamemBERT model)"),j6o=l(),Q1=a("li"),Uce=a("strong"),D6o=o("convbert"),G6o=o(" \u2014 "),wD=a("a"),O6o=o("ConvBertForMaskedLM"),V6o=o(" (ConvBERT model)"),X6o=l(),H1=a("li"),Jce=a("strong"),z6o=o("data2vec-text"),W6o=o(" \u2014 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method.`),tko=l(),B6=a("p"),ako=o("This class cannot be instantiated directly using "),ame=a("code"),nko=o("__init__()"),sko=o(" (throws an error)."),lko=l(),it=a("div"),F(I6.$$.fragment),iko=l(),nme=a("p"),dko=o("Instantiates one of the model classes of the library (with a sequence classification head) from a configuration."),cko=l(),Vi=a("p"),fko=o(`Note:
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deactivated). To train the model, you should first set it back in training mode with `),nge=a("code"),YBo=o("model.train()"),KBo=l(),F(O2.$$.fragment),sje=l(),Xi=a("h2"),V2=a("a"),sge=a("span"),F(q6.$$.fragment),ZBo=l(),lge=a("span"),eIo=o("AutoModelForMultipleChoice"),lje=l(),Po=a("div"),F(j6.$$.fragment),oIo=l(),zi=a("p"),rIo=o(`This is a generic model class that will be instantiated as one of the model classes of the library (with a multiple choice head) when created
with the `),_O=a("a"),tIo=o("from_pretrained()"),aIo=o(" class method or the "),uO=a("a"),nIo=o("from_config()"),sIo=o(` class
method.`),lIo=l(),D6=a("p"),iIo=o("This class cannot be instantiated directly using "),ige=a("code"),dIo=o("__init__()"),cIo=o(" (throws an error)."),fIo=l(),dt=a("div"),F(G6.$$.fragment),mIo=l(),dge=a("p"),gIo=o("Instantiates one of the model classes of the library (with a multiple choice head) from a configuration."),hIo=l(),Wi=a("p"),pIo=o(`Note:
Loading a model from its configuration file does `),cge=a("strong"),_Io=o("not"),uIo=o(` load the model weights. It only affects the
model\u2019s configuration. Use `),bO=a("a"),bIo=o("from_pretrained()"),vIo=o(" to load the model weights."),FIo=l(),F(X2.$$.fragment),TIo=l(),ro=a("div"),F(O6.$$.fragment),MIo=l(),fge=a("p"),EIo=o("Instantiate one of the model classes of the library (with a multiple choice head) from a pretrained model."),CIo=l(),Pa=a("p"),wIo=o("The model class to instantiate is selected based on the "),mge=a("code"),AIo=o("model_type"),yIo=o(` property of the config object (either
passed as an argument or loaded from `),gge=a("code"),LIo=o("pretrained_model_name_or_path"),xIo=o(` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),hge=a("code"),$Io=o("pretrained_model_name_or_path"),kIo=o(":"),SIo=l(),K=a("ul"),z2=a("li"),pge=a("strong"),RIo=o("albert"),PIo=o(" \u2014 "),vO=a("a"),BIo=o("AlbertForMultipleChoice"),IIo=o(" (ALBERT model)"),NIo=l(),W2=a("li"),_ge=a("strong"),qIo=o("bert"),jIo=o(" \u2014 "),FO=a("a"),DIo=o("BertForMultipleChoice"),GIo=o(" (BERT model)"),OIo=l(),Q2=a("li"),uge=a("strong"),VIo=o("big_bird"),XIo=o(" \u2014 "),TO=a("a"),zIo=o("BigBirdForMultipleChoice"),WIo=o(" (BigBird model)"),QIo=l(),H2=a("li"),bge=a("strong"),HIo=o("camembert"),UIo=o(" \u2014 "),MO=a("a"),JIo=o("CamembertForMultipleChoice"),YIo=o(" (CamemBERT model)"),KIo=l(),U2=a("li"),vge=a("strong"),ZIo=o("canine"),eNo=o(" \u2014 "),EO=a("a"),oNo=o("CanineForMultipleChoice"),rNo=o(" (CANINE model)"),tNo=l(),J2=a("li"),Fge=a("strong"),aNo=o("convbert"),nNo=o(" \u2014 "),CO=a("a"),sNo=o("ConvBertForMultipleChoice"),lNo=o(" (ConvBERT model)"),iNo=l(),Y2=a("li"),Tge=a("strong"),dNo=o("data2vec-text"),cNo=o(" \u2014 "),wO=a("a"),fNo=o("Data2VecTextForMultipleChoice"),mNo=o(" (Data2VecText model)"),gNo=l(),K2=a("li"),Mge=a("strong"),hNo=o("deberta-v2"),pNo=o(" \u2014 "),AO=a("a"),_No=o("DebertaV2ForMultipleChoice"),uNo=o(" (DeBERTa-v2 model)"),bNo=l(),Z2=a("li"),Ege=a("strong"),vNo=o("distilbert"),FNo=o(" \u2014 "),yO=a("a"),TNo=o("DistilBertForMultipleChoice"),MNo=o(" (DistilBERT model)"),ENo=l(),e4=a("li"),Cge=a("strong"),CNo=o("electra"),wNo=o(" \u2014 "),LO=a("a"),ANo=o("ElectraForMultipleChoice"),yNo=o(" (ELECTRA model)"),LNo=l(),o4=a("li"),wge=a("strong"),xNo=o("flaubert"),$No=o(" \u2014 "),xO=a("a"),kNo=o("FlaubertForMultipleChoice"),SNo=o(" (FlauBERT model)"),RNo=l(),r4=a("li"),Age=a("strong"),PNo=o("fnet"),BNo=o(" \u2014 "),$O=a("a"),INo=o("FNetForMultipleChoice"),NNo=o(" (FNet model)"),qNo=l(),t4=a("li"),yge=a("strong"),jNo=o("funnel"),DNo=o(" \u2014 "),kO=a("a"),GNo=o("FunnelForMultipleChoice"),ONo=o(" (Funnel Transformer model)"),VNo=l(),a4=a("li"),Lge=a("strong"),XNo=o("ibert"),zNo=o(" \u2014 "),SO=a("a"),WNo=o("IBertForMultipleChoice"),QNo=o(" (I-BERT model)"),HNo=l(),n4=a("li"),xge=a("strong"),UNo=o("longformer"),JNo=o(" \u2014 "),RO=a("a"),YNo=o("LongformerForMultipleChoice"),KNo=o(" (Longformer model)"),ZNo=l(),s4=a("li"),$ge=a("strong"),eqo=o("megatron-bert"),oqo=o(" \u2014 "),PO=a("a"),rqo=o("MegatronBertForMultipleChoice"),tqo=o(" (Megatron-BERT model)"),aqo=l(),l4=a("li"),kge=a("strong"),nqo=o("mobilebert"),sqo=o(" \u2014 "),BO=a("a"),lqo=o("MobileBertForMultipleChoice"),iqo=o(" (MobileBERT model)"),dqo=l(),i4=a("li"),Sge=a("strong"),cqo=o("mpnet"),fqo=o(" \u2014 "),IO=a("a"),mqo=o("MPNetForMultipleChoice"),gqo=o(" (MPNet model)"),hqo=l(),d4=a("li"),Rge=a("strong"),pqo=o("nystromformer"),_qo=o(" \u2014 "),NO=a("a"),uqo=o("NystromformerForMultipleChoice"),bqo=o(" (Nystr\xF6mformer model)"),vqo=l(),c4=a("li"),Pge=a("strong"),Fqo=o("qdqbert"),Tqo=o(" \u2014 "),qO=a("a"),Mqo=o("QDQBertForMultipleChoice"),Eqo=o(" (QDQBert model)"),Cqo=l(),f4=a("li"),Bge=a("strong"),wqo=o("rembert"),Aqo=o(" \u2014 "),jO=a("a"),yqo=o("RemBertForMultipleChoice"),Lqo=o(" (RemBERT model)"),xqo=l(),m4=a("li"),Ige=a("strong"),$qo=o("roberta"),kqo=o(" \u2014 "),DO=a("a"),Sqo=o("RobertaForMultipleChoice"),Rqo=o(" (RoBERTa model)"),Pqo=l(),g4=a("li"),Nge=a("strong"),Bqo=o("roformer"),Iqo=o(" \u2014 "),GO=a("a"),Nqo=o("RoFormerForMultipleChoice"),qqo=o(" (RoFormer model)"),jqo=l(),h4=a("li"),qge=a("strong"),Dqo=o("squeezebert"),Gqo=o(" \u2014 "),OO=a("a"),Oqo=o("SqueezeBertForMultipleChoice"),Vqo=o(" (SqueezeBERT model)"),Xqo=l(),p4=a("li"),jge=a("strong"),zqo=o("xlm"),Wqo=o(" \u2014 "),VO=a("a"),Qqo=o("XLMForMultipleChoice"),Hqo=o(" (XLM model)"),Uqo=l(),_4=a("li"),Dge=a("strong"),Jqo=o("xlm-roberta"),Yqo=o(" \u2014 "),XO=a("a"),Kqo=o("XLMRobertaForMultipleChoice"),Zqo=o(" (XLM-RoBERTa model)"),ejo=l(),u4=a("li"),Gge=a("strong"),ojo=o("xlm-roberta-xl"),rjo=o(" \u2014 "),zO=a("a"),tjo=o("XLMRobertaXLForMultipleChoice"),ajo=o(" (XLM-RoBERTa-XL model)"),njo=l(),b4=a("li"),Oge=a("strong"),sjo=o("xlnet"),ljo=o(" \u2014 "),WO=a("a"),ijo=o("XLNetForMultipleChoice"),djo=o(" (XLNet model)"),cjo=l(),v4=a("li"),Vge=a("strong"),fjo=o("yoso"),mjo=o(" \u2014 "),QO=a("a"),gjo=o("YosoForMultipleChoice"),hjo=o(" (YOSO model)"),pjo=l(),F4=a("p"),_jo=o("The model is set in evaluation mode by default using "),Xge=a("code"),ujo=o("model.eval()"),bjo=o(` (so for instance, dropout modules are
deactivated). To train the model, you should first set it back in training mode with `),zge=a("code"),vjo=o("model.train()"),Fjo=l(),F(T4.$$.fragment),ije=l(),Qi=a("h2"),M4=a("a"),Wge=a("span"),F(V6.$$.fragment),Tjo=l(),Qge=a("span"),Mjo=o("AutoModelForNextSentencePrediction"),dje=l(),Bo=a("div"),F(X6.$$.fragment),Ejo=l(),Hi=a("p"),Cjo=o(`This is a generic model class that will be instantiated as one of the model classes of the library (with a next sentence prediction head) when created
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method.`),xjo=l(),z6=a("p"),$jo=o("This class cannot be instantiated directly using "),Hge=a("code"),kjo=o("__init__()"),Sjo=o(" (throws an error)."),Rjo=l(),ct=a("div"),F(W6.$$.fragment),Pjo=l(),Uge=a("p"),Bjo=o("Instantiates one of the model classes of the library (with a next sentence prediction head) from a configuration."),Ijo=l(),Ui=a("p"),Njo=o(`Note:
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model\u2019s configuration. Use `),JO=a("a"),Djo=o("from_pretrained()"),Gjo=o(" to load the model weights."),Ojo=l(),F(E4.$$.fragment),Vjo=l(),to=a("div"),F(Q6.$$.fragment),Xjo=l(),Yge=a("p"),zjo=o("Instantiate one of the model classes of the library (with a next sentence prediction head) from a pretrained model."),Wjo=l(),Ba=a("p"),Qjo=o("The model class to instantiate is selected based on the "),Kge=a("code"),Hjo=o("model_type"),Ujo=o(` property of the config object (either
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method.`),jDo=l(),J6=a("p"),DDo=o("This class cannot be instantiated directly using "),che=a("code"),GDo=o("__init__()"),ODo=o(" (throws an error)."),VDo=l(),ft=a("div"),F(Y6.$$.fragment),XDo=l(),fhe=a("p"),zDo=o("Instantiates one of the model classes of the library (with a token classification head) from a configuration."),WDo=l(),Ki=a("p"),QDo=o(`Note:
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model\u2019s configuration. Use `),aV=a("a"),JDo=o("from_pretrained()"),YDo=o(" to load the model weights."),KDo=l(),F(S4.$$.fragment),ZDo=l(),ao=a("div"),F(K6.$$.fragment),eGo=l(),ghe=a("p"),oGo=o("Instantiate one of the model classes of the library (with a token classification head) from a pretrained model."),rGo=l(),Ia=a("p"),tGo=o("The model class to instantiate is selected based on the "),hhe=a("code"),aGo=o("model_type"),nGo=o(` property of the config object (either
passed as an argument or loaded from `),phe=a("code"),sGo=o("pretrained_model_name_or_path"),lGo=o(` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),_he=a("code"),iGo=o("pretrained_model_name_or_path"),dGo=o(":"),cGo=l(),U=a("ul"),R4=a("li"),uhe=a("strong"),fGo=o("albert"),mGo=o(" \u2014 "),nV=a("a"),gGo=o("AlbertForTokenClassification"),hGo=o(" (ALBERT model)"),pGo=l(),P4=a("li"),bhe=a("strong"),_Go=o("bert"),uGo=o(" \u2014 "),sV=a("a"),bGo=o("BertForTokenClassification"),vGo=o(" (BERT model)"),FGo=l(),B4=a("li"),vhe=a("strong"),TGo=o("big_bird"),MGo=o(" \u2014 "),lV=a("a"),EGo=o("BigBirdForTokenClassification"),CGo=o(" (BigBird model)"),wGo=l(),I4=a("li"),Fhe=a("strong"),AGo=o("camembert"),yGo=o(" \u2014 "),iV=a("a"),LGo=o("CamembertForTokenClassification"),xGo=o(" (CamemBERT model)"),$Go=l(),N4=a("li"),The=a("strong"),kGo=o("canine"),SGo=o(" \u2014 "),dV=a("a"),RGo=o("CanineForTokenClassification"),PGo=o(" (CANINE model)"),BGo=l(),q4=a("li"),Mhe=a("strong"),IGo=o("convbert"),NGo=o(" \u2014 "),cV=a("a"),qGo=o("ConvBertForTokenClassification"),jGo=o(" (ConvBERT 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passed as an argument or loaded from `),__e=a("code"),mJo=o("pretrained_model_name_or_path"),gJo=o(` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),u_e=a("code"),hJo=o("pretrained_model_name_or_path"),pJo=o(":"),_Jo=l(),ve=a("ul"),m5=a("li"),b_e=a("strong"),uJo=o("beit"),bJo=o(" \u2014 "),BX=a("a"),vJo=o("BeitForImageClassification"),FJo=o(" (BEiT model)"),TJo=l(),g5=a("li"),v_e=a("strong"),MJo=o("convnext"),EJo=o(" \u2014 "),IX=a("a"),CJo=o("ConvNextForImageClassification"),wJo=o(" (ConvNeXT model)"),AJo=l(),h5=a("li"),F_e=a("strong"),yJo=o("cvt"),LJo=o(" \u2014 "),NX=a("a"),xJo=o("CvtForImageClassification"),$Jo=o(" (CvT model)"),kJo=l(),p5=a("li"),T_e=a("strong"),SJo=o("data2vec-vision"),RJo=o(" \u2014 "),qX=a("a"),PJo=o("Data2VecVisionForImageClassification"),BJo=o(" (Data2VecVision model)"),IJo=l(),Bs=a("li"),M_e=a("strong"),NJo=o("deit"),qJo=o(" \u2014 "),jX=a("a"),jJo=o("DeiTForImageClassification"),DJo=o(" or "),DX=a("a"),GJo=o("DeiTForImageClassificationWithTeacher"),OJo=o(" (DeiT model)"),VJo=l(),_5=a("li"),E_e=a("strong"),XJo=o("imagegpt"),zJo=o(" \u2014 "),GX=a("a"),WJo=o("ImageGPTForImageClassification"),QJo=o(" (ImageGPT model)"),HJo=l(),Is=a("li"),C_e=a("strong"),UJo=o("levit"),JJo=o(" \u2014 "),OX=a("a"),YJo=o("LevitForImageClassification"),KJo=o(" or "),VX=a("a"),ZJo=o("LevitForImageClassificationWithTeacher"),eYo=o(" (LeViT model)"),oYo=l(),pt=a("li"),w_e=a("strong"),rYo=o("perceiver"),tYo=o(" \u2014 "),XX=a("a"),aYo=o("PerceiverForImageClassificationLearned"),nYo=o(" or "),zX=a("a"),sYo=o("PerceiverForImageClassificationFourier"),lYo=o(" or "),WX=a("a"),iYo=o("PerceiverForImageClassificationConvProcessing"),dYo=o(" (Perceiver model)"),cYo=l(),u5=a("li"),A_e=a("strong"),fYo=o("poolformer"),mYo=o(" \u2014 "),QX=a("a"),gYo=o("PoolFormerForImageClassification"),hYo=o(" (PoolFormer model)"),pYo=l(),b5=a("li"),y_e=a("strong"),_Yo=o("regnet"),uYo=o(" \u2014 "),HX=a("a"),bYo=o("RegNetForImageClassification"),vYo=o(" (RegNet model)"),FYo=l(),v5=a("li"),L_e=a("strong"),TYo=o("resnet"),MYo=o(" \u2014 "),UX=a("a"),EYo=o("ResNetForImageClassification"),CYo=o(" (ResNet model)"),wYo=l(),F5=a("li"),x_e=a("strong"),AYo=o("segformer"),yYo=o(" \u2014 "),JX=a("a"),LYo=o("SegformerForImageClassification"),xYo=o(" (SegFormer model)"),$Yo=l(),T5=a("li"),$_e=a("strong"),kYo=o("swin"),SYo=o(" \u2014 "),YX=a("a"),RYo=o("SwinForImageClassification"),PYo=o(" (Swin Transformer model)"),BYo=l(),M5=a("li"),k_e=a("strong"),IYo=o("van"),NYo=o(" \u2014 "),KX=a("a"),qYo=o("VanForImageClassification"),jYo=o(" (VAN model)"),DYo=l(),E5=a("li"),S_e=a("strong"),GYo=o("vit"),OYo=o(" \u2014 "),ZX=a("a"),VYo=o("ViTForImageClassification"),XYo=o(" (ViT model)"),zYo=l(),C5=a("p"),WYo=o("The model is set in evaluation mode by default using "),R_e=a("code"),QYo=o("model.eval()"),HYo=o(` (so for instance, dropout modules are
deactivated). To train the model, you should first set it back in training mode with `),P_e=a("code"),UYo=o("model.train()"),JYo=l(),F(w5.$$.fragment),bje=l(),id=a("h2"),A5=a("a"),B_e=a("span"),F(hy.$$.fragment),YYo=l(),I_e=a("span"),KYo=o("AutoModelForVision2Seq"),vje=l(),Do=a("div"),F(py.$$.fragment),ZYo=l(),dd=a("p"),eKo=o(`This is a generic model class that will be instantiated as one of the model classes of the library (with a vision-to-text modeling head) when created
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model\u2019s configuration. Use `),rz=a("a"),_Ko=o("from_pretrained()"),uKo=o(" to load the model weights."),bKo=l(),F(y5.$$.fragment),vKo=l(),io=a("div"),F(by.$$.fragment),FKo=l(),D_e=a("p"),TKo=o("Instantiate one of the model classes of the library (with a vision-to-text modeling head) from a pretrained model."),MKo=l(),Da=a("p"),EKo=o("The model class to instantiate is selected based on the "),G_e=a("code"),CKo=o("model_type"),wKo=o(` property of the config object (either
passed as an argument or loaded from `),O_e=a("code"),AKo=o("pretrained_model_name_or_path"),yKo=o(` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),V_e=a("code"),LKo=o("pretrained_model_name_or_path"),xKo=o(":"),$Ko=l(),X_e=a("ul"),L5=a("li"),z_e=a("strong"),kKo=o("vision-encoder-decoder"),SKo=o(" \u2014 "),tz=a("a"),RKo=o("VisionEncoderDecoderModel"),PKo=o(" (Vision Encoder decoder model)"),BKo=l(),x5=a("p"),IKo=o("The model is set in evaluation mode by default using "),W_e=a("code"),NKo=o("model.eval()"),qKo=o(` (so for instance, dropout modules are
deactivated). To train the model, you should first set it back in training mode with `),Q_e=a("code"),jKo=o("model.train()"),DKo=l(),F($5.$$.fragment),Fje=l(),fd=a("h2"),k5=a("a"),H_e=a("span"),F(vy.$$.fragment),GKo=l(),U_e=a("span"),OKo=o("AutoModelForAudioClassification"),Tje=l(),Go=a("div"),F(Fy.$$.fragment),VKo=l(),md=a("p"),XKo=o(`This is a generic model class that will be instantiated as one of the model classes of the library (with a audio classification head) when created
with the `),az=a("a"),zKo=o("from_pretrained()"),WKo=o(" class method or the "),nz=a("a"),QKo=o("from_config()"),HKo=o(` class
method.`),UKo=l(),Ty=a("p"),JKo=o("This class cannot be instantiated directly using "),J_e=a("code"),YKo=o("__init__()"),KKo=o(" (throws an error)."),ZKo=l(),ut=a("div"),F(My.$$.fragment),eZo=l(),Y_e=a("p"),oZo=o("Instantiates one of the model classes of the library (with a audio classification head) from a configuration."),rZo=l(),gd=a("p"),tZo=o(`Note:
Loading a model from its configuration file does `),K_e=a("strong"),aZo=o("not"),nZo=o(` load the model weights. It only affects the
model\u2019s configuration. Use `),sz=a("a"),sZo=o("from_pretrained()"),lZo=o(" to load the model weights."),iZo=l(),F(S5.$$.fragment),dZo=l(),co=a("div"),F(Ey.$$.fragment),cZo=l(),Z_e=a("p"),fZo=o("Instantiate one of the model classes of the library (with a audio classification head) from a pretrained model."),mZo=l(),Ga=a("p"),gZo=o("The model class to instantiate is selected based on the "),eue=a("code"),hZo=o("model_type"),pZo=o(` property of the config object (either
passed as an argument or loaded from `),oue=a("code"),_Zo=o("pretrained_model_name_or_path"),uZo=o(` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),rue=a("code"),bZo=o("pretrained_model_name_or_path"),vZo=o(":"),FZo=l(),Se=a("ul"),R5=a("li"),tue=a("strong"),TZo=o("data2vec-audio"),MZo=o(" \u2014 "),lz=a("a"),EZo=o("Data2VecAudioForSequenceClassification"),CZo=o(" (Data2VecAudio model)"),wZo=l(),P5=a("li"),aue=a("strong"),AZo=o("hubert"),yZo=o(" \u2014 "),iz=a("a"),LZo=o("HubertForSequenceClassification"),xZo=o(" (Hubert model)"),$Zo=l(),B5=a("li"),nue=a("strong"),kZo=o("sew"),SZo=o(" \u2014 "),dz=a("a"),RZo=o("SEWForSequenceClassification"),PZo=o(" (SEW model)"),BZo=l(),I5=a("li"),sue=a("strong"),IZo=o("sew-d"),NZo=o(" \u2014 "),cz=a("a"),qZo=o("SEWDForSequenceClassification"),jZo=o(" (SEW-D model)"),DZo=l(),N5=a("li"),lue=a("strong"),GZo=o("unispeech"),OZo=o(" \u2014 "),fz=a("a"),VZo=o("UniSpeechForSequenceClassification"),XZo=o(" (UniSpeech model)"),zZo=l(),q5=a("li"),iue=a("strong"),WZo=o("unispeech-sat"),QZo=o(" \u2014 "),mz=a("a"),HZo=o("UniSpeechSatForSequenceClassification"),UZo=o(" (UniSpeechSat model)"),JZo=l(),j5=a("li"),due=a("strong"),YZo=o("wav2vec2"),KZo=o(" \u2014 "),gz=a("a"),ZZo=o("Wav2Vec2ForSequenceClassification"),eer=o(" (Wav2Vec2 model)"),oer=l(),D5=a("li"),cue=a("strong"),rer=o("wav2vec2-conformer"),ter=o(" \u2014 "),hz=a("a"),aer=o("Wav2Vec2ConformerForSequenceClassification"),ner=o(" (Wav2Vec2-Conformer model)"),ser=l(),G5=a("li"),fue=a("strong"),ler=o("wavlm"),ier=o(" \u2014 "),pz=a("a"),der=o("WavLMForSequenceClassification"),cer=o(" (WavLM model)"),fer=l(),O5=a("p"),mer=o("The model is set in evaluation mode by default using "),mue=a("code"),ger=o("model.eval()"),her=o(` (so for instance, dropout modules are
deactivated). To train the model, you should first set it back in training mode with `),gue=a("code"),per=o("model.train()"),_er=l(),F(V5.$$.fragment),Mje=l(),hd=a("h2"),X5=a("a"),hue=a("span"),F(Cy.$$.fragment),uer=l(),pue=a("span"),ber=o("AutoModelForAudioFrameClassification"),Eje=l(),Oo=a("div"),F(wy.$$.fragment),ver=l(),pd=a("p"),Fer=o(`This is a generic model class that will be instantiated as one of the model classes of the library (with a audio frame (token) classification head) when created
with the `),_z=a("a"),Ter=o("from_pretrained()"),Mer=o(" class method or the "),uz=a("a"),Eer=o("from_config()"),Cer=o(` class
method.`),wer=l(),Ay=a("p"),Aer=o("This class cannot be instantiated directly using "),_ue=a("code"),yer=o("__init__()"),Ler=o(" (throws an error)."),xer=l(),bt=a("div"),F(yy.$$.fragment),$er=l(),uue=a("p"),ker=o("Instantiates one of the model classes of the library (with a audio frame (token) classification head) from a configuration."),Ser=l(),_d=a("p"),Rer=o(`Note:
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model\u2019s configuration. Use `),bz=a("a"),Ier=o("from_pretrained()"),Ner=o(" to load the model weights."),qer=l(),F(z5.$$.fragment),jer=l(),fo=a("div"),F(Ly.$$.fragment),Der=l(),vue=a("p"),Ger=o("Instantiate one of the model classes of the library (with a audio frame (token) classification head) from a pretrained model."),Oer=l(),Oa=a("p"),Ver=o("The model class to instantiate is selected based on the "),Fue=a("code"),Xer=o("model_type"),zer=o(` property of the config object (either
passed as an argument or loaded from `),Tue=a("code"),Wer=o("pretrained_model_name_or_path"),Qer=o(` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),Mue=a("code"),Her=o("pretrained_model_name_or_path"),Uer=o(":"),Jer=l(),Kr=a("ul"),W5=a("li"),Eue=a("strong"),Yer=o("data2vec-audio"),Ker=o(" \u2014 "),vz=a("a"),Zer=o("Data2VecAudioForAudioFrameClassification"),eor=o(" (Data2VecAudio model)"),oor=l(),Q5=a("li"),Cue=a("strong"),ror=o("unispeech-sat"),tor=o(" \u2014 "),Fz=a("a"),aor=o("UniSpeechSatForAudioFrameClassification"),nor=o(" (UniSpeechSat model)"),sor=l(),H5=a("li"),wue=a("strong"),lor=o("wav2vec2"),ior=o(" \u2014 "),Tz=a("a"),dor=o("Wav2Vec2ForAudioFrameClassification"),cor=o(" (Wav2Vec2 model)"),mor=l(),U5=a("li"),Aue=a("strong"),gor=o("wav2vec2-conformer"),hor=o(" \u2014 "),Mz=a("a"),por=o("Wav2Vec2ConformerForAudioFrameClassification"),_or=o(" (Wav2Vec2-Conformer model)"),uor=l(),J5=a("li"),yue=a("strong"),bor=o("wavlm"),vor=o(" \u2014 "),Ez=a("a"),For=o("WavLMForAudioFrameClassification"),Tor=o(" (WavLM model)"),Mor=l(),Y5=a("p"),Eor=o("The model is set in evaluation mode by default using "),Lue=a("code"),Cor=o("model.eval()"),wor=o(` (so for instance, dropout modules are
deactivated). To train the model, you should first set it back in training mode with `),xue=a("code"),Aor=o("model.train()"),yor=l(),F(K5.$$.fragment),Cje=l(),ud=a("h2"),Z5=a("a"),$ue=a("span"),F(xy.$$.fragment),Lor=l(),kue=a("span"),xor=o("AutoModelForCTC"),wje=l(),Vo=a("div"),F($y.$$.fragment),$or=l(),bd=a("p"),kor=o(`This is a generic model class that will be instantiated as one of the model classes of the library (with a connectionist temporal classification head) when created
with the `),Cz=a("a"),Sor=o("from_pretrained()"),Ror=o(" class method or the "),wz=a("a"),Por=o("from_config()"),Bor=o(` class
method.`),Ior=l(),ky=a("p"),Nor=o("This class cannot be instantiated directly using "),Sue=a("code"),qor=o("__init__()"),jor=o(" (throws an error)."),Dor=l(),vt=a("div"),F(Sy.$$.fragment),Gor=l(),Rue=a("p"),Oor=o("Instantiates one of the model classes of the library (with a connectionist temporal classification head) from a configuration."),Vor=l(),vd=a("p"),Xor=o(`Note:
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model\u2019s configuration. Use `),Az=a("a"),Qor=o("from_pretrained()"),Hor=o(" to load the model weights."),Uor=l(),F(eF.$$.fragment),Jor=l(),mo=a("div"),F(Ry.$$.fragment),Yor=l(),Bue=a("p"),Kor=o("Instantiate one of the model classes of the library (with a connectionist temporal classification head) from a pretrained model."),Zor=l(),Va=a("p"),err=o("The model class to instantiate is selected based on the "),Iue=a("code"),orr=o("model_type"),rrr=o(` property of the config object (either
passed as an argument or loaded from `),Nue=a("code"),trr=o("pretrained_model_name_or_path"),arr=o(` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),que=a("code"),nrr=o("pretrained_model_name_or_path"),srr=o(":"),lrr=l(),Re=a("ul"),oF=a("li"),jue=a("strong"),irr=o("data2vec-audio"),drr=o(" \u2014 "),yz=a("a"),crr=o("Data2VecAudioForCTC"),frr=o(" (Data2VecAudio model)"),mrr=l(),rF=a("li"),Due=a("strong"),grr=o("hubert"),hrr=o(" \u2014 "),Lz=a("a"),prr=o("HubertForCTC"),_rr=o(" (Hubert model)"),urr=l(),tF=a("li"),Gue=a("strong"),brr=o("sew"),vrr=o(" \u2014 "),xz=a("a"),Frr=o("SEWForCTC"),Trr=o(" (SEW model)"),Mrr=l(),aF=a("li"),Oue=a("strong"),Err=o("sew-d"),Crr=o(" \u2014 "),$z=a("a"),wrr=o("SEWDForCTC"),Arr=o(" (SEW-D model)"),yrr=l(),nF=a("li"),Vue=a("strong"),Lrr=o("unispeech"),xrr=o(" \u2014 "),kz=a("a"),$rr=o("UniSpeechForCTC"),krr=o(" (UniSpeech model)"),Srr=l(),sF=a("li"),Xue=a("strong"),Rrr=o("unispeech-sat"),Prr=o(" \u2014 "),Sz=a("a"),Brr=o("UniSpeechSatForCTC"),Irr=o(" (UniSpeechSat model)"),Nrr=l(),lF=a("li"),zue=a("strong"),qrr=o("wav2vec2"),jrr=o(" \u2014 "),Rz=a("a"),Drr=o("Wav2Vec2ForCTC"),Grr=o(" (Wav2Vec2 model)"),Orr=l(),iF=a("li"),Wue=a("strong"),Vrr=o("wav2vec2-conformer"),Xrr=o(" \u2014 "),Pz=a("a"),zrr=o("Wav2Vec2ConformerForCTC"),Wrr=o(" (Wav2Vec2-Conformer model)"),Qrr=l(),dF=a("li"),Que=a("strong"),Hrr=o("wavlm"),Urr=o(" \u2014 "),Bz=a("a"),Jrr=o("WavLMForCTC"),Yrr=o(" (WavLM model)"),Krr=l(),cF=a("p"),Zrr=o("The model is set in evaluation mode by default using "),Hue=a("code"),etr=o("model.eval()"),otr=o(` (so for instance, dropout modules are
deactivated). To train the model, you should first set it back in training mode with `),Uue=a("code"),rtr=o("model.train()"),ttr=l(),F(fF.$$.fragment),Aje=l(),Fd=a("h2"),mF=a("a"),Jue=a("span"),F(Py.$$.fragment),atr=l(),Yue=a("span"),ntr=o("AutoModelForSpeechSeq2Seq"),yje=l(),Xo=a("div"),F(By.$$.fragment),str=l(),Td=a("p"),ltr=o(`This is a generic model class that will be instantiated as one of the model classes of the library (with a sequence-to-sequence speech-to-text modeling head) when created
with the `),Iz=a("a"),itr=o("from_pretrained()"),dtr=o(" class method or the "),Nz=a("a"),ctr=o("from_config()"),ftr=o(` class
method.`),mtr=l(),Iy=a("p"),gtr=o("This class cannot be instantiated directly using "),Kue=a("code"),htr=o("__init__()"),ptr=o(" (throws an error)."),_tr=l(),Ft=a("div"),F(Ny.$$.fragment),utr=l(),Zue=a("p"),btr=o("Instantiates one of the model classes of the library (with a sequence-to-sequence speech-to-text modeling head) from a configuration."),vtr=l(),Md=a("p"),Ftr=o(`Note:
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model\u2019s configuration. Use `),qz=a("a"),Etr=o("from_pretrained()"),Ctr=o(" to load the model weights."),wtr=l(),F(gF.$$.fragment),Atr=l(),go=a("div"),F(qy.$$.fragment),ytr=l(),o1e=a("p"),Ltr=o("Instantiate one of the model classes of the library (with a sequence-to-sequence speech-to-text modeling head) from a pretrained model."),xtr=l(),Xa=a("p"),$tr=o("The model class to instantiate is selected based on the "),r1e=a("code"),ktr=o("model_type"),Str=o(` property of the config object (either
passed as an argument or loaded from `),t1e=a("code"),Rtr=o("pretrained_model_name_or_path"),Ptr=o(` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),a1e=a("code"),Btr=o("pretrained_model_name_or_path"),Itr=o(":"),Ntr=l(),jy=a("ul"),hF=a("li"),n1e=a("strong"),qtr=o("speech-encoder-decoder"),jtr=o(" \u2014 "),jz=a("a"),Dtr=o("SpeechEncoderDecoderModel"),Gtr=o(" (Speech Encoder decoder model)"),Otr=l(),pF=a("li"),s1e=a("strong"),Vtr=o("speech_to_text"),Xtr=o(" \u2014 "),Dz=a("a"),ztr=o("Speech2TextForConditionalGeneration"),Wtr=o(" (Speech2Text model)"),Qtr=l(),_F=a("p"),Htr=o("The model is set in evaluation mode by default using "),l1e=a("code"),Utr=o("model.eval()"),Jtr=o(` (so for instance, dropout modules are
deactivated). To train the model, you should first set it back in training mode with `),i1e=a("code"),Ytr=o("model.train()"),Ktr=l(),F(uF.$$.fragment),Lje=l(),Ed=a("h2"),bF=a("a"),d1e=a("span"),F(Dy.$$.fragment),Ztr=l(),c1e=a("span"),ear=o("AutoModelForAudioXVector"),xje=l(),zo=a("div"),F(Gy.$$.fragment),oar=l(),Cd=a("p"),rar=o(`This is a generic model class that will be instantiated as one of the model classes of the library (with a audio retrieval via x-vector head) when created
with the `),Gz=a("a"),tar=o("from_pretrained()"),aar=o(" class method or the "),Oz=a("a"),nar=o("from_config()"),sar=o(` class
method.`),lar=l(),Oy=a("p"),iar=o("This class cannot be instantiated directly using "),f1e=a("code"),dar=o("__init__()"),car=o(" (throws an error)."),far=l(),Tt=a("div"),F(Vy.$$.fragment),mar=l(),m1e=a("p"),gar=o("Instantiates one of the model classes of the library (with a audio retrieval via x-vector head) from a configuration."),har=l(),wd=a("p"),par=o(`Note:
Loading a model from its configuration file does `),g1e=a("strong"),_ar=o("not"),uar=o(` load the model weights. It only affects the
model\u2019s configuration. Use `),Vz=a("a"),bar=o("from_pretrained()"),Far=o(" to load the model weights."),Tar=l(),F(vF.$$.fragment),Mar=l(),ho=a("div"),F(Xy.$$.fragment),Ear=l(),h1e=a("p"),Car=o("Instantiate one of the model classes of the library (with a audio retrieval via x-vector head) from a pretrained model."),war=l(),za=a("p"),Aar=o("The model class to instantiate is selected based on the "),p1e=a("code"),yar=o("model_type"),Lar=o(` property of the config object (either
passed as an argument or loaded from `),_1e=a("code"),xar=o("pretrained_model_name_or_path"),$ar=o(` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),u1e=a("code"),kar=o("pretrained_model_name_or_path"),Sar=o(":"),Rar=l(),Zr=a("ul"),FF=a("li"),b1e=a("strong"),Par=o("data2vec-audio"),Bar=o(" \u2014 "),Xz=a("a"),Iar=o("Data2VecAudioForXVector"),Nar=o(" (Data2VecAudio model)"),qar=l(),TF=a("li"),v1e=a("strong"),jar=o("unispeech-sat"),Dar=o(" \u2014 "),zz=a("a"),Gar=o("UniSpeechSatForXVector"),Oar=o(" (UniSpeechSat model)"),Var=l(),MF=a("li"),F1e=a("strong"),Xar=o("wav2vec2"),zar=o(" \u2014 "),Wz=a("a"),War=o("Wav2Vec2ForXVector"),Qar=o(" (Wav2Vec2 model)"),Har=l(),EF=a("li"),T1e=a("strong"),Uar=o("wav2vec2-conformer"),Jar=o(" \u2014 "),Qz=a("a"),Yar=o("Wav2Vec2ConformerForXVector"),Kar=o(" (Wav2Vec2-Conformer model)"),Zar=l(),CF=a("li"),M1e=a("strong"),enr=o("wavlm"),onr=o(" \u2014 "),Hz=a("a"),rnr=o("WavLMForXVector"),tnr=o(" (WavLM model)"),anr=l(),wF=a("p"),nnr=o("The model is set in evaluation mode by default using "),E1e=a("code"),snr=o("model.eval()"),lnr=o(` (so for instance, dropout modules are
deactivated). To train the model, you should first set it back in training mode with `),C1e=a("code"),inr=o("model.train()"),dnr=l(),F(AF.$$.fragment),$je=l(),Ad=a("h2"),yF=a("a"),w1e=a("span"),F(zy.$$.fragment),cnr=l(),A1e=a("span"),fnr=o("AutoModelForMaskedImageModeling"),kje=l(),Wo=a("div"),F(Wy.$$.fragment),mnr=l(),yd=a("p"),gnr=o(`This is a generic model class that will be instantiated as one of the model classes of the library (with a masked image modeling head) when created
with the `),Uz=a("a"),hnr=o("from_pretrained()"),pnr=o(" class method or the "),Jz=a("a"),_nr=o("from_config()"),unr=o(` class
method.`),bnr=l(),Qy=a("p"),vnr=o("This class cannot be instantiated directly using "),y1e=a("code"),Fnr=o("__init__()"),Tnr=o(" (throws an error)."),Mnr=l(),Mt=a("div"),F(Hy.$$.fragment),Enr=l(),L1e=a("p"),Cnr=o("Instantiates one of the model classes of the library (with a masked image modeling head) from a configuration."),wnr=l(),Ld=a("p"),Anr=o(`Note:
Loading a model from its configuration file does `),x1e=a("strong"),ynr=o("not"),Lnr=o(` load the model weights. It only affects the
model\u2019s configuration. Use `),Yz=a("a"),xnr=o("from_pretrained()"),$nr=o(" to load the model weights."),knr=l(),F(LF.$$.fragment),Snr=l(),po=a("div"),F(Uy.$$.fragment),Rnr=l(),$1e=a("p"),Pnr=o("Instantiate one of the model classes of the library (with a masked image modeling head) from a pretrained model."),Bnr=l(),Wa=a("p"),Inr=o("The model class to instantiate is selected based on the "),k1e=a("code"),Nnr=o("model_type"),qnr=o(` property of the config object (either
passed as an argument or loaded from `),S1e=a("code"),jnr=o("pretrained_model_name_or_path"),Dnr=o(` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),R1e=a("code"),Gnr=o("pretrained_model_name_or_path"),Onr=o(":"),Vnr=l(),xd=a("ul"),xF=a("li"),P1e=a("strong"),Xnr=o("deit"),znr=o(" \u2014 "),Kz=a("a"),Wnr=o("DeiTForMaskedImageModeling"),Qnr=o(" (DeiT model)"),Hnr=l(),$F=a("li"),B1e=a("strong"),Unr=o("swin"),Jnr=o(" \u2014 "),Zz=a("a"),Ynr=o("SwinForMaskedImageModeling"),Knr=o(" (Swin Transformer model)"),Znr=l(),kF=a("li"),I1e=a("strong"),esr=o("vit"),osr=o(" \u2014 "),eW=a("a"),rsr=o("ViTForMaskedImageModeling"),tsr=o(" (ViT model)"),asr=l(),SF=a("p"),nsr=o("The model is set in evaluation mode by default using "),N1e=a("code"),ssr=o("model.eval()"),lsr=o(` (so for instance, dropout modules are
deactivated). To train the model, you should first set it back in training mode with `),q1e=a("code"),isr=o("model.train()"),dsr=l(),F(RF.$$.fragment),Sje=l(),$d=a("h2"),PF=a("a"),j1e=a("span"),F(Jy.$$.fragment),csr=l(),D1e=a("span"),fsr=o("AutoModelForObjectDetection"),Rje=l(),Qo=a("div"),F(Yy.$$.fragment),msr=l(),kd=a("p"),gsr=o(`This is a generic model class that will be instantiated as one of the model classes of the library (with a object detection head) when created
with the `),oW=a("a"),hsr=o("from_pretrained()"),psr=o(" class method or the "),rW=a("a"),_sr=o("from_config()"),usr=o(` class
method.`),bsr=l(),Ky=a("p"),vsr=o("This class cannot be instantiated directly using "),G1e=a("code"),Fsr=o("__init__()"),Tsr=o(" (throws an error)."),Msr=l(),Et=a("div"),F(Zy.$$.fragment),Esr=l(),O1e=a("p"),Csr=o("Instantiates one of the model classes of the library (with a object detection head) from a configuration."),wsr=l(),Sd=a("p"),Asr=o(`Note:
Loading a model from its configuration file does `),V1e=a("strong"),ysr=o("not"),Lsr=o(` load the model weights. It only affects the
model\u2019s configuration. Use `),tW=a("a"),xsr=o("from_pretrained()"),$sr=o(" to load the model weights."),ksr=l(),F(BF.$$.fragment),Ssr=l(),_o=a("div"),F(eL.$$.fragment),Rsr=l(),X1e=a("p"),Psr=o("Instantiate one of the model classes of the library (with a object detection head) from a pretrained model."),Bsr=l(),Qa=a("p"),Isr=o("The model class to instantiate is selected based on the "),z1e=a("code"),Nsr=o("model_type"),qsr=o(` property of the config object (either
passed as an argument or loaded from `),W1e=a("code"),jsr=o("pretrained_model_name_or_path"),Dsr=o(` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),Q1e=a("code"),Gsr=o("pretrained_model_name_or_path"),Osr=o(":"),Vsr=l(),oL=a("ul"),IF=a("li"),H1e=a("strong"),Xsr=o("detr"),zsr=o(" \u2014 "),aW=a("a"),Wsr=o("DetrForObjectDetection"),Qsr=o(" (DETR model)"),Hsr=l(),NF=a("li"),U1e=a("strong"),Usr=o("yolos"),Jsr=o(" \u2014 "),nW=a("a"),Ysr=o("YolosForObjectDetection"),Ksr=o(" (YOLOS model)"),Zsr=l(),qF=a("p"),elr=o("The model is set in evaluation mode by default using "),J1e=a("code"),olr=o("model.eval()"),rlr=o(` (so for instance, dropout modules are
deactivated). To train the model, you should first set it back in training mode with `),Y1e=a("code"),tlr=o("model.train()"),alr=l(),F(jF.$$.fragment),Pje=l(),Rd=a("h2"),DF=a("a"),K1e=a("span"),F(rL.$$.fragment),nlr=l(),Z1e=a("span"),slr=o("AutoModelForImageSegmentation"),Bje=l(),Ho=a("div"),F(tL.$$.fragment),llr=l(),Pd=a("p"),ilr=o(`This is a generic model class that will be instantiated as one of the model classes of the library (with a image segmentation head) when created
with the `),sW=a("a"),dlr=o("from_pretrained()"),clr=o(" class method or the "),lW=a("a"),flr=o("from_config()"),mlr=o(` class
method.`),glr=l(),aL=a("p"),hlr=o("This class cannot be instantiated directly using "),ebe=a("code"),plr=o("__init__()"),_lr=o(" (throws an error)."),ulr=l(),Ct=a("div"),F(nL.$$.fragment),blr=l(),obe=a("p"),vlr=o("Instantiates one of the model classes of the library (with a image segmentation head) from a configuration."),Flr=l(),Bd=a("p"),Tlr=o(`Note:
Loading a model from its configuration file does `),rbe=a("strong"),Mlr=o("not"),Elr=o(` load the model weights. It only affects the
model\u2019s configuration. Use `),iW=a("a"),Clr=o("from_pretrained()"),wlr=o(" to load the model weights."),Alr=l(),F(GF.$$.fragment),ylr=l(),uo=a("div"),F(sL.$$.fragment),Llr=l(),tbe=a("p"),xlr=o("Instantiate one of the model classes of the library (with a image segmentation head) from a pretrained model."),$lr=l(),Ha=a("p"),klr=o("The model class to instantiate is selected based on the "),abe=a("code"),Slr=o("model_type"),Rlr=o(` property of the config object (either
passed as an argument or loaded from `),nbe=a("code"),Plr=o("pretrained_model_name_or_path"),Blr=o(` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),sbe=a("code"),Ilr=o("pretrained_model_name_or_path"),Nlr=o(":"),qlr=l(),lbe=a("ul"),OF=a("li"),ibe=a("strong"),jlr=o("detr"),Dlr=o(" \u2014 "),dW=a("a"),Glr=o("DetrForSegmentation"),Olr=o(" (DETR model)"),Vlr=l(),VF=a("p"),Xlr=o("The model is set in evaluation mode by default using "),dbe=a("code"),zlr=o("model.eval()"),Wlr=o(` (so for instance, dropout modules are
deactivated). To train the model, you should first set it back in training mode with `),cbe=a("code"),Qlr=o("model.train()"),Hlr=l(),F(XF.$$.fragment),Ije=l(),Id=a("h2"),zF=a("a"),fbe=a("span"),F(lL.$$.fragment),Ulr=l(),mbe=a("span"),Jlr=o("AutoModelForSemanticSegmentation"),Nje=l(),Uo=a("div"),F(iL.$$.fragment),Ylr=l(),Nd=a("p"),Klr=o(`This is a generic model class that will be instantiated as one of the model classes of the library (with a semantic segmentation head) when created
with the `),cW=a("a"),Zlr=o("from_pretrained()"),eir=o(" class method or the "),fW=a("a"),oir=o("from_config()"),rir=o(` class
method.`),tir=l(),dL=a("p"),air=o("This class cannot be instantiated directly using "),gbe=a("code"),nir=o("__init__()"),sir=o(" (throws an error)."),lir=l(),wt=a("div"),F(cL.$$.fragment),iir=l(),hbe=a("p"),dir=o("Instantiates one of the model classes of the library (with a semantic segmentation head) from a configuration."),cir=l(),qd=a("p"),fir=o(`Note:
Loading a model from its configuration file does `),pbe=a("strong"),mir=o("not"),gir=o(` load the model weights. It only affects the
model\u2019s configuration. Use `),mW=a("a"),hir=o("from_pretrained()"),pir=o(" to load the model weights."),_ir=l(),F(WF.$$.fragment),uir=l(),bo=a("div"),F(fL.$$.fragment),bir=l(),_be=a("p"),vir=o("Instantiate one of the model classes of the library (with a semantic segmentation head) from a pretrained model."),Fir=l(),Ua=a("p"),Tir=o("The model class to instantiate is selected based on the "),ube=a("code"),Mir=o("model_type"),Eir=o(` property of the config object (either
passed as an argument or loaded from `),bbe=a("code"),Cir=o("pretrained_model_name_or_path"),wir=o(` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),vbe=a("code"),Air=o("pretrained_model_name_or_path"),yir=o(":"),Lir=l(),Ja=a("ul"),QF=a("li"),Fbe=a("strong"),xir=o("beit"),$ir=o(" \u2014 "),gW=a("a"),kir=o("BeitForSemanticSegmentation"),Sir=o(" (BEiT model)"),Rir=l(),HF=a("li"),Tbe=a("strong"),Pir=o("data2vec-vision"),Bir=o(" \u2014 "),hW=a("a"),Iir=o("Data2VecVisionForSemanticSegmentation"),Nir=o(" (Data2VecVision model)"),qir=l(),UF=a("li"),Mbe=a("strong"),jir=o("dpt"),Dir=o(" \u2014 "),pW=a("a"),Gir=o("DPTForSemanticSegmentation"),Oir=o(" (DPT model)"),Vir=l(),JF=a("li"),Ebe=a("strong"),Xir=o("segformer"),zir=o(" \u2014 "),_W=a("a"),Wir=o("SegformerForSemanticSegmentation"),Qir=o(" (SegFormer model)"),Hir=l(),YF=a("p"),Uir=o("The model is set in evaluation mode by default using "),Cbe=a("code"),Jir=o("model.eval()"),Yir=o(` (so for instance, dropout modules are
deactivated). To train the model, you should first set it back in training mode with `),wbe=a("code"),Kir=o("model.train()"),Zir=l(),F(KF.$$.fragment),qje=l(),jd=a("h2"),ZF=a("a"),Abe=a("span"),F(mL.$$.fragment),edr=l(),ybe=a("span"),odr=o("AutoModelForInstanceSegmentation"),jje=l(),Jo=a("div"),F(gL.$$.fragment),rdr=l(),Dd=a("p"),tdr=o(`This is a generic model class that will be instantiated as one of the model classes of the library (with a instance segmentation head) when created
with the `),uW=a("a"),adr=o("from_pretrained()"),ndr=o(" class method or the "),bW=a("a"),sdr=o("from_config()"),ldr=o(` class
method.`),idr=l(),hL=a("p"),ddr=o("This class cannot be instantiated directly using "),Lbe=a("code"),cdr=o("__init__()"),fdr=o(" (throws an error)."),mdr=l(),At=a("div"),F(pL.$$.fragment),gdr=l(),xbe=a("p"),hdr=o("Instantiates one of the model classes of the library (with a instance segmentation head) from a configuration."),pdr=l(),Gd=a("p"),_dr=o(`Note:
Loading a model from its configuration file does `),$be=a("strong"),udr=o("not"),bdr=o(` load the model weights. It only affects the
model\u2019s configuration. Use `),vW=a("a"),vdr=o("from_pretrained()"),Fdr=o(" to load the model weights."),Tdr=l(),F(eT.$$.fragment),Mdr=l(),vo=a("div"),F(_L.$$.fragment),Edr=l(),kbe=a("p"),Cdr=o("Instantiate one of the model classes of the library (with a instance segmentation head) from a pretrained model."),wdr=l(),Ya=a("p"),Adr=o("The model class to instantiate is selected based on the "),Sbe=a("code"),ydr=o("model_type"),Ldr=o(` property of the config object (either
passed as an argument or loaded from `),Rbe=a("code"),xdr=o("pretrained_model_name_or_path"),$dr=o(` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),Pbe=a("code"),kdr=o("pretrained_model_name_or_path"),Sdr=o(":"),Rdr=l(),Bbe=a("ul"),oT=a("li"),Ibe=a("strong"),Pdr=o("maskformer"),Bdr=o(" \u2014 "),FW=a("a"),Idr=o("MaskFormerForInstanceSegmentation"),Ndr=o(" (MaskFormer model)"),qdr=l(),rT=a("p"),jdr=o("The model is set in evaluation mode by default using "),Nbe=a("code"),Ddr=o("model.eval()"),Gdr=o(` (so for instance, dropout modules are
deactivated). To train the model, you should first set it back in training mode with `),qbe=a("code"),Odr=o("model.train()"),Vdr=l(),F(tT.$$.fragment),Dje=l(),Od=a("h2"),aT=a("a"),jbe=a("span"),F(uL.$$.fragment),Xdr=l(),Dbe=a("span"),zdr=o("TFAutoModel"),Gje=l(),Yo=a("div"),F(bL.$$.fragment),Wdr=l(),Vd=a("p"),Qdr=o(`This is a generic model class that will be instantiated as one of the base model classes of the library when created
with the `),TW=a("a"),Hdr=o("from_pretrained()"),Udr=o(" class method or the "),MW=a("a"),Jdr=o("from_config()"),Ydr=o(` class
method.`),Kdr=l(),vL=a("p"),Zdr=o("This class cannot be instantiated directly using "),Gbe=a("code"),ecr=o("__init__()"),ocr=o(" (throws an error)."),rcr=l(),yt=a("div"),F(FL.$$.fragment),tcr=l(),Obe=a("p"),acr=o("Instantiates one of the base model classes of the library from a configuration."),ncr=l(),Xd=a("p"),scr=o(`Note:
Loading a model from its configuration file does `),Vbe=a("strong"),lcr=o("not"),icr=o(` load the model weights. It only affects the
model\u2019s configuration. Use `),EW=a("a"),dcr=o("from_pretrained()"),ccr=o(" to load the model weights."),fcr=l(),F(nT.$$.fragment),mcr=l(),wr=a("div"),F(TL.$$.fragment),gcr=l(),Xbe=a("p"),hcr=o("Instantiate one of the base model classes of the library from a pretrained model."),pcr=l(),Ka=a("p"),_cr=o("The model class to instantiate is selected based on the "),zbe=a("code"),ucr=o("model_type"),bcr=o(` property of the config object (either
passed as an argument or loaded from `),Wbe=a("code"),vcr=o("pretrained_model_name_or_path"),Fcr=o(` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),Qbe=a("code"),Tcr=o("pretrained_model_name_or_path"),Mcr=o(":"),Ecr=l(),q=a("ul"),sT=a("li"),Hbe=a("strong"),Ccr=o("albert"),wcr=o(" \u2014 "),CW=a("a"),Acr=o("TFAlbertModel"),ycr=o(" (ALBERT model)"),Lcr=l(),lT=a("li"),Ube=a("strong"),xcr=o("bart"),$cr=o(" \u2014 "),wW=a("a"),kcr=o("TFBartModel"),Scr=o(" (BART model)"),Rcr=l(),iT=a("li"),Jbe=a("strong"),Pcr=o("bert"),Bcr=o(" \u2014 "),AW=a("a"),Icr=o("TFBertModel"),Ncr=o(" (BERT model)"),qcr=l(),dT=a("li"),Ybe=a("strong"),jcr=o("blenderbot"),Dcr=o(" \u2014 "),yW=a("a"),Gcr=o("TFBlenderbotModel"),Ocr=o(" (Blenderbot model)"),Vcr=l(),cT=a("li"),Kbe=a("strong"),Xcr=o("blenderbot-small"),zcr=o(" \u2014 "),LW=a("a"),Wcr=o("TFBlenderbotSmallModel"),Qcr=o(" (BlenderbotSmall model)"),Hcr=l(),fT=a("li"),Zbe=a("strong"),Ucr=o("camembert"),Jcr=o(" \u2014 "),xW=a("a"),Ycr=o("TFCamembertModel"),Kcr=o(" (CamemBERT model)"),Zcr=l(),mT=a("li"),e2e=a("strong"),efr=o("clip"),ofr=o(" \u2014 "),$W=a("a"),rfr=o("TFCLIPModel"),tfr=o(" (CLIP model)"),afr=l(),gT=a("li"),o2e=a("strong"),nfr=o("convbert"),sfr=o(" \u2014 "),kW=a("a"),lfr=o("TFConvBertModel"),ifr=o(" (ConvBERT model)"),dfr=l(),hT=a("li"),r2e=a("strong"),cfr=o("convnext"),ffr=o(" \u2014 "),SW=a("a"),mfr=o("TFConvNextModel"),gfr=o(" (ConvNeXT model)"),hfr=l(),pT=a("li"),t2e=a("strong"),pfr=o("ctrl"),_fr=o(" \u2014 "),RW=a("a"),ufr=o("TFCTRLModel"),bfr=o(" (CTRL model)"),vfr=l(),_T=a("li"),a2e=a("strong"),Ffr=o("data2vec-vision"),Tfr=o(" \u2014 "),PW=a("a"),Mfr=o("TFData2VecVisionModel"),Efr=o(" (Data2VecVision model)"),Cfr=l(),uT=a("li"),n2e=a("strong"),wfr=o("deberta"),Afr=o(" \u2014 "),BW=a("a"),yfr=o("TFDebertaModel"),Lfr=o(" (DeBERTa model)"),xfr=l(),bT=a("li"),s2e=a("strong"),$fr=o("deberta-v2"),kfr=o(" \u2014 "),IW=a("a"),Sfr=o("TFDebertaV2Model"),Rfr=o(" (DeBERTa-v2 model)"),Pfr=l(),vT=a("li"),l2e=a("strong"),Bfr=o("distilbert"),Ifr=o(" \u2014 "),NW=a("a"),Nfr=o("TFDistilBertModel"),qfr=o(" (DistilBERT model)"),jfr=l(),FT=a("li"),i2e=a("strong"),Dfr=o("dpr"),Gfr=o(" \u2014 "),qW=a("a"),Ofr=o("TFDPRQuestionEncoder"),Vfr=o(" (DPR model)"),Xfr=l(),TT=a("li"),d2e=a("strong"),zfr=o("electra"),Wfr=o(" \u2014 "),jW=a("a"),Qfr=o("TFElectraModel"),Hfr=o(" (ELECTRA model)"),Ufr=l(),MT=a("li"),c2e=a("strong"),Jfr=o("flaubert"),Yfr=o(" \u2014 "),DW=a("a"),Kfr=o("TFFlaubertModel"),Zfr=o(" (FlauBERT model)"),emr=l(),Ns=a("li"),f2e=a("strong"),omr=o("funnel"),rmr=o(" \u2014 "),GW=a("a"),tmr=o("TFFunnelModel"),amr=o(" or "),OW=a("a"),nmr=o("TFFunnelBaseModel"),smr=o(" (Funnel Transformer model)"),lmr=l(),ET=a("li"),m2e=a("strong"),imr=o("gpt2"),dmr=o(" \u2014 "),VW=a("a"),cmr=o("TFGPT2Model"),fmr=o(" (OpenAI GPT-2 model)"),mmr=l(),CT=a("li"),g2e=a("strong"),gmr=o("gptj"),hmr=o(" \u2014 "),XW=a("a"),pmr=o("TFGPTJModel"),_mr=o(" (GPT-J model)"),umr=l(),wT=a("li"),h2e=a("strong"),bmr=o("hubert"),vmr=o(" \u2014 "),zW=a("a"),Fmr=o("TFHubertModel"),Tmr=o(" (Hubert 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`),$n=n(An,"A",{href:!0});var rCe=s($n);BA=r(rCe,"AutoTokenizer"),rCe.forEach(t),ui=r(An," will directly create a class of the relevant architecture. For instance"),An.forEach(t),bf=i(f),T(Ca.$$.fragment,f),Qe=i(f),Ae=n(f,"P",{});var Vx=s(Ae);ok=r(Vx,"will create a model that is an instance of "),bi=n(Vx,"A",{href:!0});var tCe=s(bi);rk=r(tCe,"BertModel"),tCe.forEach(t),tk=r(Vx,"."),Vx.forEach(t),Eo=i(f),wa=n(f,"P",{});var Xx=s(wa);ak=r(Xx,"There is one class of "),vf=n(Xx,"CODE",{});var aCe=s(vf);nk=r(aCe,"AutoModel"),aCe.forEach(t),WOe=r(Xx," for each task, and for each backend (PyTorch, TensorFlow, or Flax)."),Xx.forEach(t),Bqe=i(f),vi=n(f,"H2",{class:!0});var zx=s(vi);Ff=n(zx,"A",{id:!0,class:!0,href:!0});var nCe=s(Ff);Aoe=n(nCe,"SPAN",{});var sCe=s(Aoe);T(IA.$$.fragment,sCe),sCe.forEach(t),nCe.forEach(t),QOe=i(zx),yoe=n(zx,"SPAN",{});var lCe=s(yoe);HOe=r(lCe,"Extending the Auto Classes"),lCe.forEach(t),zx.forEach(t),Iqe=i(f),kn=n(f,"P",{});var gf=s(kn);UOe=r(gf,`Each of the auto classes has a method to be extended with your custom classes. For instance, if you have defined a
custom class of model `),Loe=n(gf,"CODE",{});var iCe=s(Loe);JOe=r(iCe,"NewModel"),iCe.forEach(t),YOe=r(gf,", make sure you have a "),xoe=n(gf,"CODE",{});var dCe=s(xoe);KOe=r(dCe,"NewModelConfig"),dCe.forEach(t),ZOe=r(gf,` then you can add those to the auto
classes like this:`),gf.forEach(t),Nqe=i(f),T(NA.$$.fragment,f),qqe=i(f),sk=n(f,"P",{});var cCe=s(sk);eVe=r(cCe,"You will then be able to use the auto classes like you would usually do!"),cCe.forEach(t),jqe=i(f),T(Tf.$$.fragment,f),Dqe=i(f),Fi=n(f,"H2",{class:!0});var Wx=s(Fi);Mf=n(Wx,"A",{id:!0,class:!0,href:!0});var fCe=s(Mf);$oe=n(fCe,"SPAN",{});var mCe=s($oe);T(qA.$$.fragment,mCe),mCe.forEach(t),fCe.forEach(t),oVe=i(Wx),koe=n(Wx,"SPAN",{});var gCe=s(koe);rVe=r(gCe,"AutoConfig"),gCe.forEach(t),Wx.forEach(t),Gqe=i(f),Co=n(f,"DIV",{class:!0});var et=s(Co);T(jA.$$.fragment,et),tVe=i(et),DA=n(et,"P",{});var Qx=s(DA);aVe=r(Qx,`This is a generic configuration class that will be instantiated as one of the configuration classes of the library
when created with the `),lk=n(Qx,"A",{href:!0});var hCe=s(lk);nVe=r(hCe,"from_pretrained()"),hCe.forEach(t),sVe=r(Qx," class method."),Qx.forEach(t),lVe=i(et),GA=n(et,"P",{});var Hx=s(GA);iVe=r(Hx,"This class cannot be instantiated directly using "),Soe=n(Hx,"CODE",{});var pCe=s(Soe);dVe=r(pCe,"__init__()"),pCe.forEach(t),cVe=r(Hx," (throws an error)."),Hx.forEach(t),fVe=i(et),Er=n(et,"DIV",{class:!0});var ot=s(Er);T(OA.$$.fragment,ot),mVe=i(ot),Roe=n(ot,"P",{});var _Ce=s(Roe);gVe=r(_Ce,"Instantiate one of the configuration classes of the library from a pretrained model configuration."),_Ce.forEach(t),hVe=i(ot),Ti=n(ot,"P",{});var hf=s(Ti);pVe=r(hf,"The configuration class to instantiate is selected based on the "),Poe=n(hf,"CODE",{});var uCe=s(Poe);_Ve=r(uCe,"model_type"),uCe.forEach(t),uVe=r(hf,` property of the config object that
is loaded, or when it\u2019s missing, by falling back to using pattern matching on `),Boe=n(hf,"CODE",{});var bCe=s(Boe);bVe=r(bCe,"pretrained_model_name_or_path"),bCe.forEach(t),vVe=r(hf,":"),hf.forEach(t),FVe=i(ot),A=n(ot,"UL",{});var y=s(A);Ef=n(y,"LI",{});var p0=s(Ef);Ioe=n(p0,"STRONG",{});var vCe=s(Ioe);TVe=r(vCe,"albert"),vCe.forEach(t),MVe=r(p0," \u2014 "),ik=n(p0,"A",{href:!0});var FCe=s(ik);EVe=r(FCe,"AlbertConfig"),FCe.forEach(t),CVe=r(p0," (ALBERT model)"),p0.forEach(t),wVe=i(y),Cf=n(y,"LI",{});var _0=s(Cf);Noe=n(_0,"STRONG",{});var TCe=s(Noe);AVe=r(TCe,"bart"),TCe.forEach(t),yVe=r(_0," \u2014 "),dk=n(_0,"A",{href:!0});var MCe=s(dk);LVe=r(MCe,"BartConfig"),MCe.forEach(t),xVe=r(_0," (BART model)"),_0.forEach(t),$Ve=i(y),wf=n(y,"LI",{});var u0=s(wf);qoe=n(u0,"STRONG",{});var ECe=s(qoe);kVe=r(ECe,"beit"),ECe.forEach(t),SVe=r(u0," \u2014 "),ck=n(u0,"A",{href:!0});var CCe=s(ck);RVe=r(CCe,"BeitConfig"),CCe.forEach(t),PVe=r(u0," (BEiT 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SCe=s(hk);ZVe=r(SCe,"BigBirdPegasusConfig"),SCe.forEach(t),eXe=r(T0," (BigBird-Pegasus model)"),T0.forEach(t),oXe=i(y),$f=n(y,"LI",{});var M0=s($f);Voe=n(M0,"STRONG",{});var RCe=s(Voe);rXe=r(RCe,"blenderbot"),RCe.forEach(t),tXe=r(M0," \u2014 "),pk=n(M0,"A",{href:!0});var PCe=s(pk);aXe=r(PCe,"BlenderbotConfig"),PCe.forEach(t),nXe=r(M0," (Blenderbot model)"),M0.forEach(t),sXe=i(y),kf=n(y,"LI",{});var E0=s(kf);Xoe=n(E0,"STRONG",{});var BCe=s(Xoe);lXe=r(BCe,"blenderbot-small"),BCe.forEach(t),iXe=r(E0," \u2014 "),_k=n(E0,"A",{href:!0});var ICe=s(_k);dXe=r(ICe,"BlenderbotSmallConfig"),ICe.forEach(t),cXe=r(E0," (BlenderbotSmall model)"),E0.forEach(t),fXe=i(y),Sf=n(y,"LI",{});var C0=s(Sf);zoe=n(C0,"STRONG",{});var NCe=s(zoe);mXe=r(NCe,"camembert"),NCe.forEach(t),gXe=r(C0," \u2014 "),uk=n(C0,"A",{href:!0});var qCe=s(uk);hXe=r(qCe,"CamembertConfig"),qCe.forEach(t),pXe=r(C0," (CamemBERT model)"),C0.forEach(t),_Xe=i(y),Rf=n(y,"LI",{});var w0=s(Rf);Woe=n(w0,"STRONG",{});var 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x0=s(Nf);Joe=n(x0,"STRONG",{});var QCe=s(Joe);NXe=r(QCe,"ctrl"),QCe.forEach(t),qXe=r(x0," \u2014 "),Mk=n(x0,"A",{href:!0});var HCe=s(Mk);jXe=r(HCe,"CTRLConfig"),HCe.forEach(t),DXe=r(x0," (CTRL model)"),x0.forEach(t),GXe=i(y),qf=n(y,"LI",{});var $0=s(qf);Yoe=n($0,"STRONG",{});var UCe=s(Yoe);OXe=r(UCe,"cvt"),UCe.forEach(t),VXe=r($0," \u2014 "),Ek=n($0,"A",{href:!0});var JCe=s(Ek);XXe=r(JCe,"CvtConfig"),JCe.forEach(t),zXe=r($0," (CvT model)"),$0.forEach(t),WXe=i(y),jf=n(y,"LI",{});var k0=s(jf);Koe=n(k0,"STRONG",{});var YCe=s(Koe);QXe=r(YCe,"data2vec-audio"),YCe.forEach(t),HXe=r(k0," \u2014 "),Ck=n(k0,"A",{href:!0});var KCe=s(Ck);UXe=r(KCe,"Data2VecAudioConfig"),KCe.forEach(t),JXe=r(k0," (Data2VecAudio model)"),k0.forEach(t),YXe=i(y),Df=n(y,"LI",{});var S0=s(Df);Zoe=n(S0,"STRONG",{});var ZCe=s(Zoe);KXe=r(ZCe,"data2vec-text"),ZCe.forEach(t),ZXe=r(S0," \u2014 "),wk=n(S0,"A",{href:!0});var e3e=s(wk);eze=r(e3e,"Data2VecTextConfig"),e3e.forEach(t),oze=r(S0," (Data2VecText 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fet=s(fse);T(r6.$$.fragment,fet),fet.forEach(t),cet.forEach(t),kgo=i(HDe),mse=n(HDe,"SPAN",{});var met=s(mse);Sgo=r(met,"AutoProcessor"),met.forEach(t),HDe.forEach(t),Qqe=i(f),yo=n(f,"DIV",{class:!0});var Gs=s(yo);T(t6.$$.fragment,Gs),Rgo=i(Gs),a6=n(Gs,"P",{});var UDe=s(a6);Pgo=r(UDe,`This is a generic processor class that will be instantiated as one of the processor classes of the library when
created with the `),CI=n(UDe,"A",{href:!0});var get=s(CI);Bgo=r(get,"AutoProcessor.from_pretrained()"),get.forEach(t),Igo=r(UDe," class method."),UDe.forEach(t),Ngo=i(Gs),n6=n(Gs,"P",{});var JDe=s(n6);qgo=r(JDe,"This class cannot be instantiated directly using "),gse=n(JDe,"CODE",{});var het=s(gse);jgo=r(het,"__init__()"),het.forEach(t),Dgo=r(JDe," (throws an error)."),JDe.forEach(t),Ggo=i(Gs),Ue=n(Gs,"DIV",{class:!0});var ea=s(Ue);T(s6.$$.fragment,ea),Ogo=i(ea),hse=n(ea,"P",{});var pet=s(hse);Vgo=r(pet,"Instantiate one of the processor classes of the library from a pretrained model vocabulary."),pet.forEach(t),Xgo=i(ea),wi=n(ea,"P",{});var PZ=s(wi);zgo=r(PZ,"The processor class to instantiate is selected based on the "),pse=n(PZ,"CODE",{});var _et=s(pse);Wgo=r(_et,"model_type"),_et.forEach(t),Qgo=r(PZ,` property of the config object (either
passed as an argument or loaded from `),_se=n(PZ,"CODE",{});var uet=s(_se);Hgo=r(uet,"pretrained_model_name_or_path"),uet.forEach(t),Ugo=r(PZ," if possible):"),PZ.forEach(t),Jgo=i(ea),he=n(ea,"UL",{});var ue=s(he);Vh=n(ue,"LI",{});var Ywe=s(Vh);use=n(Ywe,"STRONG",{});var bet=s(use);Ygo=r(bet,"clip"),bet.forEach(t),Kgo=r(Ywe," \u2014 "),wI=n(Ywe,"A",{href:!0});var vet=s(wI);Zgo=r(vet,"CLIPProcessor"),vet.forEach(t),eho=r(Ywe," (CLIP model)"),Ywe.forEach(t),oho=i(ue),Xh=n(ue,"LI",{});var Kwe=s(Xh);bse=n(Kwe,"STRONG",{});var Fet=s(bse);rho=r(Fet,"flava"),Fet.forEach(t),tho=r(Kwe," \u2014 "),vse=n(Kwe,"CODE",{});var Tet=s(vse);aho=r(Tet,"FLAVAProcessor"),Tet.forEach(t),nho=r(Kwe," (FLAVA model)"),Kwe.forEach(t),sho=i(ue),zh=n(ue,"LI",{});var Zwe=s(zh);Fse=n(Zwe,"STRONG",{});var Met=s(Fse);lho=r(Met,"layoutlmv2"),Met.forEach(t),iho=r(Zwe," \u2014 "),AI=n(Zwe,"A",{href:!0});var Eet=s(AI);dho=r(Eet,"LayoutLMv2Processor"),Eet.forEach(t),cho=r(Zwe," (LayoutLMv2 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YDe=s(ip);T(l6.$$.fragment,YDe),$po=i(YDe),Bse=n(YDe,"P",{});var Ket=s(Bse);kpo=r(Ket,"Register a new processor for this class."),Ket.forEach(t),YDe.forEach(t),Gs.forEach(t),Hqe=i(f),Ai=n(f,"H2",{class:!0});var KDe=s(Ai);dp=n(KDe,"A",{id:!0,class:!0,href:!0});var Zet=s(dp);Ise=n(Zet,"SPAN",{});var eot=s(Ise);T(i6.$$.fragment,eot),eot.forEach(t),Zet.forEach(t),Spo=i(KDe),Nse=n(KDe,"SPAN",{});var oot=s(Nse);Rpo=r(oot,"AutoModel"),oot.forEach(t),KDe.forEach(t),Uqe=i(f),Lo=n(f,"DIV",{class:!0});var Os=s(Lo);T(d6.$$.fragment,Os),Ppo=i(Os),yi=n(Os,"P",{});var BZ=s(yi);Bpo=r(BZ,`This is a generic model class that will be instantiated as one of the base model classes of the library when created
with the `),GI=n(BZ,"A",{href:!0});var rot=s(GI);Ipo=r(rot,"from_pretrained()"),rot.forEach(t),Npo=r(BZ," class method or the "),OI=n(BZ,"A",{href:!0});var tot=s(OI);qpo=r(tot,"from_config()"),tot.forEach(t),jpo=r(BZ,` class
method.`),BZ.forEach(t),Dpo=i(Os),c6=n(Os,"P",{});var ZDe=s(c6);Gpo=r(ZDe,"This class cannot be instantiated directly using "),qse=n(ZDe,"CODE",{});var aot=s(qse);Opo=r(aot,"__init__()"),aot.forEach(t),Vpo=r(ZDe," (throws an error)."),ZDe.forEach(t),Xpo=i(Os),tt=n(Os,"DIV",{class:!0});var D0=s(tt);T(f6.$$.fragment,D0),zpo=i(D0),jse=n(D0,"P",{});var not=s(jse);Wpo=r(not,"Instantiates one of the base model classes of the library from a configuration."),not.forEach(t),Qpo=i(D0),Li=n(D0,"P",{});var IZ=s(Li);Hpo=r(IZ,`Note:
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model\u2019s configuration. Use `),VI=n(IZ,"A",{href:!0});var lot=s(VI);Ypo=r(lot,"from_pretrained()"),lot.forEach(t),Kpo=r(IZ," to load the model weights."),IZ.forEach(t),Zpo=i(D0),T(cp.$$.fragment,D0),D0.forEach(t),e_o=i(Os),Je=n(Os,"DIV",{class:!0});var oa=s(Je);T(m6.$$.fragment,oa),o_o=i(oa),Gse=n(oa,"P",{});var iot=s(Gse);r_o=r(iot,"Instantiate one of the base model classes of the library from a pretrained model."),iot.forEach(t),t_o=i(oa),La=n(oa,"P",{});var G0=s(La);a_o=r(G0,"The model class to instantiate is selected based on the "),Ose=n(G0,"CODE",{});var dot=s(Ose);n_o=r(dot,"model_type"),dot.forEach(t),s_o=r(G0,` property of the config object (either
passed as an argument or loaded from `),Vse=n(G0,"CODE",{});var cot=s(Vse);l_o=r(cot,"pretrained_model_name_or_path"),cot.forEach(t),i_o=r(G0,` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),Xse=n(G0,"CODE",{});var fot=s(Xse);d_o=r(fot,"pretrained_model_name_or_path"),fot.forEach(t),c_o=r(G0,":"),G0.forEach(t),f_o=i(oa),x=n(oa,"UL",{});var $=s(x);fp=n($,"LI",{});var hAe=s(fp);zse=n(hAe,"STRONG",{});var mot=s(zse);m_o=r(mot,"albert"),mot.forEach(t),g_o=r(hAe," \u2014 "),XI=n(hAe,"A",{href:!0});var got=s(XI);h_o=r(got,"AlbertModel"),got.forEach(t),p_o=r(hAe," (ALBERT model)"),hAe.forEach(t),__o=i($),mp=n($,"LI",{});var pAe=s(mp);Wse=n(pAe,"STRONG",{});var hot=s(Wse);u_o=r(hot,"bart"),hot.forEach(t),b_o=r(pAe," \u2014 "),zI=n(pAe,"A",{href:!0});var pot=s(zI);v_o=r(pot,"BartModel"),pot.forEach(t),F_o=r(pAe," (BART model)"),pAe.forEach(t),T_o=i($),gp=n($,"LI",{});var _Ae=s(gp);Qse=n(_Ae,"STRONG",{});var _ot=s(Qse);M_o=r(_ot,"beit"),_ot.forEach(t),E_o=r(_Ae," \u2014 "),WI=n(_Ae,"A",{href:!0});var uot=s(WI);C_o=r(uot,"BeitModel"),uot.forEach(t),w_o=r(_Ae," (BEiT model)"),_Ae.forEach(t),A_o=i($),hp=n($,"LI",{});var 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with the `),Dq=n(NZ,"A",{href:!0});var int=s(Dq);KTo=r(int,"from_pretrained()"),int.forEach(t),ZTo=r(NZ," class method or the "),Gq=n(NZ,"A",{href:!0});var dnt=s(Gq);e8o=r(dnt,"from_config()"),dnt.forEach(t),o8o=r(NZ,` class
method.`),NZ.forEach(t),r8o=i(Vs),p6=n(Vs,"P",{});var oGe=s(p6);t8o=r(oGe,"This class cannot be instantiated directly using "),Xie=n(oGe,"CODE",{});var cnt=s(Xie);a8o=r(cnt,"__init__()"),cnt.forEach(t),n8o=r(oGe," (throws an error)."),oGe.forEach(t),s8o=i(Vs),at=n(Vs,"DIV",{class:!0});var O0=s(at);T(_6.$$.fragment,O0),l8o=i(O0),zie=n(O0,"P",{});var fnt=s(zie);i8o=r(fnt,"Instantiates one of the model classes of the library (with a pretraining head) from a configuration."),fnt.forEach(t),d8o=i(O0),ki=n(O0,"P",{});var qZ=s(ki);c8o=r(qZ,`Note:
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model\u2019s configuration. Use `),Oq=n(qZ,"A",{href:!0});var gnt=s(Oq);g8o=r(gnt,"from_pretrained()"),gnt.forEach(t),h8o=r(qZ," to load the model weights."),qZ.forEach(t),p8o=i(O0),T(iu.$$.fragment,O0),O0.forEach(t),_8o=i(Vs),Ye=n(Vs,"DIV",{class:!0});var ra=s(Ye);T(u6.$$.fragment,ra),u8o=i(ra),Qie=n(ra,"P",{});var hnt=s(Qie);b8o=r(hnt,"Instantiate one of the model classes of the library (with a pretraining head) from a pretrained model."),hnt.forEach(t),v8o=i(ra),xa=n(ra,"P",{});var V0=s(xa);F8o=r(V0,"The model class to instantiate is selected based on the "),Hie=n(V0,"CODE",{});var pnt=s(Hie);T8o=r(pnt,"model_type"),pnt.forEach(t),M8o=r(V0,` property of the config object (either
passed as an argument or loaded from `),Uie=n(V0,"CODE",{});var _nt=s(Uie);E8o=r(_nt,"pretrained_model_name_or_path"),_nt.forEach(t),C8o=r(V0,` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),Jie=n(V0,"CODE",{});var unt=s(Jie);w8o=r(unt,"pretrained_model_name_or_path"),unt.forEach(t),A8o=r(V0,":"),V0.forEach(t),y8o=i(ra),G=n(ra,"UL",{});var O=s(G);du=n(O,"LI",{});var dye=s(du);Yie=n(dye,"STRONG",{});var bnt=s(Yie);L8o=r(bnt,"albert"),bnt.forEach(t),x8o=r(dye," \u2014 "),Vq=n(dye,"A",{href:!0});var vnt=s(Vq);$8o=r(vnt,"AlbertForPreTraining"),vnt.forEach(t),k8o=r(dye," (ALBERT model)"),dye.forEach(t),S8o=i(O),cu=n(O,"LI",{});var cye=s(cu);Kie=n(cye,"STRONG",{});var Fnt=s(Kie);R8o=r(Fnt,"bart"),Fnt.forEach(t),P8o=r(cye," \u2014 "),Xq=n(cye,"A",{href:!0});var Tnt=s(Xq);B8o=r(Tnt,"BartForConditionalGeneration"),Tnt.forEach(t),I8o=r(cye," (BART model)"),cye.forEach(t),N8o=i(O),fu=n(O,"LI",{});var fye=s(fu);Zie=n(fye,"STRONG",{});var Mnt=s(Zie);q8o=r(Mnt,"bert"),Mnt.forEach(t),j8o=r(fye," \u2014 "),zq=n(fye,"A",{href:!0});var Ent=s(zq);D8o=r(Ent,"BertForPreTraining"),Ent.forEach(t),G8o=r(fye," (BERT 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knt=s(Uq);s7o=r(knt,"Data2VecTextForMaskedLM"),knt.forEach(t),l7o=r(pye," (Data2VecText model)"),pye.forEach(t),i7o=i(O),_u=n(O,"LI",{});var _ye=s(_u);ade=n(_ye,"STRONG",{});var Snt=s(ade);d7o=r(Snt,"deberta"),Snt.forEach(t),c7o=r(_ye," \u2014 "),Jq=n(_ye,"A",{href:!0});var Rnt=s(Jq);f7o=r(Rnt,"DebertaForMaskedLM"),Rnt.forEach(t),m7o=r(_ye," (DeBERTa model)"),_ye.forEach(t),g7o=i(O),uu=n(O,"LI",{});var uye=s(uu);nde=n(uye,"STRONG",{});var Pnt=s(nde);h7o=r(Pnt,"deberta-v2"),Pnt.forEach(t),p7o=r(uye," \u2014 "),Yq=n(uye,"A",{href:!0});var Bnt=s(Yq);_7o=r(Bnt,"DebertaV2ForMaskedLM"),Bnt.forEach(t),u7o=r(uye," (DeBERTa-v2 model)"),uye.forEach(t),b7o=i(O),bu=n(O,"LI",{});var bye=s(bu);sde=n(bye,"STRONG",{});var Int=s(sde);v7o=r(Int,"distilbert"),Int.forEach(t),F7o=r(bye," \u2014 "),Kq=n(bye,"A",{href:!0});var Nnt=s(Kq);T7o=r(Nnt,"DistilBertForMaskedLM"),Nnt.forEach(t),M7o=r(bye," (DistilBERT model)"),bye.forEach(t),E7o=i(O),vu=n(O,"LI",{});var vye=s(vu);lde=n(vye,"STRONG",{});var qnt=s(lde);C7o=r(qnt,"electra"),qnt.forEach(t),w7o=r(vye," \u2014 "),Zq=n(vye,"A",{href:!0});var jnt=s(Zq);A7o=r(jnt,"ElectraForPreTraining"),jnt.forEach(t),y7o=r(vye," (ELECTRA model)"),vye.forEach(t),L7o=i(O),Fu=n(O,"LI",{});var Fye=s(Fu);ide=n(Fye,"STRONG",{});var Dnt=s(ide);x7o=r(Dnt,"flaubert"),Dnt.forEach(t),$7o=r(Fye," \u2014 "),ej=n(Fye,"A",{href:!0});var Gnt=s(ej);k7o=r(Gnt,"FlaubertWithLMHeadModel"),Gnt.forEach(t),S7o=r(Fye," (FlauBERT model)"),Fye.forEach(t),R7o=i(O),Tu=n(O,"LI",{});var Tye=s(Tu);dde=n(Tye,"STRONG",{});var Ont=s(dde);P7o=r(Ont,"flava"),Ont.forEach(t),B7o=r(Tye," \u2014 "),oj=n(Tye,"A",{href:!0});var Vnt=s(oj);I7o=r(Vnt,"FlavaForPreTraining"),Vnt.forEach(t),N7o=r(Tye," (FLAVA model)"),Tye.forEach(t),q7o=i(O),Mu=n(O,"LI",{});var Mye=s(Mu);cde=n(Mye,"STRONG",{});var Xnt=s(cde);j7o=r(Xnt,"fnet"),Xnt.forEach(t),D7o=r(Mye," \u2014 "),rj=n(Mye,"A",{href:!0});var znt=s(rj);G7o=r(znt,"FNetForPreTraining"),znt.forEach(t),O7o=r(Mye," (FNet 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deactivated). To train the model, you should first set it back in training mode with `),Dde=n(Yye,"CODE",{});var Ust=s(Dde);wCo=r(Ust,"model.train()"),Ust.forEach(t),Yye.forEach(t),ACo=i(ra),T(Ku.$$.fragment,ra),ra.forEach(t),Vs.forEach(t),Kqe=i(f),Si=n(f,"H2",{class:!0});var rGe=s(Si);Zu=n(rGe,"A",{id:!0,class:!0,href:!0});var Jst=s(Zu);Gde=n(Jst,"SPAN",{});var Yst=s(Gde);T(b6.$$.fragment,Yst),Yst.forEach(t),Jst.forEach(t),yCo=i(rGe),Ode=n(rGe,"SPAN",{});var Kst=s(Ode);LCo=r(Kst,"AutoModelForCausalLM"),Kst.forEach(t),rGe.forEach(t),Zqe=i(f),$o=n(f,"DIV",{class:!0});var Xs=s($o);T(v6.$$.fragment,Xs),xCo=i(Xs),Ri=n(Xs,"P",{});var jZ=s(Ri);$Co=r(jZ,`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 `),kj=n(jZ,"A",{href:!0});var Zst=s(kj);kCo=r(Zst,"from_pretrained()"),Zst.forEach(t),SCo=r(jZ," class method or the "),Sj=n(jZ,"A",{href:!0});var elt=s(Sj);RCo=r(elt,"from_config()"),elt.forEach(t),PCo=r(jZ,` class
method.`),jZ.forEach(t),BCo=i(Xs),F6=n(Xs,"P",{});var tGe=s(F6);ICo=r(tGe,"This class cannot be instantiated directly using "),Vde=n(tGe,"CODE",{});var olt=s(Vde);NCo=r(olt,"__init__()"),olt.forEach(t),qCo=r(tGe," (throws an error)."),tGe.forEach(t),jCo=i(Xs),nt=n(Xs,"DIV",{class:!0});var X0=s(nt);T(T6.$$.fragment,X0),DCo=i(X0),Xde=n(X0,"P",{});var rlt=s(Xde);GCo=r(rlt,"Instantiates one of the model classes of the library (with a causal language modeling head) from a configuration."),rlt.forEach(t),OCo=i(X0),Pi=n(X0,"P",{});var DZ=s(Pi);VCo=r(DZ,`Note:
Loading a model from its configuration file does `),zde=n(DZ,"STRONG",{});var tlt=s(zde);XCo=r(tlt,"not"),tlt.forEach(t),zCo=r(DZ,` load the model weights. It only affects the
model\u2019s configuration. Use `),Rj=n(DZ,"A",{href:!0});var alt=s(Rj);WCo=r(alt,"from_pretrained()"),alt.forEach(t),QCo=r(DZ," to load the model weights."),DZ.forEach(t),HCo=i(X0),T(e1.$$.fragment,X0),X0.forEach(t),UCo=i(Xs),Ke=n(Xs,"DIV",{class:!0});var ta=s(Ke);T(M6.$$.fragment,ta),JCo=i(ta),Wde=n(ta,"P",{});var nlt=s(Wde);YCo=r(nlt,"Instantiate one of the model classes of the library (with a causal language modeling head) from a pretrained model."),nlt.forEach(t),KCo=i(ta),$a=n(ta,"P",{});var z0=s($a);ZCo=r(z0,"The model class to instantiate is selected based on the "),Qde=n(z0,"CODE",{});var slt=s(Qde);e3o=r(slt,"model_type"),slt.forEach(t),o3o=r(z0,` property of the config object (either
passed as an argument or loaded from `),Hde=n(z0,"CODE",{});var llt=s(Hde);r3o=r(llt,"pretrained_model_name_or_path"),llt.forEach(t),t3o=r(z0,` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),Ude=n(z0,"CODE",{});var ilt=s(Ude);a3o=r(ilt,"pretrained_model_name_or_path"),ilt.forEach(t),n3o=r(z0,":"),z0.forEach(t),s3o=i(ta),z=n(ta,"UL",{});var Q=s(z);o1=n(Q,"LI",{});var Kye=s(o1);Jde=n(Kye,"STRONG",{});var dlt=s(Jde);l3o=r(dlt,"bart"),dlt.forEach(t),i3o=r(Kye," \u2014 "),Pj=n(Kye,"A",{href:!0});var clt=s(Pj);d3o=r(clt,"BartForCausalLM"),clt.forEach(t),c3o=r(Kye," (BART model)"),Kye.forEach(t),f3o=i(Q),r1=n(Q,"LI",{});var Zye=s(r1);Yde=n(Zye,"STRONG",{});var flt=s(Yde);m3o=r(flt,"bert"),flt.forEach(t),g3o=r(Zye," \u2014 "),Bj=n(Zye,"A",{href:!0});var mlt=s(Bj);h3o=r(mlt,"BertLMHeadModel"),mlt.forEach(t),p3o=r(Zye," (BERT model)"),Zye.forEach(t),_3o=i(Q),t1=n(Q,"LI",{});var eLe=s(t1);Kde=n(eLe,"STRONG",{});var glt=s(Kde);u3o=r(glt,"bert-generation"),glt.forEach(t),b3o=r(eLe," \u2014 "),Ij=n(eLe,"A",{href:!0});var hlt=s(Ij);v3o=r(hlt,"BertGenerationDecoder"),hlt.forEach(t),F3o=r(eLe," (Bert Generation model)"),eLe.forEach(t),T3o=i(Q),a1=n(Q,"LI",{});var oLe=s(a1);Zde=n(oLe,"STRONG",{});var plt=s(Zde);M3o=r(plt,"big_bird"),plt.forEach(t),E3o=r(oLe," \u2014 "),Nj=n(oLe,"A",{href:!0});var _lt=s(Nj);C3o=r(_lt,"BigBirdForCausalLM"),_lt.forEach(t),w3o=r(oLe," (BigBird model)"),oLe.forEach(t),A3o=i(Q),n1=n(Q,"LI",{});var rLe=s(n1);ece=n(rLe,"STRONG",{});var ult=s(ece);y3o=r(ult,"bigbird_pegasus"),ult.forEach(t),L3o=r(rLe," \u2014 "),qj=n(rLe,"A",{href:!0});var blt=s(qj);x3o=r(blt,"BigBirdPegasusForCausalLM"),blt.forEach(t),$3o=r(rLe," (BigBird-Pegasus model)"),rLe.forEach(t),k3o=i(Q),s1=n(Q,"LI",{});var tLe=s(s1);oce=n(tLe,"STRONG",{});var vlt=s(oce);S3o=r(vlt,"blenderbot"),vlt.forEach(t),R3o=r(tLe," \u2014 "),jj=n(tLe,"A",{href:!0});var Flt=s(jj);P3o=r(Flt,"BlenderbotForCausalLM"),Flt.forEach(t),B3o=r(tLe," (Blenderbot model)"),tLe.forEach(t),I3o=i(Q),l1=n(Q,"LI",{});var aLe=s(l1);rce=n(aLe,"STRONG",{});var Tlt=s(rce);N3o=r(Tlt,"blenderbot-small"),Tlt.forEach(t),q3o=r(aLe," \u2014 "),Dj=n(aLe,"A",{href:!0});var Mlt=s(Dj);j3o=r(Mlt,"BlenderbotSmallForCausalLM"),Mlt.forEach(t),D3o=r(aLe," (BlenderbotSmall model)"),aLe.forEach(t),G3o=i(Q),i1=n(Q,"LI",{});var nLe=s(i1);tce=n(nLe,"STRONG",{});var Elt=s(tce);O3o=r(Elt,"camembert"),Elt.forEach(t),V3o=r(nLe," \u2014 "),Gj=n(nLe,"A",{href:!0});var Clt=s(Gj);X3o=r(Clt,"CamembertForCausalLM"),Clt.forEach(t),z3o=r(nLe," (CamemBERT model)"),nLe.forEach(t),W3o=i(Q),d1=n(Q,"LI",{});var sLe=s(d1);ace=n(sLe,"STRONG",{});var wlt=s(ace);Q3o=r(wlt,"ctrl"),wlt.forEach(t),H3o=r(sLe," \u2014 "),Oj=n(sLe,"A",{href:!0});var Alt=s(Oj);U3o=r(Alt,"CTRLLMHeadModel"),Alt.forEach(t),J3o=r(sLe," (CTRL model)"),sLe.forEach(t),Y3o=i(Q),c1=n(Q,"LI",{});var lLe=s(c1);nce=n(lLe,"STRONG",{});var ylt=s(nce);K3o=r(ylt,"data2vec-text"),ylt.forEach(t),Z3o=r(lLe," \u2014 "),Vj=n(lLe,"A",{href:!0});var Llt=s(Vj);e0o=r(Llt,"Data2VecTextForCausalLM"),Llt.forEach(t),o0o=r(lLe," (Data2VecText model)"),lLe.forEach(t),r0o=i(Q),f1=n(Q,"LI",{});var iLe=s(f1);sce=n(iLe,"STRONG",{});var xlt=s(sce);t0o=r(xlt,"electra"),xlt.forEach(t),a0o=r(iLe," \u2014 "),Xj=n(iLe,"A",{href:!0});var $lt=s(Xj);n0o=r($lt,"ElectraForCausalLM"),$lt.forEach(t),s0o=r(iLe," (ELECTRA model)"),iLe.forEach(t),l0o=i(Q),m1=n(Q,"LI",{});var dLe=s(m1);lce=n(dLe,"STRONG",{});var klt=s(lce);i0o=r(klt,"gpt2"),klt.forEach(t),d0o=r(dLe," \u2014 "),zj=n(dLe,"A",{href:!0});var Slt=s(zj);c0o=r(Slt,"GPT2LMHeadModel"),Slt.forEach(t),f0o=r(dLe," (OpenAI GPT-2 model)"),dLe.forEach(t),m0o=i(Q),g1=n(Q,"LI",{});var cLe=s(g1);ice=n(cLe,"STRONG",{});var Rlt=s(ice);g0o=r(Rlt,"gpt_neo"),Rlt.forEach(t),h0o=r(cLe," \u2014 "),Wj=n(cLe,"A",{href:!0});var Plt=s(Wj);p0o=r(Plt,"GPTNeoForCausalLM"),Plt.forEach(t),_0o=r(cLe," (GPT Neo model)"),cLe.forEach(t),u0o=i(Q),h1=n(Q,"LI",{});var fLe=s(h1);dce=n(fLe,"STRONG",{});var Blt=s(dce);b0o=r(Blt,"gpt_neox"),Blt.forEach(t),v0o=r(fLe," \u2014 "),Qj=n(fLe,"A",{href:!0});var Ilt=s(Qj);F0o=r(Ilt,"GPTNeoXForCausalLM"),Ilt.forEach(t),T0o=r(fLe," (GPT NeoX model)"),fLe.forEach(t),M0o=i(Q),p1=n(Q,"LI",{});var mLe=s(p1);cce=n(mLe,"STRONG",{});var Nlt=s(cce);E0o=r(Nlt,"gptj"),Nlt.forEach(t),C0o=r(mLe," \u2014 "),Hj=n(mLe,"A",{href:!0});var qlt=s(Hj);w0o=r(qlt,"GPTJForCausalLM"),qlt.forEach(t),A0o=r(mLe," (GPT-J model)"),mLe.forEach(t),y0o=i(Q),_1=n(Q,"LI",{});var gLe=s(_1);fce=n(gLe,"STRONG",{});var jlt=s(fce);L0o=r(jlt,"marian"),jlt.forEach(t),x0o=r(gLe," \u2014 "),Uj=n(gLe,"A",{href:!0});var Dlt=s(Uj);$0o=r(Dlt,"MarianForCausalLM"),Dlt.forEach(t),k0o=r(gLe," (Marian model)"),gLe.forEach(t),S0o=i(Q),u1=n(Q,"LI",{});var hLe=s(u1);mce=n(hLe,"STRONG",{});var Glt=s(mce);R0o=r(Glt,"mbart"),Glt.forEach(t),P0o=r(hLe," \u2014 "),Jj=n(hLe,"A",{href:!0});var Olt=s(Jj);B0o=r(Olt,"MBartForCausalLM"),Olt.forEach(t),I0o=r(hLe," (mBART model)"),hLe.forEach(t),N0o=i(Q),b1=n(Q,"LI",{});var pLe=s(b1);gce=n(pLe,"STRONG",{});var Vlt=s(gce);q0o=r(Vlt,"megatron-bert"),Vlt.forEach(t),j0o=r(pLe," \u2014 "),Yj=n(pLe,"A",{href:!0});var Xlt=s(Yj);D0o=r(Xlt,"MegatronBertForCausalLM"),Xlt.forEach(t),G0o=r(pLe," (Megatron-BERT model)"),pLe.forEach(t),O0o=i(Q),v1=n(Q,"LI",{});var _Le=s(v1);hce=n(_Le,"STRONG",{});var zlt=s(hce);V0o=r(zlt,"openai-gpt"),zlt.forEach(t),X0o=r(_Le," \u2014 "),Kj=n(_Le,"A",{href:!0});var Wlt=s(Kj);z0o=r(Wlt,"OpenAIGPTLMHeadModel"),Wlt.forEach(t),W0o=r(_Le," (OpenAI GPT model)"),_Le.forEach(t),Q0o=i(Q),F1=n(Q,"LI",{});var uLe=s(F1);pce=n(uLe,"STRONG",{});var Qlt=s(pce);H0o=r(Qlt,"opt"),Qlt.forEach(t),U0o=r(uLe," \u2014 "),Zj=n(uLe,"A",{href:!0});var Hlt=s(Zj);J0o=r(Hlt,"OPTForCausalLM"),Hlt.forEach(t),Y0o=r(uLe," (OPT model)"),uLe.forEach(t),K0o=i(Q),T1=n(Q,"LI",{});var bLe=s(T1);_ce=n(bLe,"STRONG",{});var Ult=s(_ce);Z0o=r(Ult,"pegasus"),Ult.forEach(t),ewo=r(bLe," \u2014 "),eD=n(bLe,"A",{href:!0});var Jlt=s(eD);owo=r(Jlt,"PegasusForCausalLM"),Jlt.forEach(t),rwo=r(bLe," (Pegasus model)"),bLe.forEach(t),two=i(Q),M1=n(Q,"LI",{});var vLe=s(M1);uce=n(vLe,"STRONG",{});var Ylt=s(uce);awo=r(Ylt,"plbart"),Ylt.forEach(t),nwo=r(vLe," \u2014 "),oD=n(vLe,"A",{href:!0});var Klt=s(oD);swo=r(Klt,"PLBartForCausalLM"),Klt.forEach(t),lwo=r(vLe," (PLBart model)"),vLe.forEach(t),iwo=i(Q),E1=n(Q,"LI",{});var FLe=s(E1);bce=n(FLe,"STRONG",{});var Zlt=s(bce);dwo=r(Zlt,"prophetnet"),Zlt.forEach(t),cwo=r(FLe," \u2014 "),rD=n(FLe,"A",{href:!0});var eit=s(rD);fwo=r(eit,"ProphetNetForCausalLM"),eit.forEach(t),mwo=r(FLe," (ProphetNet model)"),FLe.forEach(t),gwo=i(Q),C1=n(Q,"LI",{});var TLe=s(C1);vce=n(TLe,"STRONG",{});var oit=s(vce);hwo=r(oit,"qdqbert"),oit.forEach(t),pwo=r(TLe," \u2014 "),tD=n(TLe,"A",{href:!0});var rit=s(tD);_wo=r(rit,"QDQBertLMHeadModel"),rit.forEach(t),uwo=r(TLe," (QDQBert model)"),TLe.forEach(t),bwo=i(Q),w1=n(Q,"LI",{});var MLe=s(w1);Fce=n(MLe,"STRONG",{});var tit=s(Fce);vwo=r(tit,"reformer"),tit.forEach(t),Fwo=r(MLe," \u2014 "),aD=n(MLe,"A",{href:!0});var ait=s(aD);Two=r(ait,"ReformerModelWithLMHead"),ait.forEach(t),Mwo=r(MLe," (Reformer 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mit=s(iD);Gwo=r(mit,"Speech2Text2ForCausalLM"),mit.forEach(t),Owo=r(ALe," (Speech2Text2 model)"),ALe.forEach(t),Vwo=i(Q),$1=n(Q,"LI",{});var yLe=s($1);wce=n(yLe,"STRONG",{});var git=s(wce);Xwo=r(git,"transfo-xl"),git.forEach(t),zwo=r(yLe," \u2014 "),dD=n(yLe,"A",{href:!0});var hit=s(dD);Wwo=r(hit,"TransfoXLLMHeadModel"),hit.forEach(t),Qwo=r(yLe," (Transformer-XL model)"),yLe.forEach(t),Hwo=i(Q),k1=n(Q,"LI",{});var LLe=s(k1);Ace=n(LLe,"STRONG",{});var pit=s(Ace);Uwo=r(pit,"trocr"),pit.forEach(t),Jwo=r(LLe," \u2014 "),cD=n(LLe,"A",{href:!0});var _it=s(cD);Ywo=r(_it,"TrOCRForCausalLM"),_it.forEach(t),Kwo=r(LLe," (TrOCR model)"),LLe.forEach(t),Zwo=i(Q),S1=n(Q,"LI",{});var xLe=s(S1);yce=n(xLe,"STRONG",{});var uit=s(yce);eAo=r(uit,"xglm"),uit.forEach(t),oAo=r(xLe," \u2014 "),fD=n(xLe,"A",{href:!0});var bit=s(fD);rAo=r(bit,"XGLMForCausalLM"),bit.forEach(t),tAo=r(xLe," (XGLM model)"),xLe.forEach(t),aAo=i(Q),R1=n(Q,"LI",{});var $Le=s(R1);Lce=n($Le,"STRONG",{});var vit=s(Lce);nAo=r(vit,"xlm"),vit.forEach(t),sAo=r($Le," \u2014 "),mD=n($Le,"A",{href:!0});var Fit=s(mD);lAo=r(Fit,"XLMWithLMHeadModel"),Fit.forEach(t),iAo=r($Le," (XLM model)"),$Le.forEach(t),dAo=i(Q),P1=n(Q,"LI",{});var kLe=s(P1);xce=n(kLe,"STRONG",{});var Tit=s(xce);cAo=r(Tit,"xlm-prophetnet"),Tit.forEach(t),fAo=r(kLe," \u2014 "),gD=n(kLe,"A",{href:!0});var Mit=s(gD);mAo=r(Mit,"XLMProphetNetForCausalLM"),Mit.forEach(t),gAo=r(kLe," (XLM-ProphetNet model)"),kLe.forEach(t),hAo=i(Q),B1=n(Q,"LI",{});var SLe=s(B1);$ce=n(SLe,"STRONG",{});var Eit=s($ce);pAo=r(Eit,"xlm-roberta"),Eit.forEach(t),_Ao=r(SLe," \u2014 "),hD=n(SLe,"A",{href:!0});var Cit=s(hD);uAo=r(Cit,"XLMRobertaForCausalLM"),Cit.forEach(t),bAo=r(SLe," (XLM-RoBERTa model)"),SLe.forEach(t),vAo=i(Q),I1=n(Q,"LI",{});var RLe=s(I1);kce=n(RLe,"STRONG",{});var wit=s(kce);FAo=r(wit,"xlm-roberta-xl"),wit.forEach(t),TAo=r(RLe," \u2014 "),pD=n(RLe,"A",{href:!0});var Ait=s(pD);MAo=r(Ait,"XLMRobertaXLForCausalLM"),Ait.forEach(t),EAo=r(RLe," (XLM-RoBERTa-XL model)"),RLe.forEach(t),CAo=i(Q),N1=n(Q,"LI",{});var PLe=s(N1);Sce=n(PLe,"STRONG",{});var yit=s(Sce);wAo=r(yit,"xlnet"),yit.forEach(t),AAo=r(PLe," \u2014 "),_D=n(PLe,"A",{href:!0});var Lit=s(_D);yAo=r(Lit,"XLNetLMHeadModel"),Lit.forEach(t),LAo=r(PLe," (XLNet model)"),PLe.forEach(t),Q.forEach(t),xAo=i(ta),q1=n(ta,"P",{});var BLe=s(q1);$Ao=r(BLe,"The model is set in evaluation mode by default using "),Rce=n(BLe,"CODE",{});var xit=s(Rce);kAo=r(xit,"model.eval()"),xit.forEach(t),SAo=r(BLe,` (so for instance, dropout modules are
deactivated). To train the model, you should first set it back in training mode with `),Pce=n(BLe,"CODE",{});var $it=s(Pce);RAo=r($it,"model.train()"),$it.forEach(t),BLe.forEach(t),PAo=i(ta),T(j1.$$.fragment,ta),ta.forEach(t),Xs.forEach(t),eje=i(f),Bi=n(f,"H2",{class:!0});var aGe=s(Bi);D1=n(aGe,"A",{id:!0,class:!0,href:!0});var kit=s(D1);Bce=n(kit,"SPAN",{});var Sit=s(Bce);T(E6.$$.fragment,Sit),Sit.forEach(t),kit.forEach(t),BAo=i(aGe),Ice=n(aGe,"SPAN",{});var Rit=s(Ice);IAo=r(Rit,"AutoModelForMaskedLM"),Rit.forEach(t),aGe.forEach(t),oje=i(f),ko=n(f,"DIV",{class:!0});var zs=s(ko);T(C6.$$.fragment,zs),NAo=i(zs),Ii=n(zs,"P",{});var GZ=s(Ii);qAo=r(GZ,`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 `),uD=n(GZ,"A",{href:!0});var Pit=s(uD);jAo=r(Pit,"from_pretrained()"),Pit.forEach(t),DAo=r(GZ," class method or the "),bD=n(GZ,"A",{href:!0});var Bit=s(bD);GAo=r(Bit,"from_config()"),Bit.forEach(t),OAo=r(GZ,` class
method.`),GZ.forEach(t),VAo=i(zs),w6=n(zs,"P",{});var nGe=s(w6);XAo=r(nGe,"This class cannot be instantiated directly using "),Nce=n(nGe,"CODE",{});var Iit=s(Nce);zAo=r(Iit,"__init__()"),Iit.forEach(t),WAo=r(nGe," (throws an error)."),nGe.forEach(t),QAo=i(zs),st=n(zs,"DIV",{class:!0});var W0=s(st);T(A6.$$.fragment,W0),HAo=i(W0),qce=n(W0,"P",{});var Nit=s(qce);UAo=r(Nit,"Instantiates one of the model classes of the library (with a masked language modeling head) from a configuration."),Nit.forEach(t),JAo=i(W0),Ni=n(W0,"P",{});var OZ=s(Ni);YAo=r(OZ,`Note:
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model\u2019s configuration. Use `),vD=n(OZ,"A",{href:!0});var jit=s(vD);e6o=r(jit,"from_pretrained()"),jit.forEach(t),o6o=r(OZ," to load the model weights."),OZ.forEach(t),r6o=i(W0),T(G1.$$.fragment,W0),W0.forEach(t),t6o=i(zs),Ze=n(zs,"DIV",{class:!0});var aa=s(Ze);T(y6.$$.fragment,aa),a6o=i(aa),Dce=n(aa,"P",{});var Dit=s(Dce);n6o=r(Dit,"Instantiate one of the model classes of the library (with a masked language modeling head) from a pretrained model."),Dit.forEach(t),s6o=i(aa),ka=n(aa,"P",{});var Q0=s(ka);l6o=r(Q0,"The model class to instantiate is selected based on the "),Gce=n(Q0,"CODE",{});var Git=s(Gce);i6o=r(Git,"model_type"),Git.forEach(t),d6o=r(Q0,` property of the config object (either
passed as an argument or loaded from `),Oce=n(Q0,"CODE",{});var Oit=s(Oce);c6o=r(Oit,"pretrained_model_name_or_path"),Oit.forEach(t),f6o=r(Q0,` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),Vce=n(Q0,"CODE",{});var Vit=s(Vce);m6o=r(Vit,"pretrained_model_name_or_path"),Vit.forEach(t),g6o=r(Q0,":"),Q0.forEach(t),h6o=i(aa),W=n(aa,"UL",{});var H=s(W);O1=n(H,"LI",{});var ILe=s(O1);Xce=n(ILe,"STRONG",{});var Xit=s(Xce);p6o=r(Xit,"albert"),Xit.forEach(t),_6o=r(ILe," \u2014 "),FD=n(ILe,"A",{href:!0});var zit=s(FD);u6o=r(zit,"AlbertForMaskedLM"),zit.forEach(t),b6o=r(ILe," (ALBERT model)"),ILe.forEach(t),v6o=i(H),V1=n(H,"LI",{});var NLe=s(V1);zce=n(NLe,"STRONG",{});var Wit=s(zce);F6o=r(Wit,"bart"),Wit.forEach(t),T6o=r(NLe," \u2014 "),TD=n(NLe,"A",{href:!0});var Qit=s(TD);M6o=r(Qit,"BartForConditionalGeneration"),Qit.forEach(t),E6o=r(NLe," (BART model)"),NLe.forEach(t),C6o=i(H),X1=n(H,"LI",{});var qLe=s(X1);Wce=n(qLe,"STRONG",{});var Hit=s(Wce);w6o=r(Hit,"bert"),Hit.forEach(t),A6o=r(qLe," \u2014 "),MD=n(qLe,"A",{href:!0});var Uit=s(MD);y6o=r(Uit,"BertForMaskedLM"),Uit.forEach(t),L6o=r(qLe," (BERT 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tdt=s(AD);Q6o=r(tdt,"Data2VecTextForMaskedLM"),tdt.forEach(t),H6o=r(OLe," (Data2VecText model)"),OLe.forEach(t),U6o=i(H),U1=n(H,"LI",{});var VLe=s(U1);Yce=n(VLe,"STRONG",{});var adt=s(Yce);J6o=r(adt,"deberta"),adt.forEach(t),Y6o=r(VLe," \u2014 "),yD=n(VLe,"A",{href:!0});var ndt=s(yD);K6o=r(ndt,"DebertaForMaskedLM"),ndt.forEach(t),Z6o=r(VLe," (DeBERTa model)"),VLe.forEach(t),eyo=i(H),J1=n(H,"LI",{});var XLe=s(J1);Kce=n(XLe,"STRONG",{});var sdt=s(Kce);oyo=r(sdt,"deberta-v2"),sdt.forEach(t),ryo=r(XLe," \u2014 "),LD=n(XLe,"A",{href:!0});var ldt=s(LD);tyo=r(ldt,"DebertaV2ForMaskedLM"),ldt.forEach(t),ayo=r(XLe," (DeBERTa-v2 model)"),XLe.forEach(t),nyo=i(H),Y1=n(H,"LI",{});var zLe=s(Y1);Zce=n(zLe,"STRONG",{});var idt=s(Zce);syo=r(idt,"distilbert"),idt.forEach(t),lyo=r(zLe," \u2014 "),xD=n(zLe,"A",{href:!0});var ddt=s(xD);iyo=r(ddt,"DistilBertForMaskedLM"),ddt.forEach(t),dyo=r(zLe," (DistilBERT model)"),zLe.forEach(t),cyo=i(H),K1=n(H,"LI",{});var WLe=s(K1);efe=n(WLe,"STRONG",{});var 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Sdt=s(ffe);mLo=r(Sdt,"mpnet"),Sdt.forEach(t),gLo=r(t9e," \u2014 "),GD=n(t9e,"A",{href:!0});var Rdt=s(GD);hLo=r(Rdt,"MPNetForMaskedLM"),Rdt.forEach(t),pLo=r(t9e," (MPNet model)"),t9e.forEach(t),_Lo=i(H),cb=n(H,"LI",{});var a9e=s(cb);mfe=n(a9e,"STRONG",{});var Pdt=s(mfe);uLo=r(Pdt,"nystromformer"),Pdt.forEach(t),bLo=r(a9e," \u2014 "),OD=n(a9e,"A",{href:!0});var Bdt=s(OD);vLo=r(Bdt,"NystromformerForMaskedLM"),Bdt.forEach(t),FLo=r(a9e," (Nystr\xF6mformer model)"),a9e.forEach(t),TLo=i(H),fb=n(H,"LI",{});var n9e=s(fb);gfe=n(n9e,"STRONG",{});var Idt=s(gfe);MLo=r(Idt,"perceiver"),Idt.forEach(t),ELo=r(n9e," \u2014 "),VD=n(n9e,"A",{href:!0});var Ndt=s(VD);CLo=r(Ndt,"PerceiverForMaskedLM"),Ndt.forEach(t),wLo=r(n9e," (Perceiver model)"),n9e.forEach(t),ALo=i(H),mb=n(H,"LI",{});var s9e=s(mb);hfe=n(s9e,"STRONG",{});var qdt=s(hfe);yLo=r(qdt,"qdqbert"),qdt.forEach(t),LLo=r(s9e," \u2014 "),XD=n(s9e,"A",{href:!0});var jdt=s(XD);xLo=r(jdt,"QDQBertForMaskedLM"),jdt.forEach(t),$Lo=r(s9e," (QDQBert 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method.`),VZ.forEach(t),W9o=i(Ws),$6=n(Ws,"P",{});var lGe=s($6);Q9o=r(lGe,"This class cannot be instantiated directly using "),kfe=n(lGe,"CODE",{});var pct=s(kfe);H9o=r(pct,"__init__()"),pct.forEach(t),U9o=r(lGe," (throws an error)."),lGe.forEach(t),J9o=i(Ws),lt=n(Ws,"DIV",{class:!0});var H0=s(lt);T(k6.$$.fragment,H0),Y9o=i(H0),Sfe=n(H0,"P",{});var _ct=s(Sfe);K9o=r(_ct,"Instantiates one of the model classes of the library (with a sequence-to-sequence language modeling head) from a configuration."),_ct.forEach(t),Z9o=i(H0),Di=n(H0,"P",{});var XZ=s(Di);exo=r(XZ,`Note:
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model\u2019s configuration. Use `),tG=n(XZ,"A",{href:!0});var bct=s(tG);txo=r(bct,"from_pretrained()"),bct.forEach(t),axo=r(XZ," to load the model weights."),XZ.forEach(t),nxo=i(H0),T(yb.$$.fragment,H0),H0.forEach(t),sxo=i(Ws),eo=n(Ws,"DIV",{class:!0});var na=s(eo);T(S6.$$.fragment,na),lxo=i(na),Pfe=n(na,"P",{});var vct=s(Pfe);ixo=r(vct,"Instantiate one of the model classes of the library (with a sequence-to-sequence language modeling head) from a pretrained model."),vct.forEach(t),dxo=i(na),Sa=n(na,"P",{});var U0=s(Sa);cxo=r(U0,"The model class to instantiate is selected based on the "),Bfe=n(U0,"CODE",{});var Fct=s(Bfe);fxo=r(Fct,"model_type"),Fct.forEach(t),mxo=r(U0,` property of the config object (either
passed as an argument or loaded from `),Ife=n(U0,"CODE",{});var Tct=s(Ife);gxo=r(Tct,"pretrained_model_name_or_path"),Tct.forEach(t),hxo=r(U0,` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),Nfe=n(U0,"CODE",{});var Mct=s(Nfe);pxo=r(Mct,"pretrained_model_name_or_path"),Mct.forEach(t),_xo=r(U0,":"),U0.forEach(t),uxo=i(na),_e=n(na,"UL",{});var Fe=s(_e);Lb=n(Fe,"LI",{});var v9e=s(Lb);qfe=n(v9e,"STRONG",{});var Ect=s(qfe);bxo=r(Ect,"bart"),Ect.forEach(t),vxo=r(v9e," \u2014 "),aG=n(v9e,"A",{href:!0});var Cct=s(aG);Fxo=r(Cct,"BartForConditionalGeneration"),Cct.forEach(t),Txo=r(v9e," (BART model)"),v9e.forEach(t),Mxo=i(Fe),xb=n(Fe,"LI",{});var F9e=s(xb);jfe=n(F9e,"STRONG",{});var wct=s(jfe);Exo=r(wct,"bigbird_pegasus"),wct.forEach(t),Cxo=r(F9e," \u2014 "),nG=n(F9e,"A",{href:!0});var Act=s(nG);wxo=r(Act,"BigBirdPegasusForConditionalGeneration"),Act.forEach(t),Axo=r(F9e," (BigBird-Pegasus model)"),F9e.forEach(t),yxo=i(Fe),$b=n(Fe,"LI",{});var T9e=s($b);Dfe=n(T9e,"STRONG",{});var yct=s(Dfe);Lxo=r(yct,"blenderbot"),yct.forEach(t),xxo=r(T9e," \u2014 "),sG=n(T9e,"A",{href:!0});var Lct=s(sG);$xo=r(Lct,"BlenderbotForConditionalGeneration"),Lct.forEach(t),kxo=r(T9e," (Blenderbot model)"),T9e.forEach(t),Sxo=i(Fe),kb=n(Fe,"LI",{});var M9e=s(kb);Gfe=n(M9e,"STRONG",{});var xct=s(Gfe);Rxo=r(xct,"blenderbot-small"),xct.forEach(t),Pxo=r(M9e," \u2014 "),lG=n(M9e,"A",{href:!0});var $ct=s(lG);Bxo=r($ct,"BlenderbotSmallForConditionalGeneration"),$ct.forEach(t),Ixo=r(M9e," (BlenderbotSmall model)"),M9e.forEach(t),Nxo=i(Fe),Sb=n(Fe,"LI",{});var E9e=s(Sb);Ofe=n(E9e,"STRONG",{});var kct=s(Ofe);qxo=r(kct,"encoder-decoder"),kct.forEach(t),jxo=r(E9e," \u2014 "),iG=n(E9e,"A",{href:!0});var Sct=s(iG);Dxo=r(Sct,"EncoderDecoderModel"),Sct.forEach(t),Gxo=r(E9e," (Encoder decoder model)"),E9e.forEach(t),Oxo=i(Fe),Rb=n(Fe,"LI",{});var C9e=s(Rb);Vfe=n(C9e,"STRONG",{});var Rct=s(Vfe);Vxo=r(Rct,"fsmt"),Rct.forEach(t),Xxo=r(C9e," \u2014 "),dG=n(C9e,"A",{href:!0});var Pct=s(dG);zxo=r(Pct,"FSMTForConditionalGeneration"),Pct.forEach(t),Wxo=r(C9e," (FairSeq Machine-Translation model)"),C9e.forEach(t),Qxo=i(Fe),Pb=n(Fe,"LI",{});var w9e=s(Pb);Xfe=n(w9e,"STRONG",{});var Bct=s(Xfe);Hxo=r(Bct,"led"),Bct.forEach(t),Uxo=r(w9e," \u2014 "),cG=n(w9e,"A",{href:!0});var Ict=s(cG);Jxo=r(Ict,"LEDForConditionalGeneration"),Ict.forEach(t),Yxo=r(w9e," (LED model)"),w9e.forEach(t),Kxo=i(Fe),Bb=n(Fe,"LI",{});var A9e=s(Bb);zfe=n(A9e,"STRONG",{});var Nct=s(zfe);Zxo=r(Nct,"m2m_100"),Nct.forEach(t),e$o=r(A9e," \u2014 "),fG=n(A9e,"A",{href:!0});var qct=s(fG);o$o=r(qct,"M2M100ForConditionalGeneration"),qct.forEach(t),r$o=r(A9e," (M2M100 model)"),A9e.forEach(t),t$o=i(Fe),Ib=n(Fe,"LI",{});var y9e=s(Ib);Wfe=n(y9e,"STRONG",{});var jct=s(Wfe);a$o=r(jct,"marian"),jct.forEach(t),n$o=r(y9e," \u2014 "),mG=n(y9e,"A",{href:!0});var Dct=s(mG);s$o=r(Dct,"MarianMTModel"),Dct.forEach(t),l$o=r(y9e," (Marian model)"),y9e.forEach(t),i$o=i(Fe),Nb=n(Fe,"LI",{});var L9e=s(Nb);Qfe=n(L9e,"STRONG",{});var Gct=s(Qfe);d$o=r(Gct,"mbart"),Gct.forEach(t),c$o=r(L9e," \u2014 "),gG=n(L9e,"A",{href:!0});var Oct=s(gG);f$o=r(Oct,"MBartForConditionalGeneration"),Oct.forEach(t),m$o=r(L9e," (mBART model)"),L9e.forEach(t),g$o=i(Fe),qb=n(Fe,"LI",{});var x9e=s(qb);Hfe=n(x9e,"STRONG",{});var Vct=s(Hfe);h$o=r(Vct,"mt5"),Vct.forEach(t),p$o=r(x9e," \u2014 "),hG=n(x9e,"A",{href:!0});var Xct=s(hG);_$o=r(Xct,"MT5ForConditionalGeneration"),Xct.forEach(t),u$o=r(x9e," (MT5 model)"),x9e.forEach(t),b$o=i(Fe),jb=n(Fe,"LI",{});var $9e=s(jb);Ufe=n($9e,"STRONG",{});var zct=s(Ufe);v$o=r(zct,"pegasus"),zct.forEach(t),F$o=r($9e," \u2014 "),pG=n($9e,"A",{href:!0});var Wct=s(pG);T$o=r(Wct,"PegasusForConditionalGeneration"),Wct.forEach(t),M$o=r($9e," (Pegasus model)"),$9e.forEach(t),E$o=i(Fe),Db=n(Fe,"LI",{});var k9e=s(Db);Jfe=n(k9e,"STRONG",{});var Qct=s(Jfe);C$o=r(Qct,"plbart"),Qct.forEach(t),w$o=r(k9e," \u2014 "),_G=n(k9e,"A",{href:!0});var Hct=s(_G);A$o=r(Hct,"PLBartForConditionalGeneration"),Hct.forEach(t),y$o=r(k9e," (PLBart model)"),k9e.forEach(t),L$o=i(Fe),Gb=n(Fe,"LI",{});var S9e=s(Gb);Yfe=n(S9e,"STRONG",{});var Uct=s(Yfe);x$o=r(Uct,"prophetnet"),Uct.forEach(t),$$o=r(S9e," \u2014 "),uG=n(S9e,"A",{href:!0});var Jct=s(uG);k$o=r(Jct,"ProphetNetForConditionalGeneration"),Jct.forEach(t),S$o=r(S9e," (ProphetNet model)"),S9e.forEach(t),R$o=i(Fe),Ob=n(Fe,"LI",{});var R9e=s(Ob);Kfe=n(R9e,"STRONG",{});var Yct=s(Kfe);P$o=r(Yct,"t5"),Yct.forEach(t),B$o=r(R9e," \u2014 "),bG=n(R9e,"A",{href:!0});var Kct=s(bG);I$o=r(Kct,"T5ForConditionalGeneration"),Kct.forEach(t),N$o=r(R9e," (T5 model)"),R9e.forEach(t),q$o=i(Fe),Vb=n(Fe,"LI",{});var P9e=s(Vb);Zfe=n(P9e,"STRONG",{});var Zct=s(Zfe);j$o=r(Zct,"xlm-prophetnet"),Zct.forEach(t),D$o=r(P9e," \u2014 "),vG=n(P9e,"A",{href:!0});var eft=s(vG);G$o=r(eft,"XLMProphetNetForConditionalGeneration"),eft.forEach(t),O$o=r(P9e," (XLM-ProphetNet model)"),P9e.forEach(t),Fe.forEach(t),V$o=i(na),Xb=n(na,"P",{});var B9e=s(Xb);X$o=r(B9e,"The model is set in evaluation mode by default using "),eme=n(B9e,"CODE",{});var oft=s(eme);z$o=r(oft,"model.eval()"),oft.forEach(t),W$o=r(B9e,` (so for instance, dropout modules are
deactivated). To train the model, you should first set it back in training mode with `),ome=n(B9e,"CODE",{});var rft=s(ome);Q$o=r(rft,"model.train()"),rft.forEach(t),B9e.forEach(t),H$o=i(na),T(zb.$$.fragment,na),na.forEach(t),Ws.forEach(t),aje=i(f),Gi=n(f,"H2",{class:!0});var iGe=s(Gi);Wb=n(iGe,"A",{id:!0,class:!0,href:!0});var tft=s(Wb);rme=n(tft,"SPAN",{});var aft=s(rme);T(R6.$$.fragment,aft),aft.forEach(t),tft.forEach(t),U$o=i(iGe),tme=n(iGe,"SPAN",{});var nft=s(tme);J$o=r(nft,"AutoModelForSequenceClassification"),nft.forEach(t),iGe.forEach(t),nje=i(f),Ro=n(f,"DIV",{class:!0});var Qs=s(Ro);T(P6.$$.fragment,Qs),Y$o=i(Qs),Oi=n(Qs,"P",{});var zZ=s(Oi);K$o=r(zZ,`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 `),FG=n(zZ,"A",{href:!0});var sft=s(FG);Z$o=r(sft,"from_pretrained()"),sft.forEach(t),eko=r(zZ," class method or the "),TG=n(zZ,"A",{href:!0});var lft=s(TG);oko=r(lft,"from_config()"),lft.forEach(t),rko=r(zZ,` class
method.`),zZ.forEach(t),tko=i(Qs),B6=n(Qs,"P",{});var dGe=s(B6);ako=r(dGe,"This class cannot be instantiated directly using "),ame=n(dGe,"CODE",{});var ift=s(ame);nko=r(ift,"__init__()"),ift.forEach(t),sko=r(dGe," (throws an error)."),dGe.forEach(t),lko=i(Qs),it=n(Qs,"DIV",{class:!0});var J0=s(it);T(I6.$$.fragment,J0),iko=i(J0),nme=n(J0,"P",{});var dft=s(nme);dko=r(dft,"Instantiates one of the model classes of the library (with a sequence classification head) from a configuration."),dft.forEach(t),cko=i(J0),Vi=n(J0,"P",{});var WZ=s(Vi);fko=r(WZ,`Note:
Loading a model from its configuration file does `),sme=n(WZ,"STRONG",{});var cft=s(sme);mko=r(cft,"not"),cft.forEach(t),gko=r(WZ,` load the model weights. It only affects the
model\u2019s configuration. Use `),MG=n(WZ,"A",{href:!0});var fft=s(MG);hko=r(fft,"from_pretrained()"),fft.forEach(t),pko=r(WZ," to load the model weights."),WZ.forEach(t),_ko=i(J0),T(Qb.$$.fragment,J0),J0.forEach(t),uko=i(Qs),oo=n(Qs,"DIV",{class:!0});var sa=s(oo);T(N6.$$.fragment,sa),bko=i(sa),lme=n(sa,"P",{});var mft=s(lme);vko=r(mft,"Instantiate one of the model classes of the library (with a sequence classification head) from a pretrained model."),mft.forEach(t),Fko=i(sa),Ra=n(sa,"P",{});var Y0=s(Ra);Tko=r(Y0,"The model class to instantiate is selected based on the "),ime=n(Y0,"CODE",{});var gft=s(ime);Mko=r(gft,"model_type"),gft.forEach(t),Eko=r(Y0,` property of the config object (either
passed as an argument or loaded from `),dme=n(Y0,"CODE",{});var hft=s(dme);Cko=r(hft,"pretrained_model_name_or_path"),hft.forEach(t),wko=r(Y0,` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),cme=n(Y0,"CODE",{});var pft=s(cme);Ako=r(pft,"pretrained_model_name_or_path"),pft.forEach(t),yko=r(Y0,":"),Y0.forEach(t),Lko=i(sa),N=n(sa,"UL",{});var j=s(N);Hb=n(j,"LI",{});var I9e=s(Hb);fme=n(I9e,"STRONG",{});var _ft=s(fme);xko=r(_ft,"albert"),_ft.forEach(t),$ko=r(I9e," \u2014 "),EG=n(I9e,"A",{href:!0});var uft=s(EG);kko=r(uft,"AlbertForSequenceClassification"),uft.forEach(t),Sko=r(I9e," (ALBERT model)"),I9e.forEach(t),Rko=i(j),Ub=n(j,"LI",{});var N9e=s(Ub);mme=n(N9e,"STRONG",{});var bft=s(mme);Pko=r(bft,"bart"),bft.forEach(t),Bko=r(N9e," \u2014 "),CG=n(N9e,"A",{href:!0});var vft=s(CG);Iko=r(vft,"BartForSequenceClassification"),vft.forEach(t),Nko=r(N9e," (BART model)"),N9e.forEach(t),qko=i(j),Jb=n(j,"LI",{});var q9e=s(Jb);gme=n(q9e,"STRONG",{});var Fft=s(gme);jko=r(Fft,"bert"),Fft.forEach(t),Dko=r(q9e," \u2014 "),wG=n(q9e,"A",{href:!0});var Tft=s(wG);Gko=r(Tft,"BertForSequenceClassification"),Tft.forEach(t),Oko=r(q9e," (BERT model)"),q9e.forEach(t),Vko=i(j),Yb=n(j,"LI",{});var j9e=s(Yb);hme=n(j9e,"STRONG",{});var Mft=s(hme);Xko=r(Mft,"big_bird"),Mft.forEach(t),zko=r(j9e," \u2014 "),AG=n(j9e,"A",{href:!0});var Eft=s(AG);Wko=r(Eft,"BigBirdForSequenceClassification"),Eft.forEach(t),Qko=r(j9e," (BigBird model)"),j9e.forEach(t),Hko=i(j),Kb=n(j,"LI",{});var D9e=s(Kb);pme=n(D9e,"STRONG",{});var Cft=s(pme);Uko=r(Cft,"bigbird_pegasus"),Cft.forEach(t),Jko=r(D9e," \u2014 "),yG=n(D9e,"A",{href:!0});var wft=s(yG);Yko=r(wft,"BigBirdPegasusForSequenceClassification"),wft.forEach(t),Kko=r(D9e," (BigBird-Pegasus model)"),D9e.forEach(t),Zko=i(j),Zb=n(j,"LI",{});var G9e=s(Zb);_me=n(G9e,"STRONG",{});var Aft=s(_me);eSo=r(Aft,"camembert"),Aft.forEach(t),oSo=r(G9e," \u2014 "),LG=n(G9e,"A",{href:!0});var yft=s(LG);rSo=r(yft,"CamembertForSequenceClassification"),yft.forEach(t),tSo=r(G9e," (CamemBERT model)"),G9e.forEach(t),aSo=i(j),e2=n(j,"LI",{});var O9e=s(e2);ume=n(O9e,"STRONG",{});var Lft=s(ume);nSo=r(Lft,"canine"),Lft.forEach(t),sSo=r(O9e," \u2014 "),xG=n(O9e,"A",{href:!0});var xft=s(xG);lSo=r(xft,"CanineForSequenceClassification"),xft.forEach(t),iSo=r(O9e," (CANINE model)"),O9e.forEach(t),dSo=i(j),o2=n(j,"LI",{});var V9e=s(o2);bme=n(V9e,"STRONG",{});var $ft=s(bme);cSo=r($ft,"convbert"),$ft.forEach(t),fSo=r(V9e," \u2014 "),$G=n(V9e,"A",{href:!0});var kft=s($G);mSo=r(kft,"ConvBertForSequenceClassification"),kft.forEach(t),gSo=r(V9e," (ConvBERT model)"),V9e.forEach(t),hSo=i(j),r2=n(j,"LI",{});var X9e=s(r2);vme=n(X9e,"STRONG",{});var Sft=s(vme);pSo=r(Sft,"ctrl"),Sft.forEach(t),_So=r(X9e," \u2014 "),kG=n(X9e,"A",{href:!0});var Rft=s(kG);uSo=r(Rft,"CTRLForSequenceClassification"),Rft.forEach(t),bSo=r(X9e," (CTRL model)"),X9e.forEach(t),vSo=i(j),t2=n(j,"LI",{});var z9e=s(t2);Fme=n(z9e,"STRONG",{});var Pft=s(Fme);FSo=r(Pft,"data2vec-text"),Pft.forEach(t),TSo=r(z9e," \u2014 "),SG=n(z9e,"A",{href:!0});var Bft=s(SG);MSo=r(Bft,"Data2VecTextForSequenceClassification"),Bft.forEach(t),ESo=r(z9e," (Data2VecText model)"),z9e.forEach(t),CSo=i(j),a2=n(j,"LI",{});var W9e=s(a2);Tme=n(W9e,"STRONG",{});var Ift=s(Tme);wSo=r(Ift,"deberta"),Ift.forEach(t),ASo=r(W9e," \u2014 "),RG=n(W9e,"A",{href:!0});var Nft=s(RG);ySo=r(Nft,"DebertaForSequenceClassification"),Nft.forEach(t),LSo=r(W9e," (DeBERTa model)"),W9e.forEach(t),xSo=i(j),n2=n(j,"LI",{});var Q9e=s(n2);Mme=n(Q9e,"STRONG",{});var qft=s(Mme);$So=r(qft,"deberta-v2"),qft.forEach(t),kSo=r(Q9e," \u2014 "),PG=n(Q9e,"A",{href:!0});var jft=s(PG);SSo=r(jft,"DebertaV2ForSequenceClassification"),jft.forEach(t),RSo=r(Q9e," (DeBERTa-v2 model)"),Q9e.forEach(t),PSo=i(j),s2=n(j,"LI",{});var H9e=s(s2);Eme=n(H9e,"STRONG",{});var Dft=s(Eme);BSo=r(Dft,"distilbert"),Dft.forEach(t),ISo=r(H9e," \u2014 "),BG=n(H9e,"A",{href:!0});var Gft=s(BG);NSo=r(Gft,"DistilBertForSequenceClassification"),Gft.forEach(t),qSo=r(H9e," (DistilBERT model)"),H9e.forEach(t),jSo=i(j),l2=n(j,"LI",{});var U9e=s(l2);Cme=n(U9e,"STRONG",{});var Oft=s(Cme);DSo=r(Oft,"electra"),Oft.forEach(t),GSo=r(U9e," \u2014 "),IG=n(U9e,"A",{href:!0});var Vft=s(IG);OSo=r(Vft,"ElectraForSequenceClassification"),Vft.forEach(t),VSo=r(U9e," (ELECTRA model)"),U9e.forEach(t),XSo=i(j),i2=n(j,"LI",{});var J9e=s(i2);wme=n(J9e,"STRONG",{});var Xft=s(wme);zSo=r(Xft,"flaubert"),Xft.forEach(t),WSo=r(J9e," \u2014 "),NG=n(J9e,"A",{href:!0});var zft=s(NG);QSo=r(zft,"FlaubertForSequenceClassification"),zft.forEach(t),HSo=r(J9e," (FlauBERT model)"),J9e.forEach(t),USo=i(j),d2=n(j,"LI",{});var Y9e=s(d2);Ame=n(Y9e,"STRONG",{});var Wft=s(Ame);JSo=r(Wft,"fnet"),Wft.forEach(t),YSo=r(Y9e," \u2014 "),qG=n(Y9e,"A",{href:!0});var Qft=s(qG);KSo=r(Qft,"FNetForSequenceClassification"),Qft.forEach(t),ZSo=r(Y9e," (FNet model)"),Y9e.forEach(t),eRo=i(j),c2=n(j,"LI",{});var K9e=s(c2);yme=n(K9e,"STRONG",{});var Hft=s(yme);oRo=r(Hft,"funnel"),Hft.forEach(t),rRo=r(K9e," \u2014 "),jG=n(K9e,"A",{href:!0});var Uft=s(jG);tRo=r(Uft,"FunnelForSequenceClassification"),Uft.forEach(t),aRo=r(K9e," (Funnel Transformer model)"),K9e.forEach(t),nRo=i(j),f2=n(j,"LI",{});var Z9e=s(f2);Lme=n(Z9e,"STRONG",{});var Jft=s(Lme);sRo=r(Jft,"gpt2"),Jft.forEach(t),lRo=r(Z9e," \u2014 "),DG=n(Z9e,"A",{href:!0});var Yft=s(DG);iRo=r(Yft,"GPT2ForSequenceClassification"),Yft.forEach(t),dRo=r(Z9e," (OpenAI GPT-2 model)"),Z9e.forEach(t),cRo=i(j),m2=n(j,"LI",{});var exe=s(m2);xme=n(exe,"STRONG",{});var Kft=s(xme);fRo=r(Kft,"gpt_neo"),Kft.forEach(t),mRo=r(exe," \u2014 "),GG=n(exe,"A",{href:!0});var Zft=s(GG);gRo=r(Zft,"GPTNeoForSequenceClassification"),Zft.forEach(t),hRo=r(exe," (GPT Neo model)"),exe.forEach(t),pRo=i(j),g2=n(j,"LI",{});var oxe=s(g2);$me=n(oxe,"STRONG",{});var emt=s($me);_Ro=r(emt,"gptj"),emt.forEach(t),uRo=r(oxe," \u2014 "),OG=n(oxe,"A",{href:!0});var omt=s(OG);bRo=r(omt,"GPTJForSequenceClassification"),omt.forEach(t),vRo=r(oxe," (GPT-J model)"),oxe.forEach(t),FRo=i(j),h2=n(j,"LI",{});var rxe=s(h2);kme=n(rxe,"STRONG",{});var rmt=s(kme);TRo=r(rmt,"ibert"),rmt.forEach(t),MRo=r(rxe," \u2014 "),VG=n(rxe,"A",{href:!0});var tmt=s(VG);ERo=r(tmt,"IBertForSequenceClassification"),tmt.forEach(t),CRo=r(rxe," (I-BERT model)"),rxe.forEach(t),wRo=i(j),p2=n(j,"LI",{});var txe=s(p2);Sme=n(txe,"STRONG",{});var amt=s(Sme);ARo=r(amt,"layoutlm"),amt.forEach(t),yRo=r(txe," \u2014 "),XG=n(txe,"A",{href:!0});var nmt=s(XG);LRo=r(nmt,"LayoutLMForSequenceClassification"),nmt.forEach(t),xRo=r(txe," (LayoutLM model)"),txe.forEach(t),$Ro=i(j),_2=n(j,"LI",{});var axe=s(_2);Rme=n(axe,"STRONG",{});var smt=s(Rme);kRo=r(smt,"layoutlmv2"),smt.forEach(t),SRo=r(axe," \u2014 "),zG=n(axe,"A",{href:!0});var lmt=s(zG);RRo=r(lmt,"LayoutLMv2ForSequenceClassification"),lmt.forEach(t),PRo=r(axe," (LayoutLMv2 model)"),axe.forEach(t),BRo=i(j),u2=n(j,"LI",{});var nxe=s(u2);Pme=n(nxe,"STRONG",{});var imt=s(Pme);IRo=r(imt,"layoutlmv3"),imt.forEach(t),NRo=r(nxe," 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xmt=s(rO);PPo=r(xmt,"PLBartForSequenceClassification"),xmt.forEach(t),BPo=r(pxe," (PLBart model)"),pxe.forEach(t),IPo=i(j),L2=n(j,"LI",{});var _xe=s(L2);zme=n(_xe,"STRONG",{});var $mt=s(zme);NPo=r($mt,"qdqbert"),$mt.forEach(t),qPo=r(_xe," \u2014 "),tO=n(_xe,"A",{href:!0});var kmt=s(tO);jPo=r(kmt,"QDQBertForSequenceClassification"),kmt.forEach(t),DPo=r(_xe," (QDQBert model)"),_xe.forEach(t),GPo=i(j),x2=n(j,"LI",{});var uxe=s(x2);Wme=n(uxe,"STRONG",{});var Smt=s(Wme);OPo=r(Smt,"reformer"),Smt.forEach(t),VPo=r(uxe," \u2014 "),aO=n(uxe,"A",{href:!0});var Rmt=s(aO);XPo=r(Rmt,"ReformerForSequenceClassification"),Rmt.forEach(t),zPo=r(uxe," (Reformer model)"),uxe.forEach(t),WPo=i(j),$2=n(j,"LI",{});var bxe=s($2);Qme=n(bxe,"STRONG",{});var Pmt=s(Qme);QPo=r(Pmt,"rembert"),Pmt.forEach(t),HPo=r(bxe," \u2014 "),nO=n(bxe,"A",{href:!0});var Bmt=s(nO);UPo=r(Bmt,"RemBertForSequenceClassification"),Bmt.forEach(t),JPo=r(bxe," (RemBERT model)"),bxe.forEach(t),YPo=i(j),k2=n(j,"LI",{});var vxe=s(k2);Hme=n(vxe,"STRONG",{});var Imt=s(Hme);KPo=r(Imt,"roberta"),Imt.forEach(t),ZPo=r(vxe," \u2014 "),sO=n(vxe,"A",{href:!0});var Nmt=s(sO);eBo=r(Nmt,"RobertaForSequenceClassification"),Nmt.forEach(t),oBo=r(vxe," (RoBERTa model)"),vxe.forEach(t),rBo=i(j),S2=n(j,"LI",{});var Fxe=s(S2);Ume=n(Fxe,"STRONG",{});var qmt=s(Ume);tBo=r(qmt,"roformer"),qmt.forEach(t),aBo=r(Fxe," \u2014 "),lO=n(Fxe,"A",{href:!0});var jmt=s(lO);nBo=r(jmt,"RoFormerForSequenceClassification"),jmt.forEach(t),sBo=r(Fxe," (RoFormer model)"),Fxe.forEach(t),lBo=i(j),R2=n(j,"LI",{});var Txe=s(R2);Jme=n(Txe,"STRONG",{});var Dmt=s(Jme);iBo=r(Dmt,"squeezebert"),Dmt.forEach(t),dBo=r(Txe," \u2014 "),iO=n(Txe,"A",{href:!0});var Gmt=s(iO);cBo=r(Gmt,"SqueezeBertForSequenceClassification"),Gmt.forEach(t),fBo=r(Txe," (SqueezeBERT model)"),Txe.forEach(t),mBo=i(j),P2=n(j,"LI",{});var Mxe=s(P2);Yme=n(Mxe,"STRONG",{});var Omt=s(Yme);gBo=r(Omt,"tapas"),Omt.forEach(t),hBo=r(Mxe," \u2014 "),dO=n(Mxe,"A",{href:!0});var Vmt=s(dO);pBo=r(Vmt,"TapasForSequenceClassification"),Vmt.forEach(t),_Bo=r(Mxe," (TAPAS model)"),Mxe.forEach(t),uBo=i(j),B2=n(j,"LI",{});var Exe=s(B2);Kme=n(Exe,"STRONG",{});var Xmt=s(Kme);bBo=r(Xmt,"transfo-xl"),Xmt.forEach(t),vBo=r(Exe," \u2014 "),cO=n(Exe,"A",{href:!0});var zmt=s(cO);FBo=r(zmt,"TransfoXLForSequenceClassification"),zmt.forEach(t),TBo=r(Exe," (Transformer-XL model)"),Exe.forEach(t),MBo=i(j),I2=n(j,"LI",{});var Cxe=s(I2);Zme=n(Cxe,"STRONG",{});var Wmt=s(Zme);EBo=r(Wmt,"xlm"),Wmt.forEach(t),CBo=r(Cxe," \u2014 "),fO=n(Cxe,"A",{href:!0});var Qmt=s(fO);wBo=r(Qmt,"XLMForSequenceClassification"),Qmt.forEach(t),ABo=r(Cxe," (XLM model)"),Cxe.forEach(t),yBo=i(j),N2=n(j,"LI",{});var wxe=s(N2);ege=n(wxe,"STRONG",{});var Hmt=s(ege);LBo=r(Hmt,"xlm-roberta"),Hmt.forEach(t),xBo=r(wxe," \u2014 "),mO=n(wxe,"A",{href:!0});var Umt=s(mO);$Bo=r(Umt,"XLMRobertaForSequenceClassification"),Umt.forEach(t),kBo=r(wxe," (XLM-RoBERTa model)"),wxe.forEach(t),SBo=i(j),q2=n(j,"LI",{});var Axe=s(q2);oge=n(Axe,"STRONG",{});var Jmt=s(oge);RBo=r(Jmt,"xlm-roberta-xl"),Jmt.forEach(t),PBo=r(Axe," \u2014 "),gO=n(Axe,"A",{href:!0});var Ymt=s(gO);BBo=r(Ymt,"XLMRobertaXLForSequenceClassification"),Ymt.forEach(t),IBo=r(Axe," (XLM-RoBERTa-XL model)"),Axe.forEach(t),NBo=i(j),j2=n(j,"LI",{});var yxe=s(j2);rge=n(yxe,"STRONG",{});var Kmt=s(rge);qBo=r(Kmt,"xlnet"),Kmt.forEach(t),jBo=r(yxe," \u2014 "),hO=n(yxe,"A",{href:!0});var Zmt=s(hO);DBo=r(Zmt,"XLNetForSequenceClassification"),Zmt.forEach(t),GBo=r(yxe," (XLNet model)"),yxe.forEach(t),OBo=i(j),D2=n(j,"LI",{});var Lxe=s(D2);tge=n(Lxe,"STRONG",{});var egt=s(tge);VBo=r(egt,"yoso"),egt.forEach(t),XBo=r(Lxe," \u2014 "),pO=n(Lxe,"A",{href:!0});var ogt=s(pO);zBo=r(ogt,"YosoForSequenceClassification"),ogt.forEach(t),WBo=r(Lxe," (YOSO model)"),Lxe.forEach(t),j.forEach(t),QBo=i(sa),G2=n(sa,"P",{});var xxe=s(G2);HBo=r(xxe,"The model is set in evaluation mode by default using "),age=n(xxe,"CODE",{});var rgt=s(age);UBo=r(rgt,"model.eval()"),rgt.forEach(t),JBo=r(xxe,` (so for instance, dropout modules are
deactivated). To train the model, you should first set it back in training mode with `),nge=n(xxe,"CODE",{});var tgt=s(nge);YBo=r(tgt,"model.train()"),tgt.forEach(t),xxe.forEach(t),KBo=i(sa),T(O2.$$.fragment,sa),sa.forEach(t),Qs.forEach(t),sje=i(f),Xi=n(f,"H2",{class:!0});var cGe=s(Xi);V2=n(cGe,"A",{id:!0,class:!0,href:!0});var agt=s(V2);sge=n(agt,"SPAN",{});var ngt=s(sge);T(q6.$$.fragment,ngt),ngt.forEach(t),agt.forEach(t),ZBo=i(cGe),lge=n(cGe,"SPAN",{});var sgt=s(lge);eIo=r(sgt,"AutoModelForMultipleChoice"),sgt.forEach(t),cGe.forEach(t),lje=i(f),Po=n(f,"DIV",{class:!0});var Hs=s(Po);T(j6.$$.fragment,Hs),oIo=i(Hs),zi=n(Hs,"P",{});var QZ=s(zi);rIo=r(QZ,`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 `),_O=n(QZ,"A",{href:!0});var lgt=s(_O);tIo=r(lgt,"from_pretrained()"),lgt.forEach(t),aIo=r(QZ," class method or the "),uO=n(QZ,"A",{href:!0});var igt=s(uO);nIo=r(igt,"from_config()"),igt.forEach(t),sIo=r(QZ,` class
method.`),QZ.forEach(t),lIo=i(Hs),D6=n(Hs,"P",{});var fGe=s(D6);iIo=r(fGe,"This class cannot be instantiated directly using "),ige=n(fGe,"CODE",{});var dgt=s(ige);dIo=r(dgt,"__init__()"),dgt.forEach(t),cIo=r(fGe," (throws an error)."),fGe.forEach(t),fIo=i(Hs),dt=n(Hs,"DIV",{class:!0});var K0=s(dt);T(G6.$$.fragment,K0),mIo=i(K0),dge=n(K0,"P",{});var cgt=s(dge);gIo=r(cgt,"Instantiates one of the model classes of the library (with a multiple choice head) from a configuration."),cgt.forEach(t),hIo=i(K0),Wi=n(K0,"P",{});var HZ=s(Wi);pIo=r(HZ,`Note:
Loading a model from its configuration file does `),cge=n(HZ,"STRONG",{});var fgt=s(cge);_Io=r(fgt,"not"),fgt.forEach(t),uIo=r(HZ,` load the model weights. It only affects the
model\u2019s configuration. Use `),bO=n(HZ,"A",{href:!0});var mgt=s(bO);bIo=r(mgt,"from_pretrained()"),mgt.forEach(t),vIo=r(HZ," to load the model weights."),HZ.forEach(t),FIo=i(K0),T(X2.$$.fragment,K0),K0.forEach(t),TIo=i(Hs),ro=n(Hs,"DIV",{class:!0});var la=s(ro);T(O6.$$.fragment,la),MIo=i(la),fge=n(la,"P",{});var ggt=s(fge);EIo=r(ggt,"Instantiate one of the model classes of the library (with a multiple choice head) from a pretrained model."),ggt.forEach(t),CIo=i(la),Pa=n(la,"P",{});var Z0=s(Pa);wIo=r(Z0,"The model class to instantiate is selected based on the "),mge=n(Z0,"CODE",{});var hgt=s(mge);AIo=r(hgt,"model_type"),hgt.forEach(t),yIo=r(Z0,` property of the config object (either
passed as an argument or loaded from `),gge=n(Z0,"CODE",{});var pgt=s(gge);LIo=r(pgt,"pretrained_model_name_or_path"),pgt.forEach(t),xIo=r(Z0,` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),hge=n(Z0,"CODE",{});var _gt=s(hge);$Io=r(_gt,"pretrained_model_name_or_path"),_gt.forEach(t),kIo=r(Z0,":"),Z0.forEach(t),SIo=i(la),K=n(la,"UL",{});var ee=s(K);z2=n(ee,"LI",{});var $xe=s(z2);pge=n($xe,"STRONG",{});var ugt=s(pge);RIo=r(ugt,"albert"),ugt.forEach(t),PIo=r($xe," \u2014 "),vO=n($xe,"A",{href:!0});var bgt=s(vO);BIo=r(bgt,"AlbertForMultipleChoice"),bgt.forEach(t),IIo=r($xe," (ALBERT model)"),$xe.forEach(t),NIo=i(ee),W2=n(ee,"LI",{});var kxe=s(W2);_ge=n(kxe,"STRONG",{});var vgt=s(_ge);qIo=r(vgt,"bert"),vgt.forEach(t),jIo=r(kxe," \u2014 "),FO=n(kxe,"A",{href:!0});var Fgt=s(FO);DIo=r(Fgt,"BertForMultipleChoice"),Fgt.forEach(t),GIo=r(kxe," (BERT model)"),kxe.forEach(t),OIo=i(ee),Q2=n(ee,"LI",{});var Sxe=s(Q2);uge=n(Sxe,"STRONG",{});var Tgt=s(uge);VIo=r(Tgt,"big_bird"),Tgt.forEach(t),XIo=r(Sxe," \u2014 "),TO=n(Sxe,"A",{href:!0});var Mgt=s(TO);zIo=r(Mgt,"BigBirdForMultipleChoice"),Mgt.forEach(t),WIo=r(Sxe," (BigBird model)"),Sxe.forEach(t),QIo=i(ee),H2=n(ee,"LI",{});var Rxe=s(H2);bge=n(Rxe,"STRONG",{});var Egt=s(bge);HIo=r(Egt,"camembert"),Egt.forEach(t),UIo=r(Rxe," \u2014 "),MO=n(Rxe,"A",{href:!0});var Cgt=s(MO);JIo=r(Cgt,"CamembertForMultipleChoice"),Cgt.forEach(t),YIo=r(Rxe," (CamemBERT model)"),Rxe.forEach(t),KIo=i(ee),U2=n(ee,"LI",{});var Pxe=s(U2);vge=n(Pxe,"STRONG",{});var wgt=s(vge);ZIo=r(wgt,"canine"),wgt.forEach(t),eNo=r(Pxe," \u2014 "),EO=n(Pxe,"A",{href:!0});var Agt=s(EO);oNo=r(Agt,"CanineForMultipleChoice"),Agt.forEach(t),rNo=r(Pxe," (CANINE model)"),Pxe.forEach(t),tNo=i(ee),J2=n(ee,"LI",{});var Bxe=s(J2);Fge=n(Bxe,"STRONG",{});var ygt=s(Fge);aNo=r(ygt,"convbert"),ygt.forEach(t),nNo=r(Bxe," \u2014 "),CO=n(Bxe,"A",{href:!0});var Lgt=s(CO);sNo=r(Lgt,"ConvBertForMultipleChoice"),Lgt.forEach(t),lNo=r(Bxe," (ConvBERT model)"),Bxe.forEach(t),iNo=i(ee),Y2=n(ee,"LI",{});var Ixe=s(Y2);Tge=n(Ixe,"STRONG",{});var xgt=s(Tge);dNo=r(xgt,"data2vec-text"),xgt.forEach(t),cNo=r(Ixe," \u2014 "),wO=n(Ixe,"A",{href:!0});var $gt=s(wO);fNo=r($gt,"Data2VecTextForMultipleChoice"),$gt.forEach(t),mNo=r(Ixe," (Data2VecText model)"),Ixe.forEach(t),gNo=i(ee),K2=n(ee,"LI",{});var Nxe=s(K2);Mge=n(Nxe,"STRONG",{});var kgt=s(Mge);hNo=r(kgt,"deberta-v2"),kgt.forEach(t),pNo=r(Nxe," \u2014 "),AO=n(Nxe,"A",{href:!0});var Sgt=s(AO);_No=r(Sgt,"DebertaV2ForMultipleChoice"),Sgt.forEach(t),uNo=r(Nxe," (DeBERTa-v2 model)"),Nxe.forEach(t),bNo=i(ee),Z2=n(ee,"LI",{});var qxe=s(Z2);Ege=n(qxe,"STRONG",{});var Rgt=s(Ege);vNo=r(Rgt,"distilbert"),Rgt.forEach(t),FNo=r(qxe," \u2014 "),yO=n(qxe,"A",{href:!0});var Pgt=s(yO);TNo=r(Pgt,"DistilBertForMultipleChoice"),Pgt.forEach(t),MNo=r(qxe," (DistilBERT model)"),qxe.forEach(t),ENo=i(ee),e4=n(ee,"LI",{});var jxe=s(e4);Cge=n(jxe,"STRONG",{});var Bgt=s(Cge);CNo=r(Bgt,"electra"),Bgt.forEach(t),wNo=r(jxe," \u2014 "),LO=n(jxe,"A",{href:!0});var Igt=s(LO);ANo=r(Igt,"ElectraForMultipleChoice"),Igt.forEach(t),yNo=r(jxe," (ELECTRA 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with the `),HO=n(UZ,"A",{href:!0});var Aht=s(HO);wjo=r(Aht,"from_pretrained()"),Aht.forEach(t),Ajo=r(UZ," class method or the "),UO=n(UZ,"A",{href:!0});var yht=s(UO);yjo=r(yht,"from_config()"),yht.forEach(t),Ljo=r(UZ,` class
method.`),UZ.forEach(t),xjo=i(Us),z6=n(Us,"P",{});var gGe=s(z6);$jo=r(gGe,"This class cannot be instantiated directly using "),Hge=n(gGe,"CODE",{});var Lht=s(Hge);kjo=r(Lht,"__init__()"),Lht.forEach(t),Sjo=r(gGe," (throws an error)."),gGe.forEach(t),Rjo=i(Us),ct=n(Us,"DIV",{class:!0});var ew=s(ct);T(W6.$$.fragment,ew),Pjo=i(ew),Uge=n(ew,"P",{});var xht=s(Uge);Bjo=r(xht,"Instantiates one of the model classes of the library (with a next sentence prediction head) from a configuration."),xht.forEach(t),Ijo=i(ew),Ui=n(ew,"P",{});var JZ=s(Ui);Njo=r(JZ,`Note:
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model\u2019s configuration. Use `),JO=n(JZ,"A",{href:!0});var kht=s(JO);Djo=r(kht,"from_pretrained()"),kht.forEach(t),Gjo=r(JZ," to load the model weights."),JZ.forEach(t),Ojo=i(ew),T(E4.$$.fragment,ew),ew.forEach(t),Vjo=i(Us),to=n(Us,"DIV",{class:!0});var ia=s(to);T(Q6.$$.fragment,ia),Xjo=i(ia),Yge=n(ia,"P",{});var Sht=s(Yge);zjo=r(Sht,"Instantiate one of the model classes of the library (with a next sentence prediction head) from a pretrained model."),Sht.forEach(t),Wjo=i(ia),Ba=n(ia,"P",{});var ow=s(Ba);Qjo=r(ow,"The model class to instantiate is selected based on the "),Kge=n(ow,"CODE",{});var Rht=s(Kge);Hjo=r(Rht,"model_type"),Rht.forEach(t),Ujo=r(ow,` property of the config object (either
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falling back to using pattern matching on `),ehe=n(ow,"CODE",{});var Bht=s(ehe);Kjo=r(Bht,"pretrained_model_name_or_path"),Bht.forEach(t),Zjo=r(ow,":"),ow.forEach(t),eDo=i(ia),Yr=n(ia,"UL",{});var Js=s(Yr);C4=n(Js,"LI",{});var s$e=s(C4);ohe=n(s$e,"STRONG",{});var Iht=s(ohe);oDo=r(Iht,"bert"),Iht.forEach(t),rDo=r(s$e," \u2014 "),YO=n(s$e,"A",{href:!0});var Nht=s(YO);tDo=r(Nht,"BertForNextSentencePrediction"),Nht.forEach(t),aDo=r(s$e," (BERT model)"),s$e.forEach(t),nDo=i(Js),w4=n(Js,"LI",{});var l$e=s(w4);rhe=n(l$e,"STRONG",{});var qht=s(rhe);sDo=r(qht,"fnet"),qht.forEach(t),lDo=r(l$e," \u2014 "),KO=n(l$e,"A",{href:!0});var jht=s(KO);iDo=r(jht,"FNetForNextSentencePrediction"),jht.forEach(t),dDo=r(l$e," (FNet model)"),l$e.forEach(t),cDo=i(Js),A4=n(Js,"LI",{});var i$e=s(A4);the=n(i$e,"STRONG",{});var Dht=s(the);fDo=r(Dht,"megatron-bert"),Dht.forEach(t),mDo=r(i$e," \u2014 "),ZO=n(i$e,"A",{href:!0});var Ght=s(ZO);gDo=r(Ght,"MegatronBertForNextSentencePrediction"),Ght.forEach(t),hDo=r(i$e," (Megatron-BERT model)"),i$e.forEach(t),pDo=i(Js),y4=n(Js,"LI",{});var d$e=s(y4);ahe=n(d$e,"STRONG",{});var Oht=s(ahe);_Do=r(Oht,"mobilebert"),Oht.forEach(t),uDo=r(d$e," \u2014 "),eV=n(d$e,"A",{href:!0});var Vht=s(eV);bDo=r(Vht,"MobileBertForNextSentencePrediction"),Vht.forEach(t),vDo=r(d$e," (MobileBERT model)"),d$e.forEach(t),FDo=i(Js),L4=n(Js,"LI",{});var c$e=s(L4);nhe=n(c$e,"STRONG",{});var Xht=s(nhe);TDo=r(Xht,"qdqbert"),Xht.forEach(t),MDo=r(c$e," \u2014 "),oV=n(c$e,"A",{href:!0});var zht=s(oV);EDo=r(zht,"QDQBertForNextSentencePrediction"),zht.forEach(t),CDo=r(c$e," (QDQBert model)"),c$e.forEach(t),Js.forEach(t),wDo=i(ia),x4=n(ia,"P",{});var f$e=s(x4);ADo=r(f$e,"The model is set in evaluation mode by default using "),she=n(f$e,"CODE",{});var Wht=s(she);yDo=r(Wht,"model.eval()"),Wht.forEach(t),LDo=r(f$e,` (so for instance, dropout modules are
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with the `),rV=n(YZ,"A",{href:!0});var Yht=s(rV);BDo=r(Yht,"from_pretrained()"),Yht.forEach(t),IDo=r(YZ," class method or the "),tV=n(YZ,"A",{href:!0});var Kht=s(tV);NDo=r(Kht,"from_config()"),Kht.forEach(t),qDo=r(YZ,` class
method.`),YZ.forEach(t),jDo=i(Ys),J6=n(Ys,"P",{});var pGe=s(J6);DDo=r(pGe,"This class cannot be instantiated directly using "),che=n(pGe,"CODE",{});var Zht=s(che);GDo=r(Zht,"__init__()"),Zht.forEach(t),ODo=r(pGe," (throws an error)."),pGe.forEach(t),VDo=i(Ys),ft=n(Ys,"DIV",{class:!0});var rw=s(ft);T(Y6.$$.fragment,rw),XDo=i(rw),fhe=n(rw,"P",{});var ept=s(fhe);zDo=r(ept,"Instantiates one of the model classes of the library (with a token classification head) from a configuration."),ept.forEach(t),WDo=i(rw),Ki=n(rw,"P",{});var KZ=s(Ki);QDo=r(KZ,`Note:
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model\u2019s configuration. Use `),aV=n(KZ,"A",{href:!0});var rpt=s(aV);JDo=r(rpt,"from_pretrained()"),rpt.forEach(t),YDo=r(KZ," to load the model weights."),KZ.forEach(t),KDo=i(rw),T(S4.$$.fragment,rw),rw.forEach(t),ZDo=i(Ys),ao=n(Ys,"DIV",{class:!0});var da=s(ao);T(K6.$$.fragment,da),eGo=i(da),ghe=n(da,"P",{});var tpt=s(ghe);oGo=r(tpt,"Instantiate one of the model classes of the library (with a token classification head) from a pretrained model."),tpt.forEach(t),rGo=i(da),Ia=n(da,"P",{});var tw=s(Ia);tGo=r(tw,"The model class to instantiate is selected based on the "),hhe=n(tw,"CODE",{});var apt=s(hhe);aGo=r(apt,"model_type"),apt.forEach(t),nGo=r(tw,` property of the config object (either
passed as an argument or loaded from `),phe=n(tw,"CODE",{});var npt=s(phe);sGo=r(npt,"pretrained_model_name_or_path"),npt.forEach(t),lGo=r(tw,` if possible), or when it\u2019s missing, by
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cke=s(Pv);wpe=n(cke,"STRONG",{});var _ut=s(wpe);BWo=r(_ut,"ibert"),_ut.forEach(t),IWo=r(cke," \u2014 "),nX=n(cke,"A",{href:!0});var uut=s(nX);NWo=r(uut,"IBertForQuestionAnswering"),uut.forEach(t),qWo=r(cke," (I-BERT model)"),cke.forEach(t),jWo=i(X),Bv=n(X,"LI",{});var fke=s(Bv);Ape=n(fke,"STRONG",{});var but=s(Ape);DWo=r(but,"layoutlmv2"),but.forEach(t),GWo=r(fke," \u2014 "),sX=n(fke,"A",{href:!0});var vut=s(sX);OWo=r(vut,"LayoutLMv2ForQuestionAnswering"),vut.forEach(t),VWo=r(fke," (LayoutLMv2 model)"),fke.forEach(t),XWo=i(X),Iv=n(X,"LI",{});var mke=s(Iv);ype=n(mke,"STRONG",{});var Fut=s(ype);zWo=r(Fut,"layoutlmv3"),Fut.forEach(t),WWo=r(mke," \u2014 "),lX=n(mke,"A",{href:!0});var Tut=s(lX);QWo=r(Tut,"LayoutLMv3ForQuestionAnswering"),Tut.forEach(t),HWo=r(mke," (LayoutLMv3 model)"),mke.forEach(t),UWo=i(X),Nv=n(X,"LI",{});var gke=s(Nv);Lpe=n(gke,"STRONG",{});var Mut=s(Lpe);JWo=r(Mut,"led"),Mut.forEach(t),YWo=r(gke," \u2014 "),iX=n(gke,"A",{href:!0});var Eut=s(iX);KWo=r(Eut,"LEDForQuestionAnswering"),Eut.forEach(t),ZWo=r(gke," (LED model)"),gke.forEach(t),eQo=i(X),qv=n(X,"LI",{});var hke=s(qv);xpe=n(hke,"STRONG",{});var Cut=s(xpe);oQo=r(Cut,"longformer"),Cut.forEach(t),rQo=r(hke," \u2014 "),dX=n(hke,"A",{href:!0});var wut=s(dX);tQo=r(wut,"LongformerForQuestionAnswering"),wut.forEach(t),aQo=r(hke," (Longformer model)"),hke.forEach(t),nQo=i(X),jv=n(X,"LI",{});var pke=s(jv);$pe=n(pke,"STRONG",{});var Aut=s($pe);sQo=r(Aut,"lxmert"),Aut.forEach(t),lQo=r(pke," \u2014 "),cX=n(pke,"A",{href:!0});var yut=s(cX);iQo=r(yut,"LxmertForQuestionAnswering"),yut.forEach(t),dQo=r(pke," (LXMERT model)"),pke.forEach(t),cQo=i(X),Dv=n(X,"LI",{});var _ke=s(Dv);kpe=n(_ke,"STRONG",{});var Lut=s(kpe);fQo=r(Lut,"mbart"),Lut.forEach(t),mQo=r(_ke," \u2014 "),fX=n(_ke,"A",{href:!0});var xut=s(fX);gQo=r(xut,"MBartForQuestionAnswering"),xut.forEach(t),hQo=r(_ke," (mBART model)"),_ke.forEach(t),pQo=i(X),Gv=n(X,"LI",{});var uke=s(Gv);Spe=n(uke,"STRONG",{});var $ut=s(Spe);_Qo=r($ut,"megatron-bert"),$ut.forEach(t),uQo=r(uke," \u2014 "),mX=n(uke,"A",{href:!0});var kut=s(mX);bQo=r(kut,"MegatronBertForQuestionAnswering"),kut.forEach(t),vQo=r(uke," (Megatron-BERT model)"),uke.forEach(t),FQo=i(X),Ov=n(X,"LI",{});var bke=s(Ov);Rpe=n(bke,"STRONG",{});var Sut=s(Rpe);TQo=r(Sut,"mobilebert"),Sut.forEach(t),MQo=r(bke," \u2014 "),gX=n(bke,"A",{href:!0});var Rut=s(gX);EQo=r(Rut,"MobileBertForQuestionAnswering"),Rut.forEach(t),CQo=r(bke," (MobileBERT model)"),bke.forEach(t),wQo=i(X),Vv=n(X,"LI",{});var vke=s(Vv);Ppe=n(vke,"STRONG",{});var Put=s(Ppe);AQo=r(Put,"mpnet"),Put.forEach(t),yQo=r(vke," \u2014 "),hX=n(vke,"A",{href:!0});var But=s(hX);LQo=r(But,"MPNetForQuestionAnswering"),But.forEach(t),xQo=r(vke," (MPNet model)"),vke.forEach(t),$Qo=i(X),Xv=n(X,"LI",{});var Fke=s(Xv);Bpe=n(Fke,"STRONG",{});var Iut=s(Bpe);kQo=r(Iut,"nystromformer"),Iut.forEach(t),SQo=r(Fke," \u2014 "),pX=n(Fke,"A",{href:!0});var Nut=s(pX);RQo=r(Nut,"NystromformerForQuestionAnswering"),Nut.forEach(t),PQo=r(Fke," (Nystr\xF6mformer model)"),Fke.forEach(t),BQo=i(X),zv=n(X,"LI",{});var Tke=s(zv);Ipe=n(Tke,"STRONG",{});var qut=s(Ipe);IQo=r(qut,"qdqbert"),qut.forEach(t),NQo=r(Tke," \u2014 "),_X=n(Tke,"A",{href:!0});var jut=s(_X);qQo=r(jut,"QDQBertForQuestionAnswering"),jut.forEach(t),jQo=r(Tke," (QDQBert model)"),Tke.forEach(t),DQo=i(X),Wv=n(X,"LI",{});var Mke=s(Wv);Npe=n(Mke,"STRONG",{});var Dut=s(Npe);GQo=r(Dut,"reformer"),Dut.forEach(t),OQo=r(Mke," \u2014 "),uX=n(Mke,"A",{href:!0});var Gut=s(uX);VQo=r(Gut,"ReformerForQuestionAnswering"),Gut.forEach(t),XQo=r(Mke," (Reformer model)"),Mke.forEach(t),zQo=i(X),Qv=n(X,"LI",{});var Eke=s(Qv);qpe=n(Eke,"STRONG",{});var Out=s(qpe);WQo=r(Out,"rembert"),Out.forEach(t),QQo=r(Eke," \u2014 "),bX=n(Eke,"A",{href:!0});var Vut=s(bX);HQo=r(Vut,"RemBertForQuestionAnswering"),Vut.forEach(t),UQo=r(Eke," (RemBERT model)"),Eke.forEach(t),JQo=i(X),Hv=n(X,"LI",{});var Cke=s(Hv);jpe=n(Cke,"STRONG",{});var Xut=s(jpe);YQo=r(Xut,"roberta"),Xut.forEach(t),KQo=r(Cke," \u2014 "),vX=n(Cke,"A",{href:!0});var zut=s(vX);ZQo=r(zut,"RobertaForQuestionAnswering"),zut.forEach(t),eHo=r(Cke," (RoBERTa model)"),Cke.forEach(t),oHo=i(X),Uv=n(X,"LI",{});var wke=s(Uv);Dpe=n(wke,"STRONG",{});var Wut=s(Dpe);rHo=r(Wut,"roformer"),Wut.forEach(t),tHo=r(wke," \u2014 "),FX=n(wke,"A",{href:!0});var Qut=s(FX);aHo=r(Qut,"RoFormerForQuestionAnswering"),Qut.forEach(t),nHo=r(wke," (RoFormer model)"),wke.forEach(t),sHo=i(X),Jv=n(X,"LI",{});var Ake=s(Jv);Gpe=n(Ake,"STRONG",{});var Hut=s(Gpe);lHo=r(Hut,"splinter"),Hut.forEach(t),iHo=r(Ake," \u2014 "),TX=n(Ake,"A",{href:!0});var Uut=s(TX);dHo=r(Uut,"SplinterForQuestionAnswering"),Uut.forEach(t),cHo=r(Ake," (Splinter model)"),Ake.forEach(t),fHo=i(X),Yv=n(X,"LI",{});var yke=s(Yv);Ope=n(yke,"STRONG",{});var Jut=s(Ope);mHo=r(Jut,"squeezebert"),Jut.forEach(t),gHo=r(yke," \u2014 "),MX=n(yke,"A",{href:!0});var Yut=s(MX);hHo=r(Yut,"SqueezeBertForQuestionAnswering"),Yut.forEach(t),pHo=r(yke," (SqueezeBERT model)"),yke.forEach(t),_Ho=i(X),Kv=n(X,"LI",{});var Lke=s(Kv);Vpe=n(Lke,"STRONG",{});var Kut=s(Vpe);uHo=r(Kut,"xlm"),Kut.forEach(t),bHo=r(Lke," \u2014 "),EX=n(Lke,"A",{href:!0});var Zut=s(EX);vHo=r(Zut,"XLMForQuestionAnsweringSimple"),Zut.forEach(t),FHo=r(Lke," (XLM model)"),Lke.forEach(t),THo=i(X),Zv=n(X,"LI",{});var xke=s(Zv);Xpe=n(xke,"STRONG",{});var e1t=s(Xpe);MHo=r(e1t,"xlm-roberta"),e1t.forEach(t),EHo=r(xke," \u2014 "),CX=n(xke,"A",{href:!0});var o1t=s(CX);CHo=r(o1t,"XLMRobertaForQuestionAnswering"),o1t.forEach(t),wHo=r(xke," (XLM-RoBERTa model)"),xke.forEach(t),AHo=i(X),e5=n(X,"LI",{});var $ke=s(e5);zpe=n($ke,"STRONG",{});var r1t=s(zpe);yHo=r(r1t,"xlm-roberta-xl"),r1t.forEach(t),LHo=r($ke," \u2014 "),wX=n($ke,"A",{href:!0});var t1t=s(wX);xHo=r(t1t,"XLMRobertaXLForQuestionAnswering"),t1t.forEach(t),$Ho=r($ke," (XLM-RoBERTa-XL model)"),$ke.forEach(t),kHo=i(X),o5=n(X,"LI",{});var kke=s(o5);Wpe=n(kke,"STRONG",{});var a1t=s(Wpe);SHo=r(a1t,"xlnet"),a1t.forEach(t),RHo=r(kke," \u2014 "),AX=n(kke,"A",{href:!0});var n1t=s(AX);PHo=r(n1t,"XLNetForQuestionAnsweringSimple"),n1t.forEach(t),BHo=r(kke," (XLNet model)"),kke.forEach(t),IHo=i(X),r5=n(X,"LI",{});var Ske=s(r5);Qpe=n(Ske,"STRONG",{});var s1t=s(Qpe);NHo=r(s1t,"yoso"),s1t.forEach(t),qHo=r(Ske," \u2014 "),yX=n(Ske,"A",{href:!0});var l1t=s(yX);jHo=r(l1t,"YosoForQuestionAnswering"),l1t.forEach(t),DHo=r(Ske," (YOSO model)"),Ske.forEach(t),X.forEach(t),GHo=i(ca),t5=n(ca,"P",{});var Rke=s(t5);OHo=r(Rke,"The model is set in evaluation mode by default using "),Hpe=n(Rke,"CODE",{});var i1t=s(Hpe);VHo=r(i1t,"model.eval()"),i1t.forEach(t),XHo=r(Rke,` (so for instance, dropout modules are
deactivated). To train the model, you should first set it back in training mode with `),Upe=n(Rke,"CODE",{});var d1t=s(Upe);zHo=r(d1t,"model.train()"),d1t.forEach(t),Rke.forEach(t),WHo=i(ca),T(a5.$$.fragment,ca),ca.forEach(t),Ks.forEach(t),hje=i(f),rd=n(f,"H2",{class:!0});var bGe=s(rd);n5=n(bGe,"A",{id:!0,class:!0,href:!0});var c1t=s(n5);Jpe=n(c1t,"SPAN",{});var f1t=s(Jpe);T(ay.$$.fragment,f1t),f1t.forEach(t),c1t.forEach(t),QHo=i(bGe),Ype=n(bGe,"SPAN",{});var m1t=s(Ype);HHo=r(m1t,"AutoModelForTableQuestionAnswering"),m1t.forEach(t),bGe.forEach(t),pje=i(f),qo=n(f,"DIV",{class:!0});var Zs=s(qo);T(ny.$$.fragment,Zs),UHo=i(Zs),td=n(Zs,"P",{});var oee=s(td);JHo=r(oee,`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 `),LX=n(oee,"A",{href:!0});var g1t=s(LX);YHo=r(g1t,"from_pretrained()"),g1t.forEach(t),KHo=r(oee," class method or the "),xX=n(oee,"A",{href:!0});var h1t=s(xX);ZHo=r(h1t,"from_config()"),h1t.forEach(t),eUo=r(oee,` class
method.`),oee.forEach(t),oUo=i(Zs),sy=n(Zs,"P",{});var vGe=s(sy);rUo=r(vGe,"This class cannot be instantiated directly using "),Kpe=n(vGe,"CODE",{});var p1t=s(Kpe);tUo=r(p1t,"__init__()"),p1t.forEach(t),aUo=r(vGe," (throws an error)."),vGe.forEach(t),nUo=i(Zs),gt=n(Zs,"DIV",{class:!0});var sw=s(gt);T(ly.$$.fragment,sw),sUo=i(sw),Zpe=n(sw,"P",{});var _1t=s(Zpe);lUo=r(_1t,"Instantiates one of the model classes of the library (with a table question answering head) from a configuration."),_1t.forEach(t),iUo=i(sw),ad=n(sw,"P",{});var ree=s(ad);dUo=r(ree,`Note:
Loading a model from its configuration file does `),e_e=n(ree,"STRONG",{});var u1t=s(e_e);cUo=r(u1t,"not"),u1t.forEach(t),fUo=r(ree,` load the model weights. It only affects the
model\u2019s configuration. Use `),$X=n(ree,"A",{href:!0});var b1t=s($X);mUo=r(b1t,"from_pretrained()"),b1t.forEach(t),gUo=r(ree," to load the model weights."),ree.forEach(t),hUo=i(sw),T(s5.$$.fragment,sw),sw.forEach(t),pUo=i(Zs),so=n(Zs,"DIV",{class:!0});var fa=s(so);T(iy.$$.fragment,fa),_Uo=i(fa),o_e=n(fa,"P",{});var v1t=s(o_e);uUo=r(v1t,"Instantiate one of the model classes of the library (with a table question answering head) from a pretrained model."),v1t.forEach(t),bUo=i(fa),qa=n(fa,"P",{});var lw=s(qa);vUo=r(lw,"The model class to instantiate is selected based on the "),r_e=n(lw,"CODE",{});var F1t=s(r_e);FUo=r(F1t,"model_type"),F1t.forEach(t),TUo=r(lw,` property of the config object (either
passed as an argument or loaded from `),t_e=n(lw,"CODE",{});var T1t=s(t_e);MUo=r(T1t,"pretrained_model_name_or_path"),T1t.forEach(t),EUo=r(lw,` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),a_e=n(lw,"CODE",{});var M1t=s(a_e);CUo=r(M1t,"pretrained_model_name_or_path"),M1t.forEach(t),wUo=r(lw,":"),lw.forEach(t),AUo=i(fa),n_e=n(fa,"UL",{});var E1t=s(n_e);l5=n(E1t,"LI",{});var Pke=s(l5);s_e=n(Pke,"STRONG",{});var C1t=s(s_e);yUo=r(C1t,"tapas"),C1t.forEach(t),LUo=r(Pke," \u2014 "),kX=n(Pke,"A",{href:!0});var w1t=s(kX);xUo=r(w1t,"TapasForQuestionAnswering"),w1t.forEach(t),$Uo=r(Pke," (TAPAS model)"),Pke.forEach(t),E1t.forEach(t),kUo=i(fa),i5=n(fa,"P",{});var Bke=s(i5);SUo=r(Bke,"The model is set in evaluation mode by default using "),l_e=n(Bke,"CODE",{});var A1t=s(l_e);RUo=r(A1t,"model.eval()"),A1t.forEach(t),PUo=r(Bke,` (so for instance, dropout modules are
deactivated). To train the model, you should first set it back in training mode with `),i_e=n(Bke,"CODE",{});var y1t=s(i_e);BUo=r(y1t,"model.train()"),y1t.forEach(t),Bke.forEach(t),IUo=i(fa),T(d5.$$.fragment,fa),fa.forEach(t),Zs.forEach(t),_je=i(f),nd=n(f,"H2",{class:!0});var FGe=s(nd);c5=n(FGe,"A",{id:!0,class:!0,href:!0});var L1t=s(c5);d_e=n(L1t,"SPAN",{});var x1t=s(d_e);T(dy.$$.fragment,x1t),x1t.forEach(t),L1t.forEach(t),NUo=i(FGe),c_e=n(FGe,"SPAN",{});var $1t=s(c_e);qUo=r($1t,"AutoModelForImageClassification"),$1t.forEach(t),FGe.forEach(t),uje=i(f),jo=n(f,"DIV",{class:!0});var el=s(jo);T(cy.$$.fragment,el),jUo=i(el),sd=n(el,"P",{});var tee=s(sd);DUo=r(tee,`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 `),SX=n(tee,"A",{href:!0});var k1t=s(SX);GUo=r(k1t,"from_pretrained()"),k1t.forEach(t),OUo=r(tee," class method or the "),RX=n(tee,"A",{href:!0});var S1t=s(RX);VUo=r(S1t,"from_config()"),S1t.forEach(t),XUo=r(tee,` class
method.`),tee.forEach(t),zUo=i(el),fy=n(el,"P",{});var TGe=s(fy);WUo=r(TGe,"This class cannot be instantiated directly using "),f_e=n(TGe,"CODE",{});var R1t=s(f_e);QUo=r(R1t,"__init__()"),R1t.forEach(t),HUo=r(TGe," (throws an error)."),TGe.forEach(t),UUo=i(el),ht=n(el,"DIV",{class:!0});var iw=s(ht);T(my.$$.fragment,iw),JUo=i(iw),m_e=n(iw,"P",{});var P1t=s(m_e);YUo=r(P1t,"Instantiates one of the model classes of the library (with a image classification head) from a configuration."),P1t.forEach(t),KUo=i(iw),ld=n(iw,"P",{});var aee=s(ld);ZUo=r(aee,`Note:
Loading a model from its configuration file does `),g_e=n(aee,"STRONG",{});var B1t=s(g_e);eJo=r(B1t,"not"),B1t.forEach(t),oJo=r(aee,` load the model weights. It only affects the
model\u2019s configuration. Use `),PX=n(aee,"A",{href:!0});var I1t=s(PX);rJo=r(I1t,"from_pretrained()"),I1t.forEach(t),tJo=r(aee," to load the model weights."),aee.forEach(t),aJo=i(iw),T(f5.$$.fragment,iw),iw.forEach(t),nJo=i(el),lo=n(el,"DIV",{class:!0});var ma=s(lo);T(gy.$$.fragment,ma),sJo=i(ma),h_e=n(ma,"P",{});var N1t=s(h_e);lJo=r(N1t,"Instantiate one of the model classes of the library (with a image classification head) from a pretrained model."),N1t.forEach(t),iJo=i(ma),ja=n(ma,"P",{});var dw=s(ja);dJo=r(dw,"The model class to instantiate is selected based on the "),p_e=n(dw,"CODE",{});var q1t=s(p_e);cJo=r(q1t,"model_type"),q1t.forEach(t),fJo=r(dw,` property of the config object (either
passed as an argument or loaded from `),__e=n(dw,"CODE",{});var j1t=s(__e);mJo=r(j1t,"pretrained_model_name_or_path"),j1t.forEach(t),gJo=r(dw,` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),u_e=n(dw,"CODE",{});var D1t=s(u_e);hJo=r(D1t,"pretrained_model_name_or_path"),D1t.forEach(t),pJo=r(dw,":"),dw.forEach(t),_Jo=i(ma),ve=n(ma,"UL",{});var Te=s(ve);m5=n(Te,"LI",{});var Ike=s(m5);b_e=n(Ike,"STRONG",{});var G1t=s(b_e);uJo=r(G1t,"beit"),G1t.forEach(t),bJo=r(Ike," \u2014 "),BX=n(Ike,"A",{href:!0});var O1t=s(BX);vJo=r(O1t,"BeitForImageClassification"),O1t.forEach(t),FJo=r(Ike," (BEiT model)"),Ike.forEach(t),TJo=i(Te),g5=n(Te,"LI",{});var Nke=s(g5);v_e=n(Nke,"STRONG",{});var V1t=s(v_e);MJo=r(V1t,"convnext"),V1t.forEach(t),EJo=r(Nke," \u2014 "),IX=n(Nke,"A",{href:!0});var X1t=s(IX);CJo=r(X1t,"ConvNextForImageClassification"),X1t.forEach(t),wJo=r(Nke," (ConvNeXT model)"),Nke.forEach(t),AJo=i(Te),h5=n(Te,"LI",{});var qke=s(h5);F_e=n(qke,"STRONG",{});var z1t=s(F_e);yJo=r(z1t,"cvt"),z1t.forEach(t),LJo=r(qke," \u2014 "),NX=n(qke,"A",{href:!0});var W1t=s(NX);xJo=r(W1t,"CvtForImageClassification"),W1t.forEach(t),$Jo=r(qke," (CvT model)"),qke.forEach(t),kJo=i(Te),p5=n(Te,"LI",{});var jke=s(p5);T_e=n(jke,"STRONG",{});var Q1t=s(T_e);SJo=r(Q1t,"data2vec-vision"),Q1t.forEach(t),RJo=r(jke," \u2014 "),qX=n(jke,"A",{href:!0});var H1t=s(qX);PJo=r(H1t,"Data2VecVisionForImageClassification"),H1t.forEach(t),BJo=r(jke," (Data2VecVision model)"),jke.forEach(t),IJo=i(Te),Bs=n(Te,"LI",{});var K$=s(Bs);M_e=n(K$,"STRONG",{});var U1t=s(M_e);NJo=r(U1t,"deit"),U1t.forEach(t),qJo=r(K$," \u2014 "),jX=n(K$,"A",{href:!0});var J1t=s(jX);jJo=r(J1t,"DeiTForImageClassification"),J1t.forEach(t),DJo=r(K$," or "),DX=n(K$,"A",{href:!0});var Y1t=s(DX);GJo=r(Y1t,"DeiTForImageClassificationWithTeacher"),Y1t.forEach(t),OJo=r(K$," (DeiT model)"),K$.forEach(t),VJo=i(Te),_5=n(Te,"LI",{});var Dke=s(_5);E_e=n(Dke,"STRONG",{});var K1t=s(E_e);XJo=r(K1t,"imagegpt"),K1t.forEach(t),zJo=r(Dke," \u2014 "),GX=n(Dke,"A",{href:!0});var Z1t=s(GX);WJo=r(Z1t,"ImageGPTForImageClassification"),Z1t.forEach(t),QJo=r(Dke," (ImageGPT 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Gke=s(u5);A_e=n(Gke,"STRONG",{});var lbt=s(A_e);fYo=r(lbt,"poolformer"),lbt.forEach(t),mYo=r(Gke," \u2014 "),QX=n(Gke,"A",{href:!0});var ibt=s(QX);gYo=r(ibt,"PoolFormerForImageClassification"),ibt.forEach(t),hYo=r(Gke," (PoolFormer model)"),Gke.forEach(t),pYo=i(Te),b5=n(Te,"LI",{});var Oke=s(b5);y_e=n(Oke,"STRONG",{});var dbt=s(y_e);_Yo=r(dbt,"regnet"),dbt.forEach(t),uYo=r(Oke," \u2014 "),HX=n(Oke,"A",{href:!0});var cbt=s(HX);bYo=r(cbt,"RegNetForImageClassification"),cbt.forEach(t),vYo=r(Oke," (RegNet model)"),Oke.forEach(t),FYo=i(Te),v5=n(Te,"LI",{});var Vke=s(v5);L_e=n(Vke,"STRONG",{});var fbt=s(L_e);TYo=r(fbt,"resnet"),fbt.forEach(t),MYo=r(Vke," \u2014 "),UX=n(Vke,"A",{href:!0});var mbt=s(UX);EYo=r(mbt,"ResNetForImageClassification"),mbt.forEach(t),CYo=r(Vke," (ResNet model)"),Vke.forEach(t),wYo=i(Te),F5=n(Te,"LI",{});var Xke=s(F5);x_e=n(Xke,"STRONG",{});var gbt=s(x_e);AYo=r(gbt,"segformer"),gbt.forEach(t),yYo=r(Xke," \u2014 "),JX=n(Xke,"A",{href:!0});var hbt=s(JX);LYo=r(hbt,"SegformerForImageClassification"),hbt.forEach(t),xYo=r(Xke," (SegFormer model)"),Xke.forEach(t),$Yo=i(Te),T5=n(Te,"LI",{});var zke=s(T5);$_e=n(zke,"STRONG",{});var pbt=s($_e);kYo=r(pbt,"swin"),pbt.forEach(t),SYo=r(zke," \u2014 "),YX=n(zke,"A",{href:!0});var _bt=s(YX);RYo=r(_bt,"SwinForImageClassification"),_bt.forEach(t),PYo=r(zke," (Swin Transformer model)"),zke.forEach(t),BYo=i(Te),M5=n(Te,"LI",{});var Wke=s(M5);k_e=n(Wke,"STRONG",{});var ubt=s(k_e);IYo=r(ubt,"van"),ubt.forEach(t),NYo=r(Wke," \u2014 "),KX=n(Wke,"A",{href:!0});var bbt=s(KX);qYo=r(bbt,"VanForImageClassification"),bbt.forEach(t),jYo=r(Wke," (VAN model)"),Wke.forEach(t),DYo=i(Te),E5=n(Te,"LI",{});var Qke=s(E5);S_e=n(Qke,"STRONG",{});var vbt=s(S_e);GYo=r(vbt,"vit"),vbt.forEach(t),OYo=r(Qke," \u2014 "),ZX=n(Qke,"A",{href:!0});var Fbt=s(ZX);VYo=r(Fbt,"ViTForImageClassification"),Fbt.forEach(t),XYo=r(Qke," (ViT model)"),Qke.forEach(t),Te.forEach(t),zYo=i(ma),C5=n(ma,"P",{});var Hke=s(C5);WYo=r(Hke,"The model is set in evaluation mode by default using "),R_e=n(Hke,"CODE",{});var Tbt=s(R_e);QYo=r(Tbt,"model.eval()"),Tbt.forEach(t),HYo=r(Hke,` (so for instance, dropout modules are
deactivated). To train the model, you should first set it back in training mode with `),P_e=n(Hke,"CODE",{});var Mbt=s(P_e);UYo=r(Mbt,"model.train()"),Mbt.forEach(t),Hke.forEach(t),JYo=i(ma),T(w5.$$.fragment,ma),ma.forEach(t),el.forEach(t),bje=i(f),id=n(f,"H2",{class:!0});var MGe=s(id);A5=n(MGe,"A",{id:!0,class:!0,href:!0});var Ebt=s(A5);B_e=n(Ebt,"SPAN",{});var Cbt=s(B_e);T(hy.$$.fragment,Cbt),Cbt.forEach(t),Ebt.forEach(t),YYo=i(MGe),I_e=n(MGe,"SPAN",{});var wbt=s(I_e);KYo=r(wbt,"AutoModelForVision2Seq"),wbt.forEach(t),MGe.forEach(t),vje=i(f),Do=n(f,"DIV",{class:!0});var ol=s(Do);T(py.$$.fragment,ol),ZYo=i(ol),dd=n(ol,"P",{});var nee=s(dd);eKo=r(nee,`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 `),ez=n(nee,"A",{href:!0});var Abt=s(ez);oKo=r(Abt,"from_pretrained()"),Abt.forEach(t),rKo=r(nee," class method or the "),oz=n(nee,"A",{href:!0});var ybt=s(oz);tKo=r(ybt,"from_config()"),ybt.forEach(t),aKo=r(nee,` class
method.`),nee.forEach(t),nKo=i(ol),_y=n(ol,"P",{});var EGe=s(_y);sKo=r(EGe,"This class cannot be instantiated directly using "),N_e=n(EGe,"CODE",{});var Lbt=s(N_e);lKo=r(Lbt,"__init__()"),Lbt.forEach(t),iKo=r(EGe," (throws an error)."),EGe.forEach(t),dKo=i(ol),_t=n(ol,"DIV",{class:!0});var cw=s(_t);T(uy.$$.fragment,cw),cKo=i(cw),q_e=n(cw,"P",{});var xbt=s(q_e);fKo=r(xbt,"Instantiates one of the model classes of the library (with a vision-to-text modeling head) from a configuration."),xbt.forEach(t),mKo=i(cw),cd=n(cw,"P",{});var see=s(cd);gKo=r(see,`Note:
Loading a model from its configuration file does `),j_e=n(see,"STRONG",{});var $bt=s(j_e);hKo=r($bt,"not"),$bt.forEach(t),pKo=r(see,` load the model weights. It only affects the
model\u2019s configuration. Use `),rz=n(see,"A",{href:!0});var kbt=s(rz);_Ko=r(kbt,"from_pretrained()"),kbt.forEach(t),uKo=r(see," to load the model weights."),see.forEach(t),bKo=i(cw),T(y5.$$.fragment,cw),cw.forEach(t),vKo=i(ol),io=n(ol,"DIV",{class:!0});var ga=s(io);T(by.$$.fragment,ga),FKo=i(ga),D_e=n(ga,"P",{});var Sbt=s(D_e);TKo=r(Sbt,"Instantiate one of the model classes of the library (with a vision-to-text modeling head) from a pretrained model."),Sbt.forEach(t),MKo=i(ga),Da=n(ga,"P",{});var fw=s(Da);EKo=r(fw,"The model class to instantiate is selected based on the "),G_e=n(fw,"CODE",{});var Rbt=s(G_e);CKo=r(Rbt,"model_type"),Rbt.forEach(t),wKo=r(fw,` property of the config object (either
passed as an argument or loaded from `),O_e=n(fw,"CODE",{});var Pbt=s(O_e);AKo=r(Pbt,"pretrained_model_name_or_path"),Pbt.forEach(t),yKo=r(fw,` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),V_e=n(fw,"CODE",{});var Bbt=s(V_e);LKo=r(Bbt,"pretrained_model_name_or_path"),Bbt.forEach(t),xKo=r(fw,":"),fw.forEach(t),$Ko=i(ga),X_e=n(ga,"UL",{});var Ibt=s(X_e);L5=n(Ibt,"LI",{});var Uke=s(L5);z_e=n(Uke,"STRONG",{});var Nbt=s(z_e);kKo=r(Nbt,"vision-encoder-decoder"),Nbt.forEach(t),SKo=r(Uke," \u2014 "),tz=n(Uke,"A",{href:!0});var qbt=s(tz);RKo=r(qbt,"VisionEncoderDecoderModel"),qbt.forEach(t),PKo=r(Uke," (Vision Encoder decoder model)"),Uke.forEach(t),Ibt.forEach(t),BKo=i(ga),x5=n(ga,"P",{});var Jke=s(x5);IKo=r(Jke,"The model is set in evaluation mode by default using "),W_e=n(Jke,"CODE",{});var jbt=s(W_e);NKo=r(jbt,"model.eval()"),jbt.forEach(t),qKo=r(Jke,` (so for instance, dropout modules are
deactivated). To train the model, you should first set it back in training mode with `),Q_e=n(Jke,"CODE",{});var Dbt=s(Q_e);jKo=r(Dbt,"model.train()"),Dbt.forEach(t),Jke.forEach(t),DKo=i(ga),T($5.$$.fragment,ga),ga.forEach(t),ol.forEach(t),Fje=i(f),fd=n(f,"H2",{class:!0});var CGe=s(fd);k5=n(CGe,"A",{id:!0,class:!0,href:!0});var Gbt=s(k5);H_e=n(Gbt,"SPAN",{});var Obt=s(H_e);T(vy.$$.fragment,Obt),Obt.forEach(t),Gbt.forEach(t),GKo=i(CGe),U_e=n(CGe,"SPAN",{});var Vbt=s(U_e);OKo=r(Vbt,"AutoModelForAudioClassification"),Vbt.forEach(t),CGe.forEach(t),Tje=i(f),Go=n(f,"DIV",{class:!0});var rl=s(Go);T(Fy.$$.fragment,rl),VKo=i(rl),md=n(rl,"P",{});var lee=s(md);XKo=r(lee,`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 `),az=n(lee,"A",{href:!0});var Xbt=s(az);zKo=r(Xbt,"from_pretrained()"),Xbt.forEach(t),WKo=r(lee," class method or the "),nz=n(lee,"A",{href:!0});var zbt=s(nz);QKo=r(zbt,"from_config()"),zbt.forEach(t),HKo=r(lee,` class
method.`),lee.forEach(t),UKo=i(rl),Ty=n(rl,"P",{});var wGe=s(Ty);JKo=r(wGe,"This class cannot be instantiated directly using "),J_e=n(wGe,"CODE",{});var Wbt=s(J_e);YKo=r(Wbt,"__init__()"),Wbt.forEach(t),KKo=r(wGe," (throws an error)."),wGe.forEach(t),ZKo=i(rl),ut=n(rl,"DIV",{class:!0});var mw=s(ut);T(My.$$.fragment,mw),eZo=i(mw),Y_e=n(mw,"P",{});var Qbt=s(Y_e);oZo=r(Qbt,"Instantiates one of the model classes of the library (with a audio classification head) from a configuration."),Qbt.forEach(t),rZo=i(mw),gd=n(mw,"P",{});var iee=s(gd);tZo=r(iee,`Note:
Loading a model from its configuration file does `),K_e=n(iee,"STRONG",{});var Hbt=s(K_e);aZo=r(Hbt,"not"),Hbt.forEach(t),nZo=r(iee,` load the model weights. It only affects the
model\u2019s configuration. Use `),sz=n(iee,"A",{href:!0});var Ubt=s(sz);sZo=r(Ubt,"from_pretrained()"),Ubt.forEach(t),lZo=r(iee," to load the model weights."),iee.forEach(t),iZo=i(mw),T(S5.$$.fragment,mw),mw.forEach(t),dZo=i(rl),co=n(rl,"DIV",{class:!0});var ha=s(co);T(Ey.$$.fragment,ha),cZo=i(ha),Z_e=n(ha,"P",{});var Jbt=s(Z_e);fZo=r(Jbt,"Instantiate one of the model classes of the library (with a audio classification head) from a pretrained model."),Jbt.forEach(t),mZo=i(ha),Ga=n(ha,"P",{});var gw=s(Ga);gZo=r(gw,"The model class to instantiate is selected based on the "),eue=n(gw,"CODE",{});var Ybt=s(eue);hZo=r(Ybt,"model_type"),Ybt.forEach(t),pZo=r(gw,` property of the config object (either
passed as an argument or loaded from `),oue=n(gw,"CODE",{});var Kbt=s(oue);_Zo=r(Kbt,"pretrained_model_name_or_path"),Kbt.forEach(t),uZo=r(gw,` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),rue=n(gw,"CODE",{});var Zbt=s(rue);bZo=r(Zbt,"pretrained_model_name_or_path"),Zbt.forEach(t),vZo=r(gw,":"),gw.forEach(t),FZo=i(ha),Se=n(ha,"UL",{});var Ve=s(Se);R5=n(Ve,"LI",{});var Yke=s(R5);tue=n(Yke,"STRONG",{});var e2t=s(tue);TZo=r(e2t,"data2vec-audio"),e2t.forEach(t),MZo=r(Yke," \u2014 "),lz=n(Yke,"A",{href:!0});var o2t=s(lz);EZo=r(o2t,"Data2VecAudioForSequenceClassification"),o2t.forEach(t),CZo=r(Yke," (Data2VecAudio model)"),Yke.forEach(t),wZo=i(Ve),P5=n(Ve,"LI",{});var Kke=s(P5);aue=n(Kke,"STRONG",{});var r2t=s(aue);AZo=r(r2t,"hubert"),r2t.forEach(t),yZo=r(Kke," \u2014 "),iz=n(Kke,"A",{href:!0});var t2t=s(iz);LZo=r(t2t,"HubertForSequenceClassification"),t2t.forEach(t),xZo=r(Kke," (Hubert model)"),Kke.forEach(t),$Zo=i(Ve),B5=n(Ve,"LI",{});var Zke=s(B5);nue=n(Zke,"STRONG",{});var a2t=s(nue);kZo=r(a2t,"sew"),a2t.forEach(t),SZo=r(Zke," \u2014 "),dz=n(Zke,"A",{href:!0});var n2t=s(dz);RZo=r(n2t,"SEWForSequenceClassification"),n2t.forEach(t),PZo=r(Zke," (SEW model)"),Zke.forEach(t),BZo=i(Ve),I5=n(Ve,"LI",{});var eSe=s(I5);sue=n(eSe,"STRONG",{});var s2t=s(sue);IZo=r(s2t,"sew-d"),s2t.forEach(t),NZo=r(eSe," \u2014 "),cz=n(eSe,"A",{href:!0});var l2t=s(cz);qZo=r(l2t,"SEWDForSequenceClassification"),l2t.forEach(t),jZo=r(eSe," (SEW-D model)"),eSe.forEach(t),DZo=i(Ve),N5=n(Ve,"LI",{});var oSe=s(N5);lue=n(oSe,"STRONG",{});var i2t=s(lue);GZo=r(i2t,"unispeech"),i2t.forEach(t),OZo=r(oSe," \u2014 "),fz=n(oSe,"A",{href:!0});var d2t=s(fz);VZo=r(d2t,"UniSpeechForSequenceClassification"),d2t.forEach(t),XZo=r(oSe," (UniSpeech model)"),oSe.forEach(t),zZo=i(Ve),q5=n(Ve,"LI",{});var rSe=s(q5);iue=n(rSe,"STRONG",{});var c2t=s(iue);WZo=r(c2t,"unispeech-sat"),c2t.forEach(t),QZo=r(rSe," \u2014 "),mz=n(rSe,"A",{href:!0});var f2t=s(mz);HZo=r(f2t,"UniSpeechSatForSequenceClassification"),f2t.forEach(t),UZo=r(rSe," (UniSpeechSat model)"),rSe.forEach(t),JZo=i(Ve),j5=n(Ve,"LI",{});var tSe=s(j5);due=n(tSe,"STRONG",{});var m2t=s(due);YZo=r(m2t,"wav2vec2"),m2t.forEach(t),KZo=r(tSe," \u2014 "),gz=n(tSe,"A",{href:!0});var g2t=s(gz);ZZo=r(g2t,"Wav2Vec2ForSequenceClassification"),g2t.forEach(t),eer=r(tSe," (Wav2Vec2 model)"),tSe.forEach(t),oer=i(Ve),D5=n(Ve,"LI",{});var aSe=s(D5);cue=n(aSe,"STRONG",{});var h2t=s(cue);rer=r(h2t,"wav2vec2-conformer"),h2t.forEach(t),ter=r(aSe," \u2014 "),hz=n(aSe,"A",{href:!0});var p2t=s(hz);aer=r(p2t,"Wav2Vec2ConformerForSequenceClassification"),p2t.forEach(t),ner=r(aSe," (Wav2Vec2-Conformer model)"),aSe.forEach(t),ser=i(Ve),G5=n(Ve,"LI",{});var nSe=s(G5);fue=n(nSe,"STRONG",{});var _2t=s(fue);ler=r(_2t,"wavlm"),_2t.forEach(t),ier=r(nSe," \u2014 "),pz=n(nSe,"A",{href:!0});var u2t=s(pz);der=r(u2t,"WavLMForSequenceClassification"),u2t.forEach(t),cer=r(nSe," (WavLM model)"),nSe.forEach(t),Ve.forEach(t),fer=i(ha),O5=n(ha,"P",{});var sSe=s(O5);mer=r(sSe,"The model is set in evaluation mode by default using "),mue=n(sSe,"CODE",{});var b2t=s(mue);ger=r(b2t,"model.eval()"),b2t.forEach(t),her=r(sSe,` (so for instance, dropout modules are
deactivated). To train the model, you should first set it back in training mode with `),gue=n(sSe,"CODE",{});var v2t=s(gue);per=r(v2t,"model.train()"),v2t.forEach(t),sSe.forEach(t),_er=i(ha),T(V5.$$.fragment,ha),ha.forEach(t),rl.forEach(t),Mje=i(f),hd=n(f,"H2",{class:!0});var AGe=s(hd);X5=n(AGe,"A",{id:!0,class:!0,href:!0});var F2t=s(X5);hue=n(F2t,"SPAN",{});var T2t=s(hue);T(Cy.$$.fragment,T2t),T2t.forEach(t),F2t.forEach(t),uer=i(AGe),pue=n(AGe,"SPAN",{});var M2t=s(pue);ber=r(M2t,"AutoModelForAudioFrameClassification"),M2t.forEach(t),AGe.forEach(t),Eje=i(f),Oo=n(f,"DIV",{class:!0});var tl=s(Oo);T(wy.$$.fragment,tl),ver=i(tl),pd=n(tl,"P",{});var dee=s(pd);Fer=r(dee,`This is a generic model class that will be instantiated as one of the model classes of the library (with a audio frame (token) classification head) when created
with the `),_z=n(dee,"A",{href:!0});var E2t=s(_z);Ter=r(E2t,"from_pretrained()"),E2t.forEach(t),Mer=r(dee," class method or the "),uz=n(dee,"A",{href:!0});var C2t=s(uz);Eer=r(C2t,"from_config()"),C2t.forEach(t),Cer=r(dee,` class
method.`),dee.forEach(t),wer=i(tl),Ay=n(tl,"P",{});var yGe=s(Ay);Aer=r(yGe,"This class cannot be instantiated directly using "),_ue=n(yGe,"CODE",{});var w2t=s(_ue);yer=r(w2t,"__init__()"),w2t.forEach(t),Ler=r(yGe," (throws an error)."),yGe.forEach(t),xer=i(tl),bt=n(tl,"DIV",{class:!0});var hw=s(bt);T(yy.$$.fragment,hw),$er=i(hw),uue=n(hw,"P",{});var A2t=s(uue);ker=r(A2t,"Instantiates one of the model classes of the library (with a audio frame (token) classification head) from a configuration."),A2t.forEach(t),Ser=i(hw),_d=n(hw,"P",{});var cee=s(_d);Rer=r(cee,`Note:
Loading a model from its configuration file does `),bue=n(cee,"STRONG",{});var y2t=s(bue);Per=r(y2t,"not"),y2t.forEach(t),Ber=r(cee,` load the model weights. It only affects the
model\u2019s configuration. Use `),bz=n(cee,"A",{href:!0});var L2t=s(bz);Ier=r(L2t,"from_pretrained()"),L2t.forEach(t),Ner=r(cee," to load the model weights."),cee.forEach(t),qer=i(hw),T(z5.$$.fragment,hw),hw.forEach(t),jer=i(tl),fo=n(tl,"DIV",{class:!0});var pa=s(fo);T(Ly.$$.fragment,pa),Der=i(pa),vue=n(pa,"P",{});var x2t=s(vue);Ger=r(x2t,"Instantiate one of the model classes of the library (with a audio frame (token) classification head) from a pretrained model."),x2t.forEach(t),Oer=i(pa),Oa=n(pa,"P",{});var pw=s(Oa);Ver=r(pw,"The model class to instantiate is selected based on the "),Fue=n(pw,"CODE",{});var $2t=s(Fue);Xer=r($2t,"model_type"),$2t.forEach(t),zer=r(pw,` property of the config object (either
passed as an argument or loaded from `),Tue=n(pw,"CODE",{});var k2t=s(Tue);Wer=r(k2t,"pretrained_model_name_or_path"),k2t.forEach(t),Qer=r(pw,` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),Mue=n(pw,"CODE",{});var S2t=s(Mue);Her=r(S2t,"pretrained_model_name_or_path"),S2t.forEach(t),Uer=r(pw,":"),pw.forEach(t),Jer=i(pa),Kr=n(pa,"UL",{});var al=s(Kr);W5=n(al,"LI",{});var lSe=s(W5);Eue=n(lSe,"STRONG",{});var R2t=s(Eue);Yer=r(R2t,"data2vec-audio"),R2t.forEach(t),Ker=r(lSe," \u2014 "),vz=n(lSe,"A",{href:!0});var P2t=s(vz);Zer=r(P2t,"Data2VecAudioForAudioFrameClassification"),P2t.forEach(t),eor=r(lSe," (Data2VecAudio model)"),lSe.forEach(t),oor=i(al),Q5=n(al,"LI",{});var iSe=s(Q5);Cue=n(iSe,"STRONG",{});var B2t=s(Cue);ror=r(B2t,"unispeech-sat"),B2t.forEach(t),tor=r(iSe," \u2014 "),Fz=n(iSe,"A",{href:!0});var I2t=s(Fz);aor=r(I2t,"UniSpeechSatForAudioFrameClassification"),I2t.forEach(t),nor=r(iSe," (UniSpeechSat model)"),iSe.forEach(t),sor=i(al),H5=n(al,"LI",{});var dSe=s(H5);wue=n(dSe,"STRONG",{});var N2t=s(wue);lor=r(N2t,"wav2vec2"),N2t.forEach(t),ior=r(dSe," \u2014 "),Tz=n(dSe,"A",{href:!0});var q2t=s(Tz);dor=r(q2t,"Wav2Vec2ForAudioFrameClassification"),q2t.forEach(t),cor=r(dSe," (Wav2Vec2 model)"),dSe.forEach(t),mor=i(al),U5=n(al,"LI",{});var cSe=s(U5);Aue=n(cSe,"STRONG",{});var j2t=s(Aue);gor=r(j2t,"wav2vec2-conformer"),j2t.forEach(t),hor=r(cSe," \u2014 "),Mz=n(cSe,"A",{href:!0});var D2t=s(Mz);por=r(D2t,"Wav2Vec2ConformerForAudioFrameClassification"),D2t.forEach(t),_or=r(cSe," (Wav2Vec2-Conformer model)"),cSe.forEach(t),uor=i(al),J5=n(al,"LI",{});var fSe=s(J5);yue=n(fSe,"STRONG",{});var G2t=s(yue);bor=r(G2t,"wavlm"),G2t.forEach(t),vor=r(fSe," \u2014 "),Ez=n(fSe,"A",{href:!0});var O2t=s(Ez);For=r(O2t,"WavLMForAudioFrameClassification"),O2t.forEach(t),Tor=r(fSe," (WavLM model)"),fSe.forEach(t),al.forEach(t),Mor=i(pa),Y5=n(pa,"P",{});var mSe=s(Y5);Eor=r(mSe,"The model is set in evaluation mode by default using "),Lue=n(mSe,"CODE",{});var V2t=s(Lue);Cor=r(V2t,"model.eval()"),V2t.forEach(t),wor=r(mSe,` (so for instance, dropout modules are
deactivated). To train the model, you should first set it back in training mode with `),xue=n(mSe,"CODE",{});var X2t=s(xue);Aor=r(X2t,"model.train()"),X2t.forEach(t),mSe.forEach(t),yor=i(pa),T(K5.$$.fragment,pa),pa.forEach(t),tl.forEach(t),Cje=i(f),ud=n(f,"H2",{class:!0});var LGe=s(ud);Z5=n(LGe,"A",{id:!0,class:!0,href:!0});var z2t=s(Z5);$ue=n(z2t,"SPAN",{});var W2t=s($ue);T(xy.$$.fragment,W2t),W2t.forEach(t),z2t.forEach(t),Lor=i(LGe),kue=n(LGe,"SPAN",{});var Q2t=s(kue);xor=r(Q2t,"AutoModelForCTC"),Q2t.forEach(t),LGe.forEach(t),wje=i(f),Vo=n(f,"DIV",{class:!0});var nl=s(Vo);T($y.$$.fragment,nl),$or=i(nl),bd=n(nl,"P",{});var fee=s(bd);kor=r(fee,`This is a generic model class that will be instantiated as one of the model classes of the library (with a connectionist temporal classification head) when created
with the `),Cz=n(fee,"A",{href:!0});var H2t=s(Cz);Sor=r(H2t,"from_pretrained()"),H2t.forEach(t),Ror=r(fee," class method or the "),wz=n(fee,"A",{href:!0});var U2t=s(wz);Por=r(U2t,"from_config()"),U2t.forEach(t),Bor=r(fee,` class
method.`),fee.forEach(t),Ior=i(nl),ky=n(nl,"P",{});var xGe=s(ky);Nor=r(xGe,"This class cannot be instantiated directly using "),Sue=n(xGe,"CODE",{});var J2t=s(Sue);qor=r(J2t,"__init__()"),J2t.forEach(t),jor=r(xGe," (throws an error)."),xGe.forEach(t),Dor=i(nl),vt=n(nl,"DIV",{class:!0});var _w=s(vt);T(Sy.$$.fragment,_w),Gor=i(_w),Rue=n(_w,"P",{});var Y2t=s(Rue);Oor=r(Y2t,"Instantiates one of the model classes of the library (with a connectionist temporal classification head) from a configuration."),Y2t.forEach(t),Vor=i(_w),vd=n(_w,"P",{});var mee=s(vd);Xor=r(mee,`Note:
Loading a model from its configuration file does `),Pue=n(mee,"STRONG",{});var K2t=s(Pue);zor=r(K2t,"not"),K2t.forEach(t),Wor=r(mee,` load the model weights. It only affects the
model\u2019s configuration. Use `),Az=n(mee,"A",{href:!0});var Z2t=s(Az);Qor=r(Z2t,"from_pretrained()"),Z2t.forEach(t),Hor=r(mee," to load the model weights."),mee.forEach(t),Uor=i(_w),T(eF.$$.fragment,_w),_w.forEach(t),Jor=i(nl),mo=n(nl,"DIV",{class:!0});var _a=s(mo);T(Ry.$$.fragment,_a),Yor=i(_a),Bue=n(_a,"P",{});var e4t=s(Bue);Kor=r(e4t,"Instantiate one of the model classes of the library (with a connectionist temporal classification head) from a pretrained model."),e4t.forEach(t),Zor=i(_a),Va=n(_a,"P",{});var uw=s(Va);err=r(uw,"The model class to instantiate is selected based on the "),Iue=n(uw,"CODE",{});var o4t=s(Iue);orr=r(o4t,"model_type"),o4t.forEach(t),rrr=r(uw,` property of the config object (either
passed as an argument or loaded from `),Nue=n(uw,"CODE",{});var r4t=s(Nue);trr=r(r4t,"pretrained_model_name_or_path"),r4t.forEach(t),arr=r(uw,` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),que=n(uw,"CODE",{});var t4t=s(que);nrr=r(t4t,"pretrained_model_name_or_path"),t4t.forEach(t),srr=r(uw,":"),uw.forEach(t),lrr=i(_a),Re=n(_a,"UL",{});var Xe=s(Re);oF=n(Xe,"LI",{});var gSe=s(oF);jue=n(gSe,"STRONG",{});var a4t=s(jue);irr=r(a4t,"data2vec-audio"),a4t.forEach(t),drr=r(gSe," \u2014 "),yz=n(gSe,"A",{href:!0});var n4t=s(yz);crr=r(n4t,"Data2VecAudioForCTC"),n4t.forEach(t),frr=r(gSe," (Data2VecAudio model)"),gSe.forEach(t),mrr=i(Xe),rF=n(Xe,"LI",{});var hSe=s(rF);Due=n(hSe,"STRONG",{});var s4t=s(Due);grr=r(s4t,"hubert"),s4t.forEach(t),hrr=r(hSe," \u2014 "),Lz=n(hSe,"A",{href:!0});var l4t=s(Lz);prr=r(l4t,"HubertForCTC"),l4t.forEach(t),_rr=r(hSe," (Hubert model)"),hSe.forEach(t),urr=i(Xe),tF=n(Xe,"LI",{});var pSe=s(tF);Gue=n(pSe,"STRONG",{});var i4t=s(Gue);brr=r(i4t,"sew"),i4t.forEach(t),vrr=r(pSe," \u2014 "),xz=n(pSe,"A",{href:!0});var d4t=s(xz);Frr=r(d4t,"SEWForCTC"),d4t.forEach(t),Trr=r(pSe," (SEW model)"),pSe.forEach(t),Mrr=i(Xe),aF=n(Xe,"LI",{});var _Se=s(aF);Oue=n(_Se,"STRONG",{});var c4t=s(Oue);Err=r(c4t,"sew-d"),c4t.forEach(t),Crr=r(_Se," \u2014 "),$z=n(_Se,"A",{href:!0});var f4t=s($z);wrr=r(f4t,"SEWDForCTC"),f4t.forEach(t),Arr=r(_Se," (SEW-D model)"),_Se.forEach(t),yrr=i(Xe),nF=n(Xe,"LI",{});var uSe=s(nF);Vue=n(uSe,"STRONG",{});var m4t=s(Vue);Lrr=r(m4t,"unispeech"),m4t.forEach(t),xrr=r(uSe," \u2014 "),kz=n(uSe,"A",{href:!0});var g4t=s(kz);$rr=r(g4t,"UniSpeechForCTC"),g4t.forEach(t),krr=r(uSe," (UniSpeech model)"),uSe.forEach(t),Srr=i(Xe),sF=n(Xe,"LI",{});var bSe=s(sF);Xue=n(bSe,"STRONG",{});var h4t=s(Xue);Rrr=r(h4t,"unispeech-sat"),h4t.forEach(t),Prr=r(bSe," \u2014 "),Sz=n(bSe,"A",{href:!0});var p4t=s(Sz);Brr=r(p4t,"UniSpeechSatForCTC"),p4t.forEach(t),Irr=r(bSe," (UniSpeechSat model)"),bSe.forEach(t),Nrr=i(Xe),lF=n(Xe,"LI",{});var vSe=s(lF);zue=n(vSe,"STRONG",{});var _4t=s(zue);qrr=r(_4t,"wav2vec2"),_4t.forEach(t),jrr=r(vSe," \u2014 "),Rz=n(vSe,"A",{href:!0});var u4t=s(Rz);Drr=r(u4t,"Wav2Vec2ForCTC"),u4t.forEach(t),Grr=r(vSe," (Wav2Vec2 model)"),vSe.forEach(t),Orr=i(Xe),iF=n(Xe,"LI",{});var FSe=s(iF);Wue=n(FSe,"STRONG",{});var b4t=s(Wue);Vrr=r(b4t,"wav2vec2-conformer"),b4t.forEach(t),Xrr=r(FSe," \u2014 "),Pz=n(FSe,"A",{href:!0});var v4t=s(Pz);zrr=r(v4t,"Wav2Vec2ConformerForCTC"),v4t.forEach(t),Wrr=r(FSe," (Wav2Vec2-Conformer model)"),FSe.forEach(t),Qrr=i(Xe),dF=n(Xe,"LI",{});var TSe=s(dF);Que=n(TSe,"STRONG",{});var F4t=s(Que);Hrr=r(F4t,"wavlm"),F4t.forEach(t),Urr=r(TSe," \u2014 "),Bz=n(TSe,"A",{href:!0});var T4t=s(Bz);Jrr=r(T4t,"WavLMForCTC"),T4t.forEach(t),Yrr=r(TSe," (WavLM model)"),TSe.forEach(t),Xe.forEach(t),Krr=i(_a),cF=n(_a,"P",{});var MSe=s(cF);Zrr=r(MSe,"The model is set in evaluation mode by default using "),Hue=n(MSe,"CODE",{});var M4t=s(Hue);etr=r(M4t,"model.eval()"),M4t.forEach(t),otr=r(MSe,` (so for instance, dropout modules are
deactivated). To train the model, you should first set it back in training mode with `),Uue=n(MSe,"CODE",{});var E4t=s(Uue);rtr=r(E4t,"model.train()"),E4t.forEach(t),MSe.forEach(t),ttr=i(_a),T(fF.$$.fragment,_a),_a.forEach(t),nl.forEach(t),Aje=i(f),Fd=n(f,"H2",{class:!0});var $Ge=s(Fd);mF=n($Ge,"A",{id:!0,class:!0,href:!0});var C4t=s(mF);Jue=n(C4t,"SPAN",{});var w4t=s(Jue);T(Py.$$.fragment,w4t),w4t.forEach(t),C4t.forEach(t),atr=i($Ge),Yue=n($Ge,"SPAN",{});var A4t=s(Yue);ntr=r(A4t,"AutoModelForSpeechSeq2Seq"),A4t.forEach(t),$Ge.forEach(t),yje=i(f),Xo=n(f,"DIV",{class:!0});var sl=s(Xo);T(By.$$.fragment,sl),str=i(sl),Td=n(sl,"P",{});var gee=s(Td);ltr=r(gee,`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 `),Iz=n(gee,"A",{href:!0});var y4t=s(Iz);itr=r(y4t,"from_pretrained()"),y4t.forEach(t),dtr=r(gee," class method or the "),Nz=n(gee,"A",{href:!0});var L4t=s(Nz);ctr=r(L4t,"from_config()"),L4t.forEach(t),ftr=r(gee,` class
method.`),gee.forEach(t),mtr=i(sl),Iy=n(sl,"P",{});var kGe=s(Iy);gtr=r(kGe,"This class cannot be instantiated directly using "),Kue=n(kGe,"CODE",{});var x4t=s(Kue);htr=r(x4t,"__init__()"),x4t.forEach(t),ptr=r(kGe," (throws an error)."),kGe.forEach(t),_tr=i(sl),Ft=n(sl,"DIV",{class:!0});var bw=s(Ft);T(Ny.$$.fragment,bw),utr=i(bw),Zue=n(bw,"P",{});var $4t=s(Zue);btr=r($4t,"Instantiates one of the model classes of the library (with a sequence-to-sequence speech-to-text modeling head) from a configuration."),$4t.forEach(t),vtr=i(bw),Md=n(bw,"P",{});var hee=s(Md);Ftr=r(hee,`Note:
Loading a model from its configuration file does `),e1e=n(hee,"STRONG",{});var k4t=s(e1e);Ttr=r(k4t,"not"),k4t.forEach(t),Mtr=r(hee,` load the model weights. It only affects the
model\u2019s configuration. Use `),qz=n(hee,"A",{href:!0});var S4t=s(qz);Etr=r(S4t,"from_pretrained()"),S4t.forEach(t),Ctr=r(hee," to load the model weights."),hee.forEach(t),wtr=i(bw),T(gF.$$.fragment,bw),bw.forEach(t),Atr=i(sl),go=n(sl,"DIV",{class:!0});var ua=s(go);T(qy.$$.fragment,ua),ytr=i(ua),o1e=n(ua,"P",{});var R4t=s(o1e);Ltr=r(R4t,"Instantiate one of the model classes of the library (with a sequence-to-sequence speech-to-text modeling head) from a pretrained model."),R4t.forEach(t),xtr=i(ua),Xa=n(ua,"P",{});var vw=s(Xa);$tr=r(vw,"The model class to instantiate is selected based on the "),r1e=n(vw,"CODE",{});var P4t=s(r1e);ktr=r(P4t,"model_type"),P4t.forEach(t),Str=r(vw,` property of the config object (either
passed as an argument or loaded from `),t1e=n(vw,"CODE",{});var B4t=s(t1e);Rtr=r(B4t,"pretrained_model_name_or_path"),B4t.forEach(t),Ptr=r(vw,` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),a1e=n(vw,"CODE",{});var I4t=s(a1e);Btr=r(I4t,"pretrained_model_name_or_path"),I4t.forEach(t),Itr=r(vw,":"),vw.forEach(t),Ntr=i(ua),jy=n(ua,"UL",{});var SGe=s(jy);hF=n(SGe,"LI",{});var ESe=s(hF);n1e=n(ESe,"STRONG",{});var N4t=s(n1e);qtr=r(N4t,"speech-encoder-decoder"),N4t.forEach(t),jtr=r(ESe," \u2014 "),jz=n(ESe,"A",{href:!0});var q4t=s(jz);Dtr=r(q4t,"SpeechEncoderDecoderModel"),q4t.forEach(t),Gtr=r(ESe," (Speech Encoder decoder model)"),ESe.forEach(t),Otr=i(SGe),pF=n(SGe,"LI",{});var CSe=s(pF);s1e=n(CSe,"STRONG",{});var j4t=s(s1e);Vtr=r(j4t,"speech_to_text"),j4t.forEach(t),Xtr=r(CSe," \u2014 "),Dz=n(CSe,"A",{href:!0});var D4t=s(Dz);ztr=r(D4t,"Speech2TextForConditionalGeneration"),D4t.forEach(t),Wtr=r(CSe," (Speech2Text model)"),CSe.forEach(t),SGe.forEach(t),Qtr=i(ua),_F=n(ua,"P",{});var wSe=s(_F);Htr=r(wSe,"The model is set in evaluation mode by default using "),l1e=n(wSe,"CODE",{});var G4t=s(l1e);Utr=r(G4t,"model.eval()"),G4t.forEach(t),Jtr=r(wSe,` (so for instance, dropout modules are
deactivated). To train the model, you should first set it back in training mode with `),i1e=n(wSe,"CODE",{});var O4t=s(i1e);Ytr=r(O4t,"model.train()"),O4t.forEach(t),wSe.forEach(t),Ktr=i(ua),T(uF.$$.fragment,ua),ua.forEach(t),sl.forEach(t),Lje=i(f),Ed=n(f,"H2",{class:!0});var RGe=s(Ed);bF=n(RGe,"A",{id:!0,class:!0,href:!0});var V4t=s(bF);d1e=n(V4t,"SPAN",{});var X4t=s(d1e);T(Dy.$$.fragment,X4t),X4t.forEach(t),V4t.forEach(t),Ztr=i(RGe),c1e=n(RGe,"SPAN",{});var z4t=s(c1e);ear=r(z4t,"AutoModelForAudioXVector"),z4t.forEach(t),RGe.forEach(t),xje=i(f),zo=n(f,"DIV",{class:!0});var ll=s(zo);T(Gy.$$.fragment,ll),oar=i(ll),Cd=n(ll,"P",{});var pee=s(Cd);rar=r(pee,`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 `),Gz=n(pee,"A",{href:!0});var W4t=s(Gz);tar=r(W4t,"from_pretrained()"),W4t.forEach(t),aar=r(pee," class method or the "),Oz=n(pee,"A",{href:!0});var Q4t=s(Oz);nar=r(Q4t,"from_config()"),Q4t.forEach(t),sar=r(pee,` class
method.`),pee.forEach(t),lar=i(ll),Oy=n(ll,"P",{});var PGe=s(Oy);iar=r(PGe,"This class cannot be instantiated directly using "),f1e=n(PGe,"CODE",{});var H4t=s(f1e);dar=r(H4t,"__init__()"),H4t.forEach(t),car=r(PGe," (throws an error)."),PGe.forEach(t),far=i(ll),Tt=n(ll,"DIV",{class:!0});var Fw=s(Tt);T(Vy.$$.fragment,Fw),mar=i(Fw),m1e=n(Fw,"P",{});var U4t=s(m1e);gar=r(U4t,"Instantiates one of the model classes of the library (with a audio retrieval via x-vector head) from a configuration."),U4t.forEach(t),har=i(Fw),wd=n(Fw,"P",{});var _ee=s(wd);par=r(_ee,`Note:
Loading a model from its configuration file does `),g1e=n(_ee,"STRONG",{});var J4t=s(g1e);_ar=r(J4t,"not"),J4t.forEach(t),uar=r(_ee,` load the model weights. It only affects the
model\u2019s configuration. Use `),Vz=n(_ee,"A",{href:!0});var Y4t=s(Vz);bar=r(Y4t,"from_pretrained()"),Y4t.forEach(t),Far=r(_ee," to load the model weights."),_ee.forEach(t),Tar=i(Fw),T(vF.$$.fragment,Fw),Fw.forEach(t),Mar=i(ll),ho=n(ll,"DIV",{class:!0});var ba=s(ho);T(Xy.$$.fragment,ba),Ear=i(ba),h1e=n(ba,"P",{});var K4t=s(h1e);Car=r(K4t,"Instantiate one of the model classes of the library (with a audio retrieval via x-vector head) from a pretrained model."),K4t.forEach(t),war=i(ba),za=n(ba,"P",{});var Tw=s(za);Aar=r(Tw,"The model class to instantiate is selected based on the "),p1e=n(Tw,"CODE",{});var Z4t=s(p1e);yar=r(Z4t,"model_type"),Z4t.forEach(t),Lar=r(Tw,` property of the config object (either
passed as an argument or loaded from `),_1e=n(Tw,"CODE",{});var evt=s(_1e);xar=r(evt,"pretrained_model_name_or_path"),evt.forEach(t),$ar=r(Tw,` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),u1e=n(Tw,"CODE",{});var ovt=s(u1e);kar=r(ovt,"pretrained_model_name_or_path"),ovt.forEach(t),Sar=r(Tw,":"),Tw.forEach(t),Rar=i(ba),Zr=n(ba,"UL",{});var il=s(Zr);FF=n(il,"LI",{});var ASe=s(FF);b1e=n(ASe,"STRONG",{});var rvt=s(b1e);Par=r(rvt,"data2vec-audio"),rvt.forEach(t),Bar=r(ASe," \u2014 "),Xz=n(ASe,"A",{href:!0});var tvt=s(Xz);Iar=r(tvt,"Data2VecAudioForXVector"),tvt.forEach(t),Nar=r(ASe," (Data2VecAudio model)"),ASe.forEach(t),qar=i(il),TF=n(il,"LI",{});var ySe=s(TF);v1e=n(ySe,"STRONG",{});var avt=s(v1e);jar=r(avt,"unispeech-sat"),avt.forEach(t),Dar=r(ySe," \u2014 "),zz=n(ySe,"A",{href:!0});var nvt=s(zz);Gar=r(nvt,"UniSpeechSatForXVector"),nvt.forEach(t),Oar=r(ySe," (UniSpeechSat model)"),ySe.forEach(t),Var=i(il),MF=n(il,"LI",{});var LSe=s(MF);F1e=n(LSe,"STRONG",{});var svt=s(F1e);Xar=r(svt,"wav2vec2"),svt.forEach(t),zar=r(LSe," \u2014 "),Wz=n(LSe,"A",{href:!0});var lvt=s(Wz);War=r(lvt,"Wav2Vec2ForXVector"),lvt.forEach(t),Qar=r(LSe," (Wav2Vec2 model)"),LSe.forEach(t),Har=i(il),EF=n(il,"LI",{});var xSe=s(EF);T1e=n(xSe,"STRONG",{});var ivt=s(T1e);Uar=r(ivt,"wav2vec2-conformer"),ivt.forEach(t),Jar=r(xSe," \u2014 "),Qz=n(xSe,"A",{href:!0});var dvt=s(Qz);Yar=r(dvt,"Wav2Vec2ConformerForXVector"),dvt.forEach(t),Kar=r(xSe," (Wav2Vec2-Conformer model)"),xSe.forEach(t),Zar=i(il),CF=n(il,"LI",{});var $Se=s(CF);M1e=n($Se,"STRONG",{});var cvt=s(M1e);enr=r(cvt,"wavlm"),cvt.forEach(t),onr=r($Se," \u2014 "),Hz=n($Se,"A",{href:!0});var fvt=s(Hz);rnr=r(fvt,"WavLMForXVector"),fvt.forEach(t),tnr=r($Se," (WavLM model)"),$Se.forEach(t),il.forEach(t),anr=i(ba),wF=n(ba,"P",{});var kSe=s(wF);nnr=r(kSe,"The model is set in evaluation mode by default using "),E1e=n(kSe,"CODE",{});var mvt=s(E1e);snr=r(mvt,"model.eval()"),mvt.forEach(t),lnr=r(kSe,` (so for instance, dropout modules are
deactivated). To train the model, you should first set it back in training mode with `),C1e=n(kSe,"CODE",{});var gvt=s(C1e);inr=r(gvt,"model.train()"),gvt.forEach(t),kSe.forEach(t),dnr=i(ba),T(AF.$$.fragment,ba),ba.forEach(t),ll.forEach(t),$je=i(f),Ad=n(f,"H2",{class:!0});var BGe=s(Ad);yF=n(BGe,"A",{id:!0,class:!0,href:!0});var hvt=s(yF);w1e=n(hvt,"SPAN",{});var pvt=s(w1e);T(zy.$$.fragment,pvt),pvt.forEach(t),hvt.forEach(t),cnr=i(BGe),A1e=n(BGe,"SPAN",{});var _vt=s(A1e);fnr=r(_vt,"AutoModelForMaskedImageModeling"),_vt.forEach(t),BGe.forEach(t),kje=i(f),Wo=n(f,"DIV",{class:!0});var dl=s(Wo);T(Wy.$$.fragment,dl),mnr=i(dl),yd=n(dl,"P",{});var uee=s(yd);gnr=r(uee,`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 `),Uz=n(uee,"A",{href:!0});var uvt=s(Uz);hnr=r(uvt,"from_pretrained()"),uvt.forEach(t),pnr=r(uee," class method or the "),Jz=n(uee,"A",{href:!0});var bvt=s(Jz);_nr=r(bvt,"from_config()"),bvt.forEach(t),unr=r(uee,` class
method.`),uee.forEach(t),bnr=i(dl),Qy=n(dl,"P",{});var IGe=s(Qy);vnr=r(IGe,"This class cannot be instantiated directly using "),y1e=n(IGe,"CODE",{});var vvt=s(y1e);Fnr=r(vvt,"__init__()"),vvt.forEach(t),Tnr=r(IGe," (throws an error)."),IGe.forEach(t),Mnr=i(dl),Mt=n(dl,"DIV",{class:!0});var Mw=s(Mt);T(Hy.$$.fragment,Mw),Enr=i(Mw),L1e=n(Mw,"P",{});var Fvt=s(L1e);Cnr=r(Fvt,"Instantiates one of the model classes of the library (with a masked image modeling head) from a configuration."),Fvt.forEach(t),wnr=i(Mw),Ld=n(Mw,"P",{});var bee=s(Ld);Anr=r(bee,`Note:
Loading a model from its configuration file does `),x1e=n(bee,"STRONG",{});var Tvt=s(x1e);ynr=r(Tvt,"not"),Tvt.forEach(t),Lnr=r(bee,` load the model weights. It only affects the
model\u2019s configuration. Use `),Yz=n(bee,"A",{href:!0});var Mvt=s(Yz);xnr=r(Mvt,"from_pretrained()"),Mvt.forEach(t),$nr=r(bee," to load the model weights."),bee.forEach(t),knr=i(Mw),T(LF.$$.fragment,Mw),Mw.forEach(t),Snr=i(dl),po=n(dl,"DIV",{class:!0});var va=s(po);T(Uy.$$.fragment,va),Rnr=i(va),$1e=n(va,"P",{});var Evt=s($1e);Pnr=r(Evt,"Instantiate one of the model classes of the library (with a masked image modeling head) from a pretrained model."),Evt.forEach(t),Bnr=i(va),Wa=n(va,"P",{});var Ew=s(Wa);Inr=r(Ew,"The model class to instantiate is selected based on the "),k1e=n(Ew,"CODE",{});var Cvt=s(k1e);Nnr=r(Cvt,"model_type"),Cvt.forEach(t),qnr=r(Ew,` property of the config object (either
passed as an argument or loaded from `),S1e=n(Ew,"CODE",{});var wvt=s(S1e);jnr=r(wvt,"pretrained_model_name_or_path"),wvt.forEach(t),Dnr=r(Ew,` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),R1e=n(Ew,"CODE",{});var Avt=s(R1e);Gnr=r(Avt,"pretrained_model_name_or_path"),Avt.forEach(t),Onr=r(Ew,":"),Ew.forEach(t),Vnr=i(va),xd=n(va,"UL",{});var vee=s(xd);xF=n(vee,"LI",{});var SSe=s(xF);P1e=n(SSe,"STRONG",{});var yvt=s(P1e);Xnr=r(yvt,"deit"),yvt.forEach(t),znr=r(SSe," \u2014 "),Kz=n(SSe,"A",{href:!0});var Lvt=s(Kz);Wnr=r(Lvt,"DeiTForMaskedImageModeling"),Lvt.forEach(t),Qnr=r(SSe," (DeiT model)"),SSe.forEach(t),Hnr=i(vee),$F=n(vee,"LI",{});var RSe=s($F);B1e=n(RSe,"STRONG",{});var xvt=s(B1e);Unr=r(xvt,"swin"),xvt.forEach(t),Jnr=r(RSe," \u2014 "),Zz=n(RSe,"A",{href:!0});var $vt=s(Zz);Ynr=r($vt,"SwinForMaskedImageModeling"),$vt.forEach(t),Knr=r(RSe," (Swin Transformer model)"),RSe.forEach(t),Znr=i(vee),kF=n(vee,"LI",{});var PSe=s(kF);I1e=n(PSe,"STRONG",{});var kvt=s(I1e);esr=r(kvt,"vit"),kvt.forEach(t),osr=r(PSe," \u2014 "),eW=n(PSe,"A",{href:!0});var Svt=s(eW);rsr=r(Svt,"ViTForMaskedImageModeling"),Svt.forEach(t),tsr=r(PSe," (ViT model)"),PSe.forEach(t),vee.forEach(t),asr=i(va),SF=n(va,"P",{});var BSe=s(SF);nsr=r(BSe,"The model is set in evaluation mode by default using "),N1e=n(BSe,"CODE",{});var Rvt=s(N1e);ssr=r(Rvt,"model.eval()"),Rvt.forEach(t),lsr=r(BSe,` (so for instance, dropout modules are
deactivated). To train the model, you should first set it back in training mode with `),q1e=n(BSe,"CODE",{});var Pvt=s(q1e);isr=r(Pvt,"model.train()"),Pvt.forEach(t),BSe.forEach(t),dsr=i(va),T(RF.$$.fragment,va),va.forEach(t),dl.forEach(t),Sje=i(f),$d=n(f,"H2",{class:!0});var NGe=s($d);PF=n(NGe,"A",{id:!0,class:!0,href:!0});var Bvt=s(PF);j1e=n(Bvt,"SPAN",{});var Ivt=s(j1e);T(Jy.$$.fragment,Ivt),Ivt.forEach(t),Bvt.forEach(t),csr=i(NGe),D1e=n(NGe,"SPAN",{});var Nvt=s(D1e);fsr=r(Nvt,"AutoModelForObjectDetection"),Nvt.forEach(t),NGe.forEach(t),Rje=i(f),Qo=n(f,"DIV",{class:!0});var cl=s(Qo);T(Yy.$$.fragment,cl),msr=i(cl),kd=n(cl,"P",{});var Fee=s(kd);gsr=r(Fee,`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 `),oW=n(Fee,"A",{href:!0});var qvt=s(oW);hsr=r(qvt,"from_pretrained()"),qvt.forEach(t),psr=r(Fee," class method or the "),rW=n(Fee,"A",{href:!0});var jvt=s(rW);_sr=r(jvt,"from_config()"),jvt.forEach(t),usr=r(Fee,` class
method.`),Fee.forEach(t),bsr=i(cl),Ky=n(cl,"P",{});var qGe=s(Ky);vsr=r(qGe,"This class cannot be instantiated directly using "),G1e=n(qGe,"CODE",{});var Dvt=s(G1e);Fsr=r(Dvt,"__init__()"),Dvt.forEach(t),Tsr=r(qGe," (throws an error)."),qGe.forEach(t),Msr=i(cl),Et=n(cl,"DIV",{class:!0});var Cw=s(Et);T(Zy.$$.fragment,Cw),Esr=i(Cw),O1e=n(Cw,"P",{});var Gvt=s(O1e);Csr=r(Gvt,"Instantiates one of the model classes of the library (with a object detection head) from a configuration."),Gvt.forEach(t),wsr=i(Cw),Sd=n(Cw,"P",{});var Tee=s(Sd);Asr=r(Tee,`Note:
Loading a model from its configuration file does `),V1e=n(Tee,"STRONG",{});var Ovt=s(V1e);ysr=r(Ovt,"not"),Ovt.forEach(t),Lsr=r(Tee,` load the model weights. It only affects the
model\u2019s configuration. Use `),tW=n(Tee,"A",{href:!0});var Vvt=s(tW);xsr=r(Vvt,"from_pretrained()"),Vvt.forEach(t),$sr=r(Tee," to load the model weights."),Tee.forEach(t),ksr=i(Cw),T(BF.$$.fragment,Cw),Cw.forEach(t),Ssr=i(cl),_o=n(cl,"DIV",{class:!0});var Fa=s(_o);T(eL.$$.fragment,Fa),Rsr=i(Fa),X1e=n(Fa,"P",{});var Xvt=s(X1e);Psr=r(Xvt,"Instantiate one of the model classes of the library (with a object detection head) from a pretrained model."),Xvt.forEach(t),Bsr=i(Fa),Qa=n(Fa,"P",{});var ww=s(Qa);Isr=r(ww,"The model class to instantiate is selected based on the "),z1e=n(ww,"CODE",{});var zvt=s(z1e);Nsr=r(zvt,"model_type"),zvt.forEach(t),qsr=r(ww,` property of the config object (either
passed as an argument or loaded from `),W1e=n(ww,"CODE",{});var Wvt=s(W1e);jsr=r(Wvt,"pretrained_model_name_or_path"),Wvt.forEach(t),Dsr=r(ww,` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),Q1e=n(ww,"CODE",{});var Qvt=s(Q1e);Gsr=r(Qvt,"pretrained_model_name_or_path"),Qvt.forEach(t),Osr=r(ww,":"),ww.forEach(t),Vsr=i(Fa),oL=n(Fa,"UL",{});var jGe=s(oL);IF=n(jGe,"LI",{});var ISe=s(IF);H1e=n(ISe,"STRONG",{});var Hvt=s(H1e);Xsr=r(Hvt,"detr"),Hvt.forEach(t),zsr=r(ISe," \u2014 "),aW=n(ISe,"A",{href:!0});var Uvt=s(aW);Wsr=r(Uvt,"DetrForObjectDetection"),Uvt.forEach(t),Qsr=r(ISe," (DETR model)"),ISe.forEach(t),Hsr=i(jGe),NF=n(jGe,"LI",{});var NSe=s(NF);U1e=n(NSe,"STRONG",{});var Jvt=s(U1e);Usr=r(Jvt,"yolos"),Jvt.forEach(t),Jsr=r(NSe," \u2014 "),nW=n(NSe,"A",{href:!0});var Yvt=s(nW);Ysr=r(Yvt,"YolosForObjectDetection"),Yvt.forEach(t),Ksr=r(NSe," (YOLOS model)"),NSe.forEach(t),jGe.forEach(t),Zsr=i(Fa),qF=n(Fa,"P",{});var qSe=s(qF);elr=r(qSe,"The model is set in evaluation mode by default using "),J1e=n(qSe,"CODE",{});var Kvt=s(J1e);olr=r(Kvt,"model.eval()"),Kvt.forEach(t),rlr=r(qSe,` (so for instance, dropout modules are
deactivated). To train the model, you should first set it back in training mode with `),Y1e=n(qSe,"CODE",{});var Zvt=s(Y1e);tlr=r(Zvt,"model.train()"),Zvt.forEach(t),qSe.forEach(t),alr=i(Fa),T(jF.$$.fragment,Fa),Fa.forEach(t),cl.forEach(t),Pje=i(f),Rd=n(f,"H2",{class:!0});var DGe=s(Rd);DF=n(DGe,"A",{id:!0,class:!0,href:!0});var e5t=s(DF);K1e=n(e5t,"SPAN",{});var o5t=s(K1e);T(rL.$$.fragment,o5t),o5t.forEach(t),e5t.forEach(t),nlr=i(DGe),Z1e=n(DGe,"SPAN",{});var r5t=s(Z1e);slr=r(r5t,"AutoModelForImageSegmentation"),r5t.forEach(t),DGe.forEach(t),Bje=i(f),Ho=n(f,"DIV",{class:!0});var fl=s(Ho);T(tL.$$.fragment,fl),llr=i(fl),Pd=n(fl,"P",{});var Mee=s(Pd);ilr=r(Mee,`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 `),sW=n(Mee,"A",{href:!0});var t5t=s(sW);dlr=r(t5t,"from_pretrained()"),t5t.forEach(t),clr=r(Mee," class method or the "),lW=n(Mee,"A",{href:!0});var a5t=s(lW);flr=r(a5t,"from_config()"),a5t.forEach(t),mlr=r(Mee,` class
method.`),Mee.forEach(t),glr=i(fl),aL=n(fl,"P",{});var GGe=s(aL);hlr=r(GGe,"This class cannot be instantiated directly using "),ebe=n(GGe,"CODE",{});var n5t=s(ebe);plr=r(n5t,"__init__()"),n5t.forEach(t),_lr=r(GGe," (throws an error)."),GGe.forEach(t),ulr=i(fl),Ct=n(fl,"DIV",{class:!0});var Aw=s(Ct);T(nL.$$.fragment,Aw),blr=i(Aw),obe=n(Aw,"P",{});var s5t=s(obe);vlr=r(s5t,"Instantiates one of the model classes of the library (with a image segmentation head) from a configuration."),s5t.forEach(t),Flr=i(Aw),Bd=n(Aw,"P",{});var Eee=s(Bd);Tlr=r(Eee,`Note:
Loading a model from its configuration file does `),rbe=n(Eee,"STRONG",{});var l5t=s(rbe);Mlr=r(l5t,"not"),l5t.forEach(t),Elr=r(Eee,` load the model weights. It only affects the
model\u2019s configuration. Use `),iW=n(Eee,"A",{href:!0});var i5t=s(iW);Clr=r(i5t,"from_pretrained()"),i5t.forEach(t),wlr=r(Eee," to load the model weights."),Eee.forEach(t),Alr=i(Aw),T(GF.$$.fragment,Aw),Aw.forEach(t),ylr=i(fl),uo=n(fl,"DIV",{class:!0});var Ta=s(uo);T(sL.$$.fragment,Ta),Llr=i(Ta),tbe=n(Ta,"P",{});var d5t=s(tbe);xlr=r(d5t,"Instantiate one of the model classes of the library (with a image segmentation head) from a pretrained model."),d5t.forEach(t),$lr=i(Ta),Ha=n(Ta,"P",{});var yw=s(Ha);klr=r(yw,"The model class to instantiate is selected based on the "),abe=n(yw,"CODE",{});var c5t=s(abe);Slr=r(c5t,"model_type"),c5t.forEach(t),Rlr=r(yw,` property of the config object (either
passed as an argument or loaded from `),nbe=n(yw,"CODE",{});var f5t=s(nbe);Plr=r(f5t,"pretrained_model_name_or_path"),f5t.forEach(t),Blr=r(yw,` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),sbe=n(yw,"CODE",{});var m5t=s(sbe);Ilr=r(m5t,"pretrained_model_name_or_path"),m5t.forEach(t),Nlr=r(yw,":"),yw.forEach(t),qlr=i(Ta),lbe=n(Ta,"UL",{});var g5t=s(lbe);OF=n(g5t,"LI",{});var jSe=s(OF);ibe=n(jSe,"STRONG",{});var h5t=s(ibe);jlr=r(h5t,"detr"),h5t.forEach(t),Dlr=r(jSe," \u2014 "),dW=n(jSe,"A",{href:!0});var p5t=s(dW);Glr=r(p5t,"DetrForSegmentation"),p5t.forEach(t),Olr=r(jSe," (DETR model)"),jSe.forEach(t),g5t.forEach(t),Vlr=i(Ta),VF=n(Ta,"P",{});var DSe=s(VF);Xlr=r(DSe,"The model is set in evaluation mode by default using "),dbe=n(DSe,"CODE",{});var _5t=s(dbe);zlr=r(_5t,"model.eval()"),_5t.forEach(t),Wlr=r(DSe,` (so for instance, dropout modules are
deactivated). To train the model, you should first set it back in training mode with `),cbe=n(DSe,"CODE",{});var u5t=s(cbe);Qlr=r(u5t,"model.train()"),u5t.forEach(t),DSe.forEach(t),Hlr=i(Ta),T(XF.$$.fragment,Ta),Ta.forEach(t),fl.forEach(t),Ije=i(f),Id=n(f,"H2",{class:!0});var OGe=s(Id);zF=n(OGe,"A",{id:!0,class:!0,href:!0});var b5t=s(zF);fbe=n(b5t,"SPAN",{});var v5t=s(fbe);T(lL.$$.fragment,v5t),v5t.forEach(t),b5t.forEach(t),Ulr=i(OGe),mbe=n(OGe,"SPAN",{});var F5t=s(mbe);Jlr=r(F5t,"AutoModelForSemanticSegmentation"),F5t.forEach(t),OGe.forEach(t),Nje=i(f),Uo=n(f,"DIV",{class:!0});var ml=s(Uo);T(iL.$$.fragment,ml),Ylr=i(ml),Nd=n(ml,"P",{});var Cee=s(Nd);Klr=r(Cee,`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 `),cW=n(Cee,"A",{href:!0});var T5t=s(cW);Zlr=r(T5t,"from_pretrained()"),T5t.forEach(t),eir=r(Cee," class method or the "),fW=n(Cee,"A",{href:!0});var M5t=s(fW);oir=r(M5t,"from_config()"),M5t.forEach(t),rir=r(Cee,` class
method.`),Cee.forEach(t),tir=i(ml),dL=n(ml,"P",{});var VGe=s(dL);air=r(VGe,"This class cannot be instantiated directly using "),gbe=n(VGe,"CODE",{});var E5t=s(gbe);nir=r(E5t,"__init__()"),E5t.forEach(t),sir=r(VGe," (throws an error)."),VGe.forEach(t),lir=i(ml),wt=n(ml,"DIV",{class:!0});var Lw=s(wt);T(cL.$$.fragment,Lw),iir=i(Lw),hbe=n(Lw,"P",{});var C5t=s(hbe);dir=r(C5t,"Instantiates one of the model classes of the library (with a semantic segmentation head) from a configuration."),C5t.forEach(t),cir=i(Lw),qd=n(Lw,"P",{});var wee=s(qd);fir=r(wee,`Note:
Loading a model from its configuration file does `),pbe=n(wee,"STRONG",{});var w5t=s(pbe);mir=r(w5t,"not"),w5t.forEach(t),gir=r(wee,` load the model weights. It only affects the
model\u2019s configuration. Use `),mW=n(wee,"A",{href:!0});var A5t=s(mW);hir=r(A5t,"from_pretrained()"),A5t.forEach(t),pir=r(wee," to load the model weights."),wee.forEach(t),_ir=i(Lw),T(WF.$$.fragment,Lw),Lw.forEach(t),uir=i(ml),bo=n(ml,"DIV",{class:!0});var Ma=s(bo);T(fL.$$.fragment,Ma),bir=i(Ma),_be=n(Ma,"P",{});var y5t=s(_be);vir=r(y5t,"Instantiate one of the model classes of the library (with a semantic segmentation head) from a pretrained model."),y5t.forEach(t),Fir=i(Ma),Ua=n(Ma,"P",{});var xw=s(Ua);Tir=r(xw,"The model class to instantiate is selected based on the "),ube=n(xw,"CODE",{});var L5t=s(ube);Mir=r(L5t,"model_type"),L5t.forEach(t),Eir=r(xw,` property of the config object (either
passed as an argument or loaded from `),bbe=n(xw,"CODE",{});var x5t=s(bbe);Cir=r(x5t,"pretrained_model_name_or_path"),x5t.forEach(t),wir=r(xw,` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),vbe=n(xw,"CODE",{});var $5t=s(vbe);Air=r($5t,"pretrained_model_name_or_path"),$5t.forEach(t),yir=r(xw,":"),xw.forEach(t),Lir=i(Ma),Ja=n(Ma,"UL",{});var $w=s(Ja);QF=n($w,"LI",{});var GSe=s(QF);Fbe=n(GSe,"STRONG",{});var k5t=s(Fbe);xir=r(k5t,"beit"),k5t.forEach(t),$ir=r(GSe," \u2014 "),gW=n(GSe,"A",{href:!0});var S5t=s(gW);kir=r(S5t,"BeitForSemanticSegmentation"),S5t.forEach(t),Sir=r(GSe," (BEiT model)"),GSe.forEach(t),Rir=i($w),HF=n($w,"LI",{});var OSe=s(HF);Tbe=n(OSe,"STRONG",{});var R5t=s(Tbe);Pir=r(R5t,"data2vec-vision"),R5t.forEach(t),Bir=r(OSe," \u2014 "),hW=n(OSe,"A",{href:!0});var P5t=s(hW);Iir=r(P5t,"Data2VecVisionForSemanticSegmentation"),P5t.forEach(t),Nir=r(OSe," (Data2VecVision model)"),OSe.forEach(t),qir=i($w),UF=n($w,"LI",{});var VSe=s(UF);Mbe=n(VSe,"STRONG",{});var B5t=s(Mbe);jir=r(B5t,"dpt"),B5t.forEach(t),Dir=r(VSe," \u2014 "),pW=n(VSe,"A",{href:!0});var I5t=s(pW);Gir=r(I5t,"DPTForSemanticSegmentation"),I5t.forEach(t),Oir=r(VSe," (DPT model)"),VSe.forEach(t),Vir=i($w),JF=n($w,"LI",{});var XSe=s(JF);Ebe=n(XSe,"STRONG",{});var N5t=s(Ebe);Xir=r(N5t,"segformer"),N5t.forEach(t),zir=r(XSe," \u2014 "),_W=n(XSe,"A",{href:!0});var q5t=s(_W);Wir=r(q5t,"SegformerForSemanticSegmentation"),q5t.forEach(t),Qir=r(XSe," (SegFormer model)"),XSe.forEach(t),$w.forEach(t),Hir=i(Ma),YF=n(Ma,"P",{});var zSe=s(YF);Uir=r(zSe,"The model is set in evaluation mode by default using "),Cbe=n(zSe,"CODE",{});var j5t=s(Cbe);Jir=r(j5t,"model.eval()"),j5t.forEach(t),Yir=r(zSe,` (so for instance, dropout modules are
deactivated). To train the model, you should first set it back in training mode with `),wbe=n(zSe,"CODE",{});var D5t=s(wbe);Kir=r(D5t,"model.train()"),D5t.forEach(t),zSe.forEach(t),Zir=i(Ma),T(KF.$$.fragment,Ma),Ma.forEach(t),ml.forEach(t),qje=i(f),jd=n(f,"H2",{class:!0});var XGe=s(jd);ZF=n(XGe,"A",{id:!0,class:!0,href:!0});var G5t=s(ZF);Abe=n(G5t,"SPAN",{});var O5t=s(Abe);T(mL.$$.fragment,O5t),O5t.forEach(t),G5t.forEach(t),edr=i(XGe),ybe=n(XGe,"SPAN",{});var V5t=s(ybe);odr=r(V5t,"AutoModelForInstanceSegmentation"),V5t.forEach(t),XGe.forEach(t),jje=i(f),Jo=n(f,"DIV",{class:!0});var gl=s(Jo);T(gL.$$.fragment,gl),rdr=i(gl),Dd=n(gl,"P",{});var Aee=s(Dd);tdr=r(Aee,`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 `),uW=n(Aee,"A",{href:!0});var X5t=s(uW);adr=r(X5t,"from_pretrained()"),X5t.forEach(t),ndr=r(Aee," class method or the "),bW=n(Aee,"A",{href:!0});var z5t=s(bW);sdr=r(z5t,"from_config()"),z5t.forEach(t),ldr=r(Aee,` class
method.`),Aee.forEach(t),idr=i(gl),hL=n(gl,"P",{});var zGe=s(hL);ddr=r(zGe,"This class cannot be instantiated directly using "),Lbe=n(zGe,"CODE",{});var W5t=s(Lbe);cdr=r(W5t,"__init__()"),W5t.forEach(t),fdr=r(zGe," (throws an error)."),zGe.forEach(t),mdr=i(gl),At=n(gl,"DIV",{class:!0});var kw=s(At);T(pL.$$.fragment,kw),gdr=i(kw),xbe=n(kw,"P",{});var Q5t=s(xbe);hdr=r(Q5t,"Instantiates one of the model classes of the library (with a instance segmentation head) from a configuration."),Q5t.forEach(t),pdr=i(kw),Gd=n(kw,"P",{});var yee=s(Gd);_dr=r(yee,`Note:
Loading a model from its configuration file does `),$be=n(yee,"STRONG",{});var H5t=s($be);udr=r(H5t,"not"),H5t.forEach(t),bdr=r(yee,` load the model weights. It only affects the
model\u2019s configuration. Use `),vW=n(yee,"A",{href:!0});var U5t=s(vW);vdr=r(U5t,"from_pretrained()"),U5t.forEach(t),Fdr=r(yee," to load the model weights."),yee.forEach(t),Tdr=i(kw),T(eT.$$.fragment,kw),kw.forEach(t),Mdr=i(gl),vo=n(gl,"DIV",{class:!0});var Ea=s(vo);T(_L.$$.fragment,Ea),Edr=i(Ea),kbe=n(Ea,"P",{});var J5t=s(kbe);Cdr=r(J5t,"Instantiate one of the model classes of the library (with a instance segmentation head) from a pretrained model."),J5t.forEach(t),wdr=i(Ea),Ya=n(Ea,"P",{});var Sw=s(Ya);Adr=r(Sw,"The model class to instantiate is selected based on the "),Sbe=n(Sw,"CODE",{});var Y5t=s(Sbe);ydr=r(Y5t,"model_type"),Y5t.forEach(t),Ldr=r(Sw,` property of the config object (either
passed as an argument or loaded from `),Rbe=n(Sw,"CODE",{});var K5t=s(Rbe);xdr=r(K5t,"pretrained_model_name_or_path"),K5t.forEach(t),$dr=r(Sw,` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),Pbe=n(Sw,"CODE",{});var Z5t=s(Pbe);kdr=r(Z5t,"pretrained_model_name_or_path"),Z5t.forEach(t),Sdr=r(Sw,":"),Sw.forEach(t),Rdr=i(Ea),Bbe=n(Ea,"UL",{});var eFt=s(Bbe);oT=n(eFt,"LI",{});var WSe=s(oT);Ibe=n(WSe,"STRONG",{});var oFt=s(Ibe);Pdr=r(oFt,"maskformer"),oFt.forEach(t),Bdr=r(WSe," \u2014 "),FW=n(WSe,"A",{href:!0});var rFt=s(FW);Idr=r(rFt,"MaskFormerForInstanceSegmentation"),rFt.forEach(t),Ndr=r(WSe," (MaskFormer model)"),WSe.forEach(t),eFt.forEach(t),qdr=i(Ea),rT=n(Ea,"P",{});var QSe=s(rT);jdr=r(QSe,"The model is set in evaluation mode by default using "),Nbe=n(QSe,"CODE",{});var tFt=s(Nbe);Ddr=r(tFt,"model.eval()"),tFt.forEach(t),Gdr=r(QSe,` (so for instance, dropout modules are
deactivated). To train the model, you should first set it back in training mode with `),qbe=n(QSe,"CODE",{});var aFt=s(qbe);Odr=r(aFt,"model.train()"),aFt.forEach(t),QSe.forEach(t),Vdr=i(Ea),T(tT.$$.fragment,Ea),Ea.forEach(t),gl.forEach(t),Dje=i(f),Od=n(f,"H2",{class:!0});var WGe=s(Od);aT=n(WGe,"A",{id:!0,class:!0,href:!0});var nFt=s(aT);jbe=n(nFt,"SPAN",{});var sFt=s(jbe);T(uL.$$.fragment,sFt),sFt.forEach(t),nFt.forEach(t),Xdr=i(WGe),Dbe=n(WGe,"SPAN",{});var lFt=s(Dbe);zdr=r(lFt,"TFAutoModel"),lFt.forEach(t),WGe.forEach(t),Gje=i(f),Yo=n(f,"DIV",{class:!0});var hl=s(Yo);T(bL.$$.fragment,hl),Wdr=i(hl),Vd=n(hl,"P",{});var Lee=s(Vd);Qdr=r(Lee,`This is a generic model class that will be instantiated as one of the base model classes of the library when created
with the `),TW=n(Lee,"A",{href:!0});var iFt=s(TW);Hdr=r(iFt,"from_pretrained()"),iFt.forEach(t),Udr=r(Lee," class method or the "),MW=n(Lee,"A",{href:!0});var dFt=s(MW);Jdr=r(dFt,"from_config()"),dFt.forEach(t),Ydr=r(Lee,` class
method.`),Lee.forEach(t),Kdr=i(hl),vL=n(hl,"P",{});var QGe=s(vL);Zdr=r(QGe,"This class cannot be instantiated directly using "),Gbe=n(QGe,"CODE",{});var cFt=s(Gbe);ecr=r(cFt,"__init__()"),cFt.forEach(t),ocr=r(QGe," (throws an error)."),QGe.forEach(t),rcr=i(hl),yt=n(hl,"DIV",{class:!0});var Rw=s(yt);T(FL.$$.fragment,Rw),tcr=i(Rw),Obe=n(Rw,"P",{});var fFt=s(Obe);acr=r(fFt,"Instantiates one of the base model classes of the library from a configuration."),fFt.forEach(t),ncr=i(Rw),Xd=n(Rw,"P",{});var xee=s(Xd);scr=r(xee,`Note:
Loading a model from its configuration file does `),Vbe=n(xee,"STRONG",{});var mFt=s(Vbe);lcr=r(mFt,"not"),mFt.forEach(t),icr=r(xee,` load the model weights. It only affects the
model\u2019s configuration. Use `),EW=n(xee,"A",{href:!0});var gFt=s(EW);dcr=r(gFt,"from_pretrained()"),gFt.forEach(t),ccr=r(xee," to load the model weights."),xee.forEach(t),fcr=i(Rw),T(nT.$$.fragment,Rw),Rw.forEach(t),mcr=i(hl),wr=n(hl,"DIV",{class:!0});var pl=s(wr);T(TL.$$.fragment,pl),gcr=i(pl),Xbe=n(pl,"P",{});var hFt=s(Xbe);hcr=r(hFt,"Instantiate one of the base model classes of the library from a pretrained model."),hFt.forEach(t),pcr=i(pl),Ka=n(pl,"P",{});var Pw=s(Ka);_cr=r(Pw,"The model class to instantiate is selected based on the "),zbe=n(Pw,"CODE",{});var pFt=s(zbe);ucr=r(pFt,"model_type"),pFt.forEach(t),bcr=r(Pw,` property of the config object (either
passed as an argument or loaded from `),Wbe=n(Pw,"CODE",{});var _Ft=s(Wbe);vcr=r(_Ft,"pretrained_model_name_or_path"),_Ft.forEach(t),Fcr=r(Pw,` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),Qbe=n(Pw,"CODE",{});var uFt=s(Qbe);Tcr=r(uFt,"pretrained_model_name_or_path"),uFt.forEach(t),Mcr=r(Pw,":"),Pw.forEach(t),Ecr=i(pl),q=n(pl,"UL",{});var D=s(q);sT=n(D,"LI",{});var HSe=s(sT);Hbe=n(HSe,"STRONG",{});var bFt=s(Hbe);Ccr=r(bFt,"albert"),bFt.forEach(t),wcr=r(HSe," \u2014 "),CW=n(HSe,"A",{href:!0});var vFt=s(CW);Acr=r(vFt,"TFAlbertModel"),vFt.forEach(t),ycr=r(HSe," (ALBERT model)"),HSe.forEach(t),Lcr=i(D),lT=n(D,"LI",{});var USe=s(lT);Ube=n(USe,"STRONG",{});var FFt=s(Ube);xcr=r(FFt,"bart"),FFt.forEach(t),$cr=r(USe," \u2014 "),wW=n(USe,"A",{href:!0});var TFt=s(wW);kcr=r(TFt,"TFBartModel"),TFt.forEach(t),Scr=r(USe," (BART model)"),USe.forEach(t),Rcr=i(D),iT=n(D,"LI",{});var JSe=s(iT);Jbe=n(JSe,"STRONG",{});var MFt=s(Jbe);Pcr=r(MFt,"bert"),MFt.forEach(t),Bcr=r(JSe," \u2014 "),AW=n(JSe,"A",{href:!0});var EFt=s(AW);Icr=r(EFt,"TFBertModel"),EFt.forEach(t),Ncr=r(JSe," (BERT model)"),JSe.forEach(t),qcr=i(D),dT=n(D,"LI",{});var 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model\u2019s configuration. Use `),FQ=n(kee,"A",{href:!0});var g8t=s(FQ);cpr=r(g8t,"from_pretrained()"),g8t.forEach(t),fpr=r(kee," to load the model weights."),kee.forEach(t),mpr=i(Bw),T(e8.$$.fragment,Bw),Bw.forEach(t),gpr=i(_l),Ar=n(_l,"DIV",{class:!0});var ul=s(Ar);T(AL.$$.fragment,ul),hpr=i(ul),W2e=n(ul,"P",{});var h8t=s(W2e);ppr=r(h8t,"Instantiate one of the model classes of the library (with a pretraining head) from a pretrained model."),h8t.forEach(t),_pr=i(ul),Za=n(ul,"P",{});var Iw=s(Za);upr=r(Iw,"The model class to instantiate is selected based on the "),Q2e=n(Iw,"CODE",{});var p8t=s(Q2e);bpr=r(p8t,"model_type"),p8t.forEach(t),vpr=r(Iw,` property of the config object (either
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falling back to using pattern matching on `),U2e=n(Iw,"CODE",{});var u8t=s(U2e);Mpr=r(u8t,"pretrained_model_name_or_path"),u8t.forEach(t),Epr=r(Iw,":"),Iw.forEach(t),Cpr=i(ul),se=n(ul,"UL",{});var le=s(se);o8=n(le,"LI",{});var DRe=s(o8);J2e=n(DRe,"STRONG",{});var b8t=s(J2e);wpr=r(b8t,"albert"),b8t.forEach(t),Apr=r(DRe," \u2014 "),TQ=n(DRe,"A",{href:!0});var v8t=s(TQ);ypr=r(v8t,"TFAlbertForPreTraining"),v8t.forEach(t),Lpr=r(DRe," (ALBERT model)"),DRe.forEach(t),xpr=i(le),r8=n(le,"LI",{});var GRe=s(r8);Y2e=n(GRe,"STRONG",{});var F8t=s(Y2e);$pr=r(F8t,"bart"),F8t.forEach(t),kpr=r(GRe," \u2014 "),MQ=n(GRe,"A",{href:!0});var T8t=s(MQ);Spr=r(T8t,"TFBartForConditionalGeneration"),T8t.forEach(t),Rpr=r(GRe," (BART model)"),GRe.forEach(t),Ppr=i(le),t8=n(le,"LI",{});var ORe=s(t8);K2e=n(ORe,"STRONG",{});var M8t=s(K2e);Bpr=r(M8t,"bert"),M8t.forEach(t),Ipr=r(ORe," \u2014 "),EQ=n(ORe,"A",{href:!0});var E8t=s(EQ);Npr=r(E8t,"TFBertForPreTraining"),E8t.forEach(t),qpr=r(ORe," (BERT 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method.`),See.forEach(t),Dur=i(bl),xL=n(bl,"P",{});var YGe=s(xL);Gur=r(YGe,"This class cannot be instantiated directly using "),T4e=n(YGe,"CODE",{});var p7t=s(T4e);Our=r(p7t,"__init__()"),p7t.forEach(t),Vur=r(YGe," (throws an error)."),YGe.forEach(t),Xur=i(bl),xt=n(bl,"DIV",{class:!0});var Nw=s(xt);T($L.$$.fragment,Nw),zur=i(Nw),M4e=n(Nw,"P",{});var _7t=s(M4e);Wur=r(_7t,"Instantiates one of the model classes of the library (with a causal language modeling head) from a configuration."),_7t.forEach(t),Qur=i(Nw),Jd=n(Nw,"P",{});var Ree=s(Jd);Hur=r(Ree,`Note:
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with the `),sH=n(Pee,"A",{href:!0});var K7t=s(sH);ybr=r(K7t,"from_pretrained()"),K7t.forEach(t),Lbr=r(Pee," class method or the "),lH=n(Pee,"A",{href:!0});var Z7t=s(lH);xbr=r(Z7t,"from_config()"),Z7t.forEach(t),$br=r(Pee,` class
method.`),Pee.forEach(t),kbr=i(Fl),PL=n(Fl,"P",{});var ZGe=s(PL);Sbr=r(ZGe,"This class cannot be instantiated directly using "),V4e=n(ZGe,"CODE",{});var eMt=s(V4e);Rbr=r(eMt,"__init__()"),eMt.forEach(t),Pbr=r(ZGe," (throws an error)."),ZGe.forEach(t),Bbr=i(Fl),$t=n(Fl,"DIV",{class:!0});var jw=s($t);T(BL.$$.fragment,jw),Ibr=i(jw),X4e=n(jw,"P",{});var oMt=s(X4e);Nbr=r(oMt,"Instantiates one of the model classes of the library (with a image classification head) from a configuration."),oMt.forEach(t),qbr=i(jw),Zd=n(jw,"P",{});var Bee=s(Zd);jbr=r(Bee,`Note:
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model\u2019s configuration. Use `),iH=n(Bee,"A",{href:!0});var tMt=s(iH);Obr=r(tMt,"from_pretrained()"),tMt.forEach(t),Vbr=r(Bee," to load the model weights."),Bee.forEach(t),Xbr=i(jw),T(O8.$$.fragment,jw),jw.forEach(t),zbr=i(Fl),Lr=n(Fl,"DIV",{class:!0});var Tl=s(Lr);T(IL.$$.fragment,Tl),Wbr=i(Tl),W4e=n(Tl,"P",{});var aMt=s(W4e);Qbr=r(aMt,"Instantiate one of the model classes of the library (with a image classification head) from a pretrained model."),aMt.forEach(t),Hbr=i(Tl),on=n(Tl,"P",{});var Dw=s(on);Ubr=r(Dw,"The model class to instantiate is selected based on the "),Q4e=n(Dw,"CODE",{});var nMt=s(Q4e);Jbr=r(nMt,"model_type"),nMt.forEach(t),Ybr=r(Dw,` property of the config object (either
passed as an argument or loaded from `),H4e=n(Dw,"CODE",{});var sMt=s(H4e);Kbr=r(sMt,"pretrained_model_name_or_path"),sMt.forEach(t),Zbr=r(Dw,` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),U4e=n(Dw,"CODE",{});var lMt=s(U4e);e2r=r(lMt,"pretrained_model_name_or_path"),lMt.forEach(t),o2r=r(Dw,":"),Dw.forEach(t),r2r=i(Tl),rn=n(Tl,"UL",{});var Gw=s(rn);V8=n(Gw,"LI",{});var MPe=s(V8);J4e=n(MPe,"STRONG",{});var iMt=s(J4e);t2r=r(iMt,"convnext"),iMt.forEach(t),a2r=r(MPe," \u2014 "),dH=n(MPe,"A",{href:!0});var dMt=s(dH);n2r=r(dMt,"TFConvNextForImageClassification"),dMt.forEach(t),s2r=r(MPe," (ConvNeXT model)"),MPe.forEach(t),l2r=i(Gw),X8=n(Gw,"LI",{});var EPe=s(X8);Y4e=n(EPe,"STRONG",{});var cMt=s(Y4e);i2r=r(cMt,"data2vec-vision"),cMt.forEach(t),d2r=r(EPe," \u2014 "),cH=n(EPe,"A",{href:!0});var fMt=s(cH);c2r=r(fMt,"TFData2VecVisionForImageClassification"),fMt.forEach(t),f2r=r(EPe," (Data2VecVision model)"),EPe.forEach(t),m2r=i(Gw),z8=n(Gw,"LI",{});var CPe=s(z8);K4e=n(CPe,"STRONG",{});var mMt=s(K4e);g2r=r(mMt,"swin"),mMt.forEach(t),h2r=r(CPe," \u2014 "),fH=n(CPe,"A",{href:!0});var gMt=s(fH);p2r=r(gMt,"TFSwinForImageClassification"),gMt.forEach(t),_2r=r(CPe," (Swin Transformer model)"),CPe.forEach(t),u2r=i(Gw),W8=n(Gw,"LI",{});var wPe=s(W8);Z4e=n(wPe,"STRONG",{});var hMt=s(Z4e);b2r=r(hMt,"vit"),hMt.forEach(t),v2r=r(wPe," \u2014 "),mH=n(wPe,"A",{href:!0});var pMt=s(mH);F2r=r(pMt,"TFViTForImageClassification"),pMt.forEach(t),T2r=r(wPe," (ViT model)"),wPe.forEach(t),Gw.forEach(t),M2r=i(Tl),T(Q8.$$.fragment,Tl),Tl.forEach(t),Fl.forEach(t),Hje=i(f),ec=n(f,"H2",{class:!0});var eOe=s(ec);H8=n(eOe,"A",{id:!0,class:!0,href:!0});var _Mt=s(H8);eve=n(_Mt,"SPAN",{});var uMt=s(eve);T(NL.$$.fragment,uMt),uMt.forEach(t),_Mt.forEach(t),E2r=i(eOe),ove=n(eOe,"SPAN",{});var bMt=s(ove);C2r=r(bMt,"TFAutoModelForMaskedLM"),bMt.forEach(t),eOe.forEach(t),Uje=i(f),or=n(f,"DIV",{class:!0});var Ml=s(or);T(qL.$$.fragment,Ml),w2r=i(Ml),oc=n(Ml,"P",{});var Iee=s(oc);A2r=r(Iee,`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 `),gH=n(Iee,"A",{href:!0});var vMt=s(gH);y2r=r(vMt,"from_pretrained()"),vMt.forEach(t),L2r=r(Iee," class method or the "),hH=n(Iee,"A",{href:!0});var FMt=s(hH);x2r=r(FMt,"from_config()"),FMt.forEach(t),$2r=r(Iee,` class
method.`),Iee.forEach(t),k2r=i(Ml),jL=n(Ml,"P",{});var oOe=s(jL);S2r=r(oOe,"This class cannot be instantiated directly using "),rve=n(oOe,"CODE",{});var TMt=s(rve);R2r=r(TMt,"__init__()"),TMt.forEach(t),P2r=r(oOe," (throws an error)."),oOe.forEach(t),B2r=i(Ml),kt=n(Ml,"DIV",{class:!0});var Ow=s(kt);T(DL.$$.fragment,Ow),I2r=i(Ow),tve=n(Ow,"P",{});var MMt=s(tve);N2r=r(MMt,"Instantiates one of the model classes of the library (with a masked language modeling head) from a configuration."),MMt.forEach(t),q2r=i(Ow),rc=n(Ow,"P",{});var Nee=s(rc);j2r=r(Nee,`Note:
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model\u2019s configuration. Use `),pH=n(Nee,"A",{href:!0});var CMt=s(pH);O2r=r(CMt,"from_pretrained()"),CMt.forEach(t),V2r=r(Nee," to load the model weights."),Nee.forEach(t),X2r=i(Ow),T(U8.$$.fragment,Ow),Ow.forEach(t),z2r=i(Ml),xr=n(Ml,"DIV",{class:!0});var El=s(xr);T(GL.$$.fragment,El),W2r=i(El),nve=n(El,"P",{});var wMt=s(nve);Q2r=r(wMt,"Instantiate one of the model classes of the library (with a masked language modeling head) from a pretrained model."),wMt.forEach(t),H2r=i(El),tn=n(El,"P",{});var Vw=s(tn);U2r=r(Vw,"The model class to instantiate is selected based on the "),sve=n(Vw,"CODE",{});var AMt=s(sve);J2r=r(AMt,"model_type"),AMt.forEach(t),Y2r=r(Vw,` property of the config object (either
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falling back to using pattern matching on `),ive=n(Vw,"CODE",{});var LMt=s(ive);e4r=r(LMt,"pretrained_model_name_or_path"),LMt.forEach(t),o4r=r(Vw,":"),Vw.forEach(t),r4r=i(El),ie=n(El,"UL",{});var fe=s(ie);J8=n(fe,"LI",{});var APe=s(J8);dve=n(APe,"STRONG",{});var xMt=s(dve);t4r=r(xMt,"albert"),xMt.forEach(t),a4r=r(APe," \u2014 "),_H=n(APe,"A",{href:!0});var $Mt=s(_H);n4r=r($Mt,"TFAlbertForMaskedLM"),$Mt.forEach(t),s4r=r(APe," (ALBERT model)"),APe.forEach(t),l4r=i(fe),Y8=n(fe,"LI",{});var yPe=s(Y8);cve=n(yPe,"STRONG",{});var kMt=s(cve);i4r=r(kMt,"bert"),kMt.forEach(t),d4r=r(yPe," \u2014 "),uH=n(yPe,"A",{href:!0});var SMt=s(uH);c4r=r(SMt,"TFBertForMaskedLM"),SMt.forEach(t),f4r=r(yPe," (BERT model)"),yPe.forEach(t),m4r=i(fe),K8=n(fe,"LI",{});var LPe=s(K8);fve=n(LPe,"STRONG",{});var RMt=s(fve);g4r=r(RMt,"camembert"),RMt.forEach(t),h4r=r(LPe," \u2014 "),bH=n(LPe,"A",{href:!0});var PMt=s(bH);p4r=r(PMt,"TFCamembertForMaskedLM"),PMt.forEach(t),_4r=r(LPe," (CamemBERT 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falling back to using pattern matching on `),Nve=n(zw,"CODE",{});var yEt=s(Nve);A5r=r(yEt,"pretrained_model_name_or_path"),yEt.forEach(t),y5r=r(zw,":"),zw.forEach(t),L5r=i(wl),ye=n(wl,"UL",{});var Be=s(ye);v7=n(Be,"LI",{});var WPe=s(v7);qve=n(WPe,"STRONG",{});var LEt=s(qve);x5r=r(LEt,"bart"),LEt.forEach(t),$5r=r(WPe," \u2014 "),jH=n(WPe,"A",{href:!0});var xEt=s(jH);k5r=r(xEt,"TFBartForConditionalGeneration"),xEt.forEach(t),S5r=r(WPe," (BART model)"),WPe.forEach(t),R5r=i(Be),F7=n(Be,"LI",{});var QPe=s(F7);jve=n(QPe,"STRONG",{});var $Et=s(jve);P5r=r($Et,"blenderbot"),$Et.forEach(t),B5r=r(QPe," \u2014 "),DH=n(QPe,"A",{href:!0});var kEt=s(DH);I5r=r(kEt,"TFBlenderbotForConditionalGeneration"),kEt.forEach(t),N5r=r(QPe," (Blenderbot model)"),QPe.forEach(t),q5r=i(Be),T7=n(Be,"LI",{});var HPe=s(T7);Dve=n(HPe,"STRONG",{});var SEt=s(Dve);j5r=r(SEt,"blenderbot-small"),SEt.forEach(t),D5r=r(HPe," \u2014 "),GH=n(HPe,"A",{href:!0});var REt=s(GH);G5r=r(REt,"TFBlenderbotSmallForConditionalGeneration"),REt.forEach(t),O5r=r(HPe," (BlenderbotSmall model)"),HPe.forEach(t),V5r=i(Be),M7=n(Be,"LI",{});var UPe=s(M7);Gve=n(UPe,"STRONG",{});var PEt=s(Gve);X5r=r(PEt,"encoder-decoder"),PEt.forEach(t),z5r=r(UPe," \u2014 "),OH=n(UPe,"A",{href:!0});var BEt=s(OH);W5r=r(BEt,"TFEncoderDecoderModel"),BEt.forEach(t),Q5r=r(UPe," (Encoder decoder model)"),UPe.forEach(t),H5r=i(Be),E7=n(Be,"LI",{});var JPe=s(E7);Ove=n(JPe,"STRONG",{});var IEt=s(Ove);U5r=r(IEt,"led"),IEt.forEach(t),J5r=r(JPe," \u2014 "),VH=n(JPe,"A",{href:!0});var NEt=s(VH);Y5r=r(NEt,"TFLEDForConditionalGeneration"),NEt.forEach(t),K5r=r(JPe," (LED model)"),JPe.forEach(t),Z5r=i(Be),C7=n(Be,"LI",{});var YPe=s(C7);Vve=n(YPe,"STRONG",{});var qEt=s(Vve);eFr=r(qEt,"marian"),qEt.forEach(t),oFr=r(YPe," \u2014 "),XH=n(YPe,"A",{href:!0});var jEt=s(XH);rFr=r(jEt,"TFMarianMTModel"),jEt.forEach(t),tFr=r(YPe," (Marian model)"),YPe.forEach(t),aFr=i(Be),w7=n(Be,"LI",{});var KPe=s(w7);Xve=n(KPe,"STRONG",{});var DEt=s(Xve);nFr=r(DEt,"mbart"),DEt.forEach(t),sFr=r(KPe," \u2014 "),zH=n(KPe,"A",{href:!0});var GEt=s(zH);lFr=r(GEt,"TFMBartForConditionalGeneration"),GEt.forEach(t),iFr=r(KPe," (mBART model)"),KPe.forEach(t),dFr=i(Be),A7=n(Be,"LI",{});var ZPe=s(A7);zve=n(ZPe,"STRONG",{});var OEt=s(zve);cFr=r(OEt,"mt5"),OEt.forEach(t),fFr=r(ZPe," \u2014 "),WH=n(ZPe,"A",{href:!0});var VEt=s(WH);mFr=r(VEt,"TFMT5ForConditionalGeneration"),VEt.forEach(t),gFr=r(ZPe," (MT5 model)"),ZPe.forEach(t),hFr=i(Be),y7=n(Be,"LI",{});var eBe=s(y7);Wve=n(eBe,"STRONG",{});var XEt=s(Wve);pFr=r(XEt,"pegasus"),XEt.forEach(t),_Fr=r(eBe," \u2014 "),QH=n(eBe,"A",{href:!0});var zEt=s(QH);uFr=r(zEt,"TFPegasusForConditionalGeneration"),zEt.forEach(t),bFr=r(eBe," (Pegasus model)"),eBe.forEach(t),vFr=i(Be),L7=n(Be,"LI",{});var oBe=s(L7);Qve=n(oBe,"STRONG",{});var WEt=s(Qve);FFr=r(WEt,"t5"),WEt.forEach(t),TFr=r(oBe," \u2014 "),HH=n(oBe,"A",{href:!0});var QEt=s(HH);MFr=r(QEt,"TFT5ForConditionalGeneration"),QEt.forEach(t),EFr=r(oBe," (T5 model)"),oBe.forEach(t),Be.forEach(t),CFr=i(wl),T(x7.$$.fragment,wl),wl.forEach(t),Cl.forEach(t),Kje=i(f),sc=n(f,"H2",{class:!0});var aOe=s(sc);$7=n(aOe,"A",{id:!0,class:!0,href:!0});var HEt=s($7);Hve=n(HEt,"SPAN",{});var UEt=s(Hve);T(QL.$$.fragment,UEt),UEt.forEach(t),HEt.forEach(t),wFr=i(aOe),Uve=n(aOe,"SPAN",{});var JEt=s(Uve);AFr=r(JEt,"TFAutoModelForSequenceClassification"),JEt.forEach(t),aOe.forEach(t),Zje=i(f),tr=n(f,"DIV",{class:!0});var Al=s(tr);T(HL.$$.fragment,Al),yFr=i(Al),lc=n(Al,"P",{});var Dee=s(lc);LFr=r(Dee,`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
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model\u2019s configuration. Use `),WU=n(zee,"A",{href:!0});var s0t=s(WU);tCr=r(s0t,"from_pretrained()"),s0t.forEach(t),aCr=r(zee," to load the model weights."),zee.forEach(t),nCr=i(Jw),T(AM.$$.fragment,Jw),Jw.forEach(t),sCr=i($l),Rr=n($l,"DIV",{class:!0});var kl=s(Rr);T(l9.$$.fragment,kl),lCr=i(kl),sFe=n(kl,"P",{});var l0t=s(sFe);iCr=r(l0t,"Instantiate one of the model classes of the library (with a next sentence prediction head) from a pretrained model."),l0t.forEach(t),dCr=i(kl),ln=n(kl,"P",{});var Yw=s(ln);cCr=r(Yw,"The model class to instantiate is selected based on the "),lFe=n(Yw,"CODE",{});var i0t=s(lFe);fCr=r(i0t,"model_type"),i0t.forEach(t),mCr=r(Yw,` property of the config object (either
passed as an argument or loaded from `),iFe=n(Yw,"CODE",{});var d0t=s(iFe);gCr=r(d0t,"pretrained_model_name_or_path"),d0t.forEach(t),hCr=r(Yw,` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),dFe=n(Yw,"CODE",{});var c0t=s(dFe);pCr=r(c0t,"pretrained_model_name_or_path"),c0t.forEach(t),_Cr=r(Yw,":"),Yw.forEach(t),uCr=i(kl),i9=n(kl,"UL",{});var cOe=s(i9);yM=n(cOe,"LI",{});var zBe=s(yM);cFe=n(zBe,"STRONG",{});var f0t=s(cFe);bCr=r(f0t,"bert"),f0t.forEach(t),vCr=r(zBe," \u2014 "),QU=n(zBe,"A",{href:!0});var m0t=s(QU);FCr=r(m0t,"TFBertForNextSentencePrediction"),m0t.forEach(t),TCr=r(zBe," (BERT model)"),zBe.forEach(t),MCr=i(cOe),LM=n(cOe,"LI",{});var WBe=s(LM);fFe=n(WBe,"STRONG",{});var g0t=s(fFe);ECr=r(g0t,"mobilebert"),g0t.forEach(t),CCr=r(WBe," \u2014 "),HU=n(WBe,"A",{href:!0});var h0t=s(HU);wCr=r(h0t,"TFMobileBertForNextSentencePrediction"),h0t.forEach(t),ACr=r(WBe," (MobileBERT model)"),WBe.forEach(t),cOe.forEach(t),yCr=i(kl),T(xM.$$.fragment,kl),kl.forEach(t),$l.forEach(t),aDe=i(f),pc=n(f,"H2",{class:!0});var fOe=s(pc);$M=n(fOe,"A",{id:!0,class:!0,href:!0});var p0t=s($M);mFe=n(p0t,"SPAN",{});var _0t=s(mFe);T(d9.$$.fragment,_0t),_0t.forEach(t),p0t.forEach(t),LCr=i(fOe),gFe=n(fOe,"SPAN",{});var u0t=s(gFe);xCr=r(u0t,"TFAutoModelForTableQuestionAnswering"),u0t.forEach(t),fOe.forEach(t),nDe=i(f),sr=n(f,"DIV",{class:!0});var Sl=s(sr);T(c9.$$.fragment,Sl),$Cr=i(Sl),_c=n(Sl,"P",{});var Wee=s(_c);kCr=r(Wee,`This is a generic model class that will be instantiated as one of the model classes of the library (with a table question answering head) when created
with the `),UU=n(Wee,"A",{href:!0});var b0t=s(UU);SCr=r(b0t,"from_pretrained()"),b0t.forEach(t),RCr=r(Wee," class method or the "),JU=n(Wee,"A",{href:!0});var v0t=s(JU);PCr=r(v0t,"from_config()"),v0t.forEach(t),BCr=r(Wee,` class
method.`),Wee.forEach(t),ICr=i(Sl),f9=n(Sl,"P",{});var mOe=s(f9);NCr=r(mOe,"This class cannot be instantiated directly using "),hFe=n(mOe,"CODE",{});var F0t=s(hFe);qCr=r(F0t,"__init__()"),F0t.forEach(t),jCr=r(mOe," (throws an error)."),mOe.forEach(t),DCr=i(Sl),It=n(Sl,"DIV",{class:!0});var Kw=s(It);T(m9.$$.fragment,Kw),GCr=i(Kw),pFe=n(Kw,"P",{});var T0t=s(pFe);OCr=r(T0t,"Instantiates one of the model classes of the library (with a table question answering head) from a configuration."),T0t.forEach(t),VCr=i(Kw),uc=n(Kw,"P",{});var Qee=s(uc);XCr=r(Qee,`Note:
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model\u2019s configuration. Use `),YU=n(Qee,"A",{href:!0});var E0t=s(YU);QCr=r(E0t,"from_pretrained()"),E0t.forEach(t),HCr=r(Qee," to load the model weights."),Qee.forEach(t),UCr=i(Kw),T(kM.$$.fragment,Kw),Kw.forEach(t),JCr=i(Sl),Pr=n(Sl,"DIV",{class:!0});var Rl=s(Pr);T(g9.$$.fragment,Rl),YCr=i(Rl),uFe=n(Rl,"P",{});var C0t=s(uFe);KCr=r(C0t,"Instantiate one of the model classes of the library (with a table question answering head) from a pretrained model."),C0t.forEach(t),ZCr=i(Rl),dn=n(Rl,"P",{});var Zw=s(dn);e3r=r(Zw,"The model class to instantiate is selected based on the "),bFe=n(Zw,"CODE",{});var w0t=s(bFe);o3r=r(w0t,"model_type"),w0t.forEach(t),r3r=r(Zw,` property of the config object (either
passed as an argument or loaded from `),vFe=n(Zw,"CODE",{});var A0t=s(vFe);t3r=r(A0t,"pretrained_model_name_or_path"),A0t.forEach(t),a3r=r(Zw,` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),FFe=n(Zw,"CODE",{});var y0t=s(FFe);n3r=r(y0t,"pretrained_model_name_or_path"),y0t.forEach(t),s3r=r(Zw,":"),Zw.forEach(t),l3r=i(Rl),TFe=n(Rl,"UL",{});var L0t=s(TFe);SM=n(L0t,"LI",{});var QBe=s(SM);MFe=n(QBe,"STRONG",{});var x0t=s(MFe);i3r=r(x0t,"tapas"),x0t.forEach(t),d3r=r(QBe," \u2014 "),KU=n(QBe,"A",{href:!0});var $0t=s(KU);c3r=r($0t,"TFTapasForQuestionAnswering"),$0t.forEach(t),f3r=r(QBe," (TAPAS model)"),QBe.forEach(t),L0t.forEach(t),m3r=i(Rl),T(RM.$$.fragment,Rl),Rl.forEach(t),Sl.forEach(t),sDe=i(f),bc=n(f,"H2",{class:!0});var gOe=s(bc);PM=n(gOe,"A",{id:!0,class:!0,href:!0});var k0t=s(PM);EFe=n(k0t,"SPAN",{});var S0t=s(EFe);T(h9.$$.fragment,S0t),S0t.forEach(t),k0t.forEach(t),g3r=i(gOe),CFe=n(gOe,"SPAN",{});var R0t=s(CFe);h3r=r(R0t,"TFAutoModelForTokenClassification"),R0t.forEach(t),gOe.forEach(t),lDe=i(f),lr=n(f,"DIV",{class:!0});var Pl=s(lr);T(p9.$$.fragment,Pl),p3r=i(Pl),vc=n(Pl,"P",{});var Hee=s(vc);_3r=r(Hee,`This is a generic model class that will be instantiated as one of the model classes of the library (with a token classification head) when created
with the `),ZU=n(Hee,"A",{href:!0});var P0t=s(ZU);u3r=r(P0t,"from_pretrained()"),P0t.forEach(t),b3r=r(Hee," class method or the "),eJ=n(Hee,"A",{href:!0});var B0t=s(eJ);v3r=r(B0t,"from_config()"),B0t.forEach(t),F3r=r(Hee,` class
method.`),Hee.forEach(t),T3r=i(Pl),_9=n(Pl,"P",{});var hOe=s(_9);M3r=r(hOe,"This class cannot be instantiated directly using "),wFe=n(hOe,"CODE",{});var I0t=s(wFe);E3r=r(I0t,"__init__()"),I0t.forEach(t),C3r=r(hOe," (throws an error)."),hOe.forEach(t),w3r=i(Pl),Nt=n(Pl,"DIV",{class:!0});var eA=s(Nt);T(u9.$$.fragment,eA),A3r=i(eA),AFe=n(eA,"P",{});var N0t=s(AFe);y3r=r(N0t,"Instantiates one of the model classes of the library (with a token classification head) from a configuration."),N0t.forEach(t),L3r=i(eA),Fc=n(eA,"P",{});var Uee=s(Fc);x3r=r(Uee,`Note:
Loading a model from its configuration file does `),yFe=n(Uee,"STRONG",{});var q0t=s(yFe);$3r=r(q0t,"not"),q0t.forEach(t),k3r=r(Uee,` load the model weights. It only affects the
model\u2019s configuration. Use `),oJ=n(Uee,"A",{href:!0});var j0t=s(oJ);S3r=r(j0t,"from_pretrained()"),j0t.forEach(t),R3r=r(Uee," to load the model weights."),Uee.forEach(t),P3r=i(eA),T(BM.$$.fragment,eA),eA.forEach(t),B3r=i(Pl),Br=n(Pl,"DIV",{class:!0});var Bl=s(Br);T(b9.$$.fragment,Bl),I3r=i(Bl),LFe=n(Bl,"P",{});var D0t=s(LFe);N3r=r(D0t,"Instantiate one of the model classes of the library (with a token classification head) from a pretrained model."),D0t.forEach(t),q3r=i(Bl),cn=n(Bl,"P",{});var oA=s(cn);j3r=r(oA,"The model class to instantiate is selected based on the "),xFe=n(oA,"CODE",{});var G0t=s(xFe);D3r=r(G0t,"model_type"),G0t.forEach(t),G3r=r(oA,` property of the config object (either
passed as an argument or loaded from `),$Fe=n(oA,"CODE",{});var O0t=s($Fe);O3r=r(O0t,"pretrained_model_name_or_path"),O0t.forEach(t),V3r=r(oA,` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),kFe=n(oA,"CODE",{});var V0t=s(kFe);X3r=r(V0t,"pretrained_model_name_or_path"),V0t.forEach(t),z3r=r(oA,":"),oA.forEach(t),W3r=i(Bl),de=n(Bl,"UL",{});var me=s(de);IM=n(me,"LI",{});var HBe=s(IM);SFe=n(HBe,"STRONG",{});var X0t=s(SFe);Q3r=r(X0t,"albert"),X0t.forEach(t),H3r=r(HBe," \u2014 "),rJ=n(HBe,"A",{href:!0});var z0t=s(rJ);U3r=r(z0t,"TFAlbertForTokenClassification"),z0t.forEach(t),J3r=r(HBe," (ALBERT model)"),HBe.forEach(t),Y3r=i(me),NM=n(me,"LI",{});var UBe=s(NM);RFe=n(UBe,"STRONG",{});var W0t=s(RFe);K3r=r(W0t,"bert"),W0t.forEach(t),Z3r=r(UBe," \u2014 "),tJ=n(UBe,"A",{href:!0});var Q0t=s(tJ);e0r=r(Q0t,"TFBertForTokenClassification"),Q0t.forEach(t),o0r=r(UBe," (BERT model)"),UBe.forEach(t),r0r=i(me),qM=n(me,"LI",{});var JBe=s(qM);PFe=n(JBe,"STRONG",{});var H0t=s(PFe);t0r=r(H0t,"camembert"),H0t.forEach(t),a0r=r(JBe," \u2014 "),aJ=n(JBe,"A",{href:!0});var U0t=s(aJ);n0r=r(U0t,"TFCamembertForTokenClassification"),U0t.forEach(t),s0r=r(JBe," (CamemBERT model)"),JBe.forEach(t),l0r=i(me),jM=n(me,"LI",{});var YBe=s(jM);BFe=n(YBe,"STRONG",{});var J0t=s(BFe);i0r=r(J0t,"convbert"),J0t.forEach(t),d0r=r(YBe," \u2014 "),nJ=n(YBe,"A",{href:!0});var Y0t=s(nJ);c0r=r(Y0t,"TFConvBertForTokenClassification"),Y0t.forEach(t),f0r=r(YBe," (ConvBERT model)"),YBe.forEach(t),m0r=i(me),DM=n(me,"LI",{});var KBe=s(DM);IFe=n(KBe,"STRONG",{});var K0t=s(IFe);g0r=r(K0t,"deberta"),K0t.forEach(t),h0r=r(KBe," \u2014 "),sJ=n(KBe,"A",{href:!0});var Z0t=s(sJ);p0r=r(Z0t,"TFDebertaForTokenClassification"),Z0t.forEach(t),_0r=r(KBe," (DeBERTa model)"),KBe.forEach(t),u0r=i(me),GM=n(me,"LI",{});var ZBe=s(GM);NFe=n(ZBe,"STRONG",{});var ewt=s(NFe);b0r=r(ewt,"deberta-v2"),ewt.forEach(t),v0r=r(ZBe," \u2014 "),lJ=n(ZBe,"A",{href:!0});var owt=s(lJ);F0r=r(owt,"TFDebertaV2ForTokenClassification"),owt.forEach(t),T0r=r(ZBe," (DeBERTa-v2 model)"),ZBe.forEach(t),M0r=i(me),OM=n(me,"LI",{});var eIe=s(OM);qFe=n(eIe,"STRONG",{});var rwt=s(qFe);E0r=r(rwt,"distilbert"),rwt.forEach(t),C0r=r(eIe," \u2014 "),iJ=n(eIe,"A",{href:!0});var twt=s(iJ);w0r=r(twt,"TFDistilBertForTokenClassification"),twt.forEach(t),A0r=r(eIe," (DistilBERT model)"),eIe.forEach(t),y0r=i(me),VM=n(me,"LI",{});var oIe=s(VM);jFe=n(oIe,"STRONG",{});var awt=s(jFe);L0r=r(awt,"electra"),awt.forEach(t),x0r=r(oIe," \u2014 "),dJ=n(oIe,"A",{href:!0});var nwt=s(dJ);$0r=r(nwt,"TFElectraForTokenClassification"),nwt.forEach(t),k0r=r(oIe," (ELECTRA model)"),oIe.forEach(t),S0r=i(me),XM=n(me,"LI",{});var rIe=s(XM);DFe=n(rIe,"STRONG",{});var swt=s(DFe);R0r=r(swt,"flaubert"),swt.forEach(t),P0r=r(rIe," \u2014 "),cJ=n(rIe,"A",{href:!0});var lwt=s(cJ);B0r=r(lwt,"TFFlaubertForTokenClassification"),lwt.forEach(t),I0r=r(rIe," (FlauBERT model)"),rIe.forEach(t),N0r=i(me),zM=n(me,"LI",{});var tIe=s(zM);GFe=n(tIe,"STRONG",{});var iwt=s(GFe);q0r=r(iwt,"funnel"),iwt.forEach(t),j0r=r(tIe," \u2014 "),fJ=n(tIe,"A",{href:!0});var dwt=s(fJ);D0r=r(dwt,"TFFunnelForTokenClassification"),dwt.forEach(t),G0r=r(tIe," (Funnel Transformer model)"),tIe.forEach(t),O0r=i(me),WM=n(me,"LI",{});var aIe=s(WM);OFe=n(aIe,"STRONG",{});var cwt=s(OFe);V0r=r(cwt,"layoutlm"),cwt.forEach(t),X0r=r(aIe," \u2014 "),mJ=n(aIe,"A",{href:!0});var fwt=s(mJ);z0r=r(fwt,"TFLayoutLMForTokenClassification"),fwt.forEach(t),W0r=r(aIe," (LayoutLM model)"),aIe.forEach(t),Q0r=i(me),QM=n(me,"LI",{});var nIe=s(QM);VFe=n(nIe,"STRONG",{});var mwt=s(VFe);H0r=r(mwt,"longformer"),mwt.forEach(t),U0r=r(nIe," \u2014 "),gJ=n(nIe,"A",{href:!0});var gwt=s(gJ);J0r=r(gwt,"TFLongformerForTokenClassification"),gwt.forEach(t),Y0r=r(nIe," (Longformer model)"),nIe.forEach(t),K0r=i(me),HM=n(me,"LI",{});var sIe=s(HM);XFe=n(sIe,"STRONG",{});var hwt=s(XFe);Z0r=r(hwt,"mobilebert"),hwt.forEach(t),ewr=r(sIe," \u2014 "),hJ=n(sIe,"A",{href:!0});var pwt=s(hJ);owr=r(pwt,"TFMobileBertForTokenClassification"),pwt.forEach(t),rwr=r(sIe," (MobileBERT 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"),bJ=n(cIe,"A",{href:!0});var Ewt=s(bJ);Twr=r(Ewt,"TFRoFormerForTokenClassification"),Ewt.forEach(t),Mwr=r(cIe," (RoFormer model)"),cIe.forEach(t),Ewr=i(me),ZM=n(me,"LI",{});var fIe=s(ZM);UFe=n(fIe,"STRONG",{});var Cwt=s(UFe);Cwr=r(Cwt,"xlm"),Cwt.forEach(t),wwr=r(fIe," \u2014 "),vJ=n(fIe,"A",{href:!0});var wwt=s(vJ);Awr=r(wwt,"TFXLMForTokenClassification"),wwt.forEach(t),ywr=r(fIe," (XLM model)"),fIe.forEach(t),Lwr=i(me),eE=n(me,"LI",{});var mIe=s(eE);JFe=n(mIe,"STRONG",{});var Awt=s(JFe);xwr=r(Awt,"xlm-roberta"),Awt.forEach(t),$wr=r(mIe," \u2014 "),FJ=n(mIe,"A",{href:!0});var ywt=s(FJ);kwr=r(ywt,"TFXLMRobertaForTokenClassification"),ywt.forEach(t),Swr=r(mIe," (XLM-RoBERTa model)"),mIe.forEach(t),Rwr=i(me),oE=n(me,"LI",{});var gIe=s(oE);YFe=n(gIe,"STRONG",{});var Lwt=s(YFe);Pwr=r(Lwt,"xlnet"),Lwt.forEach(t),Bwr=r(gIe," \u2014 "),TJ=n(gIe,"A",{href:!0});var xwt=s(TJ);Iwr=r(xwt,"TFXLNetForTokenClassification"),xwt.forEach(t),Nwr=r(gIe," (XLNet 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with the `),MJ=n(Jee,"A",{href:!0});var Rwt=s(MJ);Vwr=r(Rwt,"from_pretrained()"),Rwt.forEach(t),Xwr=r(Jee," class method or the "),EJ=n(Jee,"A",{href:!0});var Pwt=s(EJ);zwr=r(Pwt,"from_config()"),Pwt.forEach(t),Wwr=r(Jee,` class
method.`),Jee.forEach(t),Qwr=i(Il),T9=n(Il,"P",{});var _Oe=s(T9);Hwr=r(_Oe,"This class cannot be instantiated directly using "),eTe=n(_Oe,"CODE",{});var Bwt=s(eTe);Uwr=r(Bwt,"__init__()"),Bwt.forEach(t),Jwr=r(_Oe," (throws an error)."),_Oe.forEach(t),Ywr=i(Il),qt=n(Il,"DIV",{class:!0});var rA=s(qt);T(M9.$$.fragment,rA),Kwr=i(rA),oTe=n(rA,"P",{});var Iwt=s(oTe);Zwr=r(Iwt,"Instantiates one of the model classes of the library (with a question answering head) from a configuration."),Iwt.forEach(t),eAr=i(rA),Ec=n(rA,"P",{});var Yee=s(Ec);oAr=r(Yee,`Note:
Loading a model from its configuration file does `),rTe=n(Yee,"STRONG",{});var Nwt=s(rTe);rAr=r(Nwt,"not"),Nwt.forEach(t),tAr=r(Yee,` load the model weights. It only affects the
model\u2019s configuration. Use `),CJ=n(Yee,"A",{href:!0});var qwt=s(CJ);aAr=r(qwt,"from_pretrained()"),qwt.forEach(t),nAr=r(Yee," to load the model weights."),Yee.forEach(t),sAr=i(rA),T(aE.$$.fragment,rA),rA.forEach(t),lAr=i(Il),Ir=n(Il,"DIV",{class:!0});var Nl=s(Ir);T(E9.$$.fragment,Nl),iAr=i(Nl),tTe=n(Nl,"P",{});var jwt=s(tTe);dAr=r(jwt,"Instantiate one of the model classes of the library (with a question answering head) from a pretrained model."),jwt.forEach(t),cAr=i(Nl),fn=n(Nl,"P",{});var tA=s(fn);fAr=r(tA,"The model class to instantiate is selected based on the "),aTe=n(tA,"CODE",{});var Dwt=s(aTe);mAr=r(Dwt,"model_type"),Dwt.forEach(t),gAr=r(tA,` property of the config object (either
passed as an argument or loaded from `),nTe=n(tA,"CODE",{});var Gwt=s(nTe);hAr=r(Gwt,"pretrained_model_name_or_path"),Gwt.forEach(t),pAr=r(tA,` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),sTe=n(tA,"CODE",{});var Owt=s(sTe);_Ar=r(Owt,"pretrained_model_name_or_path"),Owt.forEach(t),uAr=r(tA,":"),tA.forEach(t),bAr=i(Nl),ce=n(Nl,"UL",{});var ge=s(ce);nE=n(ge,"LI",{});var hIe=s(nE);lTe=n(hIe,"STRONG",{});var Vwt=s(lTe);vAr=r(Vwt,"albert"),Vwt.forEach(t),FAr=r(hIe," \u2014 "),wJ=n(hIe,"A",{href:!0});var Xwt=s(wJ);TAr=r(Xwt,"TFAlbertForQuestionAnswering"),Xwt.forEach(t),MAr=r(hIe," (ALBERT model)"),hIe.forEach(t),EAr=i(ge),sE=n(ge,"LI",{});var pIe=s(sE);iTe=n(pIe,"STRONG",{});var zwt=s(iTe);CAr=r(zwt,"bert"),zwt.forEach(t),wAr=r(pIe," \u2014 "),AJ=n(pIe,"A",{href:!0});var Wwt=s(AJ);AAr=r(Wwt,"TFBertForQuestionAnswering"),Wwt.forEach(t),yAr=r(pIe," (BERT model)"),pIe.forEach(t),LAr=i(ge),lE=n(ge,"LI",{});var _Ie=s(lE);dTe=n(_Ie,"STRONG",{});var Qwt=s(dTe);xAr=r(Qwt,"camembert"),Qwt.forEach(t),$Ar=r(_Ie," \u2014 "),yJ=n(_Ie,"A",{href:!0});var Hwt=s(yJ);kAr=r(Hwt,"TFCamembertForQuestionAnswering"),Hwt.forEach(t),SAr=r(_Ie," (CamemBERT model)"),_Ie.forEach(t),RAr=i(ge),iE=n(ge,"LI",{});var uIe=s(iE);cTe=n(uIe,"STRONG",{});var Uwt=s(cTe);PAr=r(Uwt,"convbert"),Uwt.forEach(t),BAr=r(uIe," \u2014 "),LJ=n(uIe,"A",{href:!0});var Jwt=s(LJ);IAr=r(Jwt,"TFConvBertForQuestionAnswering"),Jwt.forEach(t),NAr=r(uIe," (ConvBERT model)"),uIe.forEach(t),qAr=i(ge),dE=n(ge,"LI",{});var bIe=s(dE);fTe=n(bIe,"STRONG",{});var Ywt=s(fTe);jAr=r(Ywt,"deberta"),Ywt.forEach(t),DAr=r(bIe," \u2014 "),xJ=n(bIe,"A",{href:!0});var Kwt=s(xJ);GAr=r(Kwt,"TFDebertaForQuestionAnswering"),Kwt.forEach(t),OAr=r(bIe," (DeBERTa model)"),bIe.forEach(t),VAr=i(ge),cE=n(ge,"LI",{});var vIe=s(cE);mTe=n(vIe,"STRONG",{});var Zwt=s(mTe);XAr=r(Zwt,"deberta-v2"),Zwt.forEach(t),zAr=r(vIe," \u2014 "),$J=n(vIe,"A",{href:!0});var eAt=s($J);WAr=r(eAt,"TFDebertaV2ForQuestionAnswering"),eAt.forEach(t),QAr=r(vIe," (DeBERTa-v2 model)"),vIe.forEach(t),HAr=i(ge),fE=n(ge,"LI",{});var FIe=s(fE);gTe=n(FIe,"STRONG",{});var oAt=s(gTe);UAr=r(oAt,"distilbert"),oAt.forEach(t),JAr=r(FIe," \u2014 "),kJ=n(FIe,"A",{href:!0});var rAt=s(kJ);YAr=r(rAt,"TFDistilBertForQuestionAnswering"),rAt.forEach(t),KAr=r(FIe," (DistilBERT model)"),FIe.forEach(t),ZAr=i(ge),mE=n(ge,"LI",{});var TIe=s(mE);hTe=n(TIe,"STRONG",{});var tAt=s(hTe);e6r=r(tAt,"electra"),tAt.forEach(t),o6r=r(TIe," \u2014 "),SJ=n(TIe,"A",{href:!0});var aAt=s(SJ);r6r=r(aAt,"TFElectraForQuestionAnswering"),aAt.forEach(t),t6r=r(TIe," (ELECTRA model)"),TIe.forEach(t),a6r=i(ge),gE=n(ge,"LI",{});var MIe=s(gE);pTe=n(MIe,"STRONG",{});var nAt=s(pTe);n6r=r(nAt,"flaubert"),nAt.forEach(t),s6r=r(MIe," \u2014 "),RJ=n(MIe,"A",{href:!0});var sAt=s(RJ);l6r=r(sAt,"TFFlaubertForQuestionAnsweringSimple"),sAt.forEach(t),i6r=r(MIe," (FlauBERT model)"),MIe.forEach(t),d6r=i(ge),hE=n(ge,"LI",{});var EIe=s(hE);_Te=n(EIe,"STRONG",{});var lAt=s(_Te);c6r=r(lAt,"funnel"),lAt.forEach(t),f6r=r(EIe," \u2014 "),PJ=n(EIe,"A",{href:!0});var iAt=s(PJ);m6r=r(iAt,"TFFunnelForQuestionAnswering"),iAt.forEach(t),g6r=r(EIe," (Funnel Transformer model)"),EIe.forEach(t),h6r=i(ge),pE=n(ge,"LI",{});var CIe=s(pE);uTe=n(CIe,"STRONG",{});var dAt=s(uTe);p6r=r(dAt,"gptj"),dAt.forEach(t),_6r=r(CIe," \u2014 "),BJ=n(CIe,"A",{href:!0});var cAt=s(BJ);u6r=r(cAt,"TFGPTJForQuestionAnswering"),cAt.forEach(t),b6r=r(CIe," (GPT-J model)"),CIe.forEach(t),v6r=i(ge),_E=n(ge,"LI",{});var wIe=s(_E);bTe=n(wIe,"STRONG",{});var fAt=s(bTe);F6r=r(fAt,"longformer"),fAt.forEach(t),T6r=r(wIe," \u2014 "),IJ=n(wIe,"A",{href:!0});var mAt=s(IJ);M6r=r(mAt,"TFLongformerForQuestionAnswering"),mAt.forEach(t),E6r=r(wIe," (Longformer model)"),wIe.forEach(t),C6r=i(ge),uE=n(ge,"LI",{});var AIe=s(uE);vTe=n(AIe,"STRONG",{});var gAt=s(vTe);w6r=r(gAt,"mobilebert"),gAt.forEach(t),A6r=r(AIe," \u2014 "),NJ=n(AIe,"A",{href:!0});var hAt=s(NJ);y6r=r(hAt,"TFMobileBertForQuestionAnswering"),hAt.forEach(t),L6r=r(AIe," (MobileBERT model)"),AIe.forEach(t),x6r=i(ge),bE=n(ge,"LI",{});var yIe=s(bE);FTe=n(yIe,"STRONG",{});var pAt=s(FTe);$6r=r(pAt,"mpnet"),pAt.forEach(t),k6r=r(yIe," \u2014 "),qJ=n(yIe,"A",{href:!0});var _At=s(qJ);S6r=r(_At,"TFMPNetForQuestionAnswering"),_At.forEach(t),R6r=r(yIe," (MPNet model)"),yIe.forEach(t),P6r=i(ge),vE=n(ge,"LI",{});var LIe=s(vE);TTe=n(LIe,"STRONG",{});var uAt=s(TTe);B6r=r(uAt,"rembert"),uAt.forEach(t),I6r=r(LIe," \u2014 "),jJ=n(LIe,"A",{href:!0});var bAt=s(jJ);N6r=r(bAt,"TFRemBertForQuestionAnswering"),bAt.forEach(t),q6r=r(LIe," (RemBERT model)"),LIe.forEach(t),j6r=i(ge),FE=n(ge,"LI",{});var xIe=s(FE);MTe=n(xIe,"STRONG",{});var vAt=s(MTe);D6r=r(vAt,"roberta"),vAt.forEach(t),G6r=r(xIe," \u2014 "),DJ=n(xIe,"A",{href:!0});var FAt=s(DJ);O6r=r(FAt,"TFRobertaForQuestionAnswering"),FAt.forEach(t),V6r=r(xIe," (RoBERTa model)"),xIe.forEach(t),X6r=i(ge),TE=n(ge,"LI",{});var $Ie=s(TE);ETe=n($Ie,"STRONG",{});var TAt=s(ETe);z6r=r(TAt,"roformer"),TAt.forEach(t),W6r=r($Ie," \u2014 "),GJ=n($Ie,"A",{href:!0});var MAt=s(GJ);Q6r=r(MAt,"TFRoFormerForQuestionAnswering"),MAt.forEach(t),H6r=r($Ie," (RoFormer model)"),$Ie.forEach(t),U6r=i(ge),ME=n(ge,"LI",{});var kIe=s(ME);CTe=n(kIe,"STRONG",{});var EAt=s(CTe);J6r=r(EAt,"xlm"),EAt.forEach(t),Y6r=r(kIe," \u2014 "),OJ=n(kIe,"A",{href:!0});var CAt=s(OJ);K6r=r(CAt,"TFXLMForQuestionAnsweringSimple"),CAt.forEach(t),Z6r=r(kIe," (XLM model)"),kIe.forEach(t),eyr=i(ge),EE=n(ge,"LI",{});var SIe=s(EE);wTe=n(SIe,"STRONG",{});var wAt=s(wTe);oyr=r(wAt,"xlm-roberta"),wAt.forEach(t),ryr=r(SIe," \u2014 "),VJ=n(SIe,"A",{href:!0});var AAt=s(VJ);tyr=r(AAt,"TFXLMRobertaForQuestionAnswering"),AAt.forEach(t),ayr=r(SIe," (XLM-RoBERTa model)"),SIe.forEach(t),nyr=i(ge),CE=n(ge,"LI",{});var RIe=s(CE);ATe=n(RIe,"STRONG",{});var yAt=s(ATe);syr=r(yAt,"xlnet"),yAt.forEach(t),lyr=r(RIe," \u2014 "),XJ=n(RIe,"A",{href:!0});var LAt=s(XJ);iyr=r(LAt,"TFXLNetForQuestionAnsweringSimple"),LAt.forEach(t),dyr=r(RIe," (XLNet model)"),RIe.forEach(t),ge.forEach(t),cyr=i(Nl),T(wE.$$.fragment,Nl),Nl.forEach(t),Il.forEach(t),cDe=i(f),Cc=n(f,"H2",{class:!0});var uOe=s(Cc);AE=n(uOe,"A",{id:!0,class:!0,href:!0});var xAt=s(AE);yTe=n(xAt,"SPAN",{});var $At=s(yTe);T(C9.$$.fragment,$At),$At.forEach(t),xAt.forEach(t),fyr=i(uOe),LTe=n(uOe,"SPAN",{});var kAt=s(LTe);myr=r(kAt,"TFAutoModelForVision2Seq"),kAt.forEach(t),uOe.forEach(t),fDe=i(f),dr=n(f,"DIV",{class:!0});var ql=s(dr);T(w9.$$.fragment,ql),gyr=i(ql),wc=n(ql,"P",{});var Kee=s(wc);hyr=r(Kee,`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 `),zJ=n(Kee,"A",{href:!0});var SAt=s(zJ);pyr=r(SAt,"from_pretrained()"),SAt.forEach(t),_yr=r(Kee," class method or the "),WJ=n(Kee,"A",{href:!0});var RAt=s(WJ);uyr=r(RAt,"from_config()"),RAt.forEach(t),byr=r(Kee,` class
method.`),Kee.forEach(t),vyr=i(ql),A9=n(ql,"P",{});var bOe=s(A9);Fyr=r(bOe,"This class cannot be instantiated directly using "),xTe=n(bOe,"CODE",{});var PAt=s(xTe);Tyr=r(PAt,"__init__()"),PAt.forEach(t),Myr=r(bOe," (throws an error)."),bOe.forEach(t),Eyr=i(ql),jt=n(ql,"DIV",{class:!0});var aA=s(jt);T(y9.$$.fragment,aA),Cyr=i(aA),$Te=n(aA,"P",{});var BAt=s($Te);wyr=r(BAt,"Instantiates one of the model classes of the library (with a vision-to-text modeling head) from a configuration."),BAt.forEach(t),Ayr=i(aA),Ac=n(aA,"P",{});var Zee=s(Ac);yyr=r(Zee,`Note:
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model\u2019s configuration. Use `),QJ=n(Zee,"A",{href:!0});var NAt=s(QJ);$yr=r(NAt,"from_pretrained()"),NAt.forEach(t),kyr=r(Zee," to load the model weights."),Zee.forEach(t),Syr=i(aA),T(yE.$$.fragment,aA),aA.forEach(t),Ryr=i(ql),Nr=n(ql,"DIV",{class:!0});var jl=s(Nr);T(L9.$$.fragment,jl),Pyr=i(jl),STe=n(jl,"P",{});var qAt=s(STe);Byr=r(qAt,"Instantiate one of the model classes of the library (with a vision-to-text modeling head) from a pretrained model."),qAt.forEach(t),Iyr=i(jl),mn=n(jl,"P",{});var nA=s(mn);Nyr=r(nA,"The model class to instantiate is selected based on the "),RTe=n(nA,"CODE",{});var jAt=s(RTe);qyr=r(jAt,"model_type"),jAt.forEach(t),jyr=r(nA,` property of the config object (either
passed as an argument or loaded from `),PTe=n(nA,"CODE",{});var DAt=s(PTe);Dyr=r(DAt,"pretrained_model_name_or_path"),DAt.forEach(t),Gyr=r(nA,` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),BTe=n(nA,"CODE",{});var GAt=s(BTe);Oyr=r(GAt,"pretrained_model_name_or_path"),GAt.forEach(t),Vyr=r(nA,":"),nA.forEach(t),Xyr=i(jl),ITe=n(jl,"UL",{});var OAt=s(ITe);LE=n(OAt,"LI",{});var PIe=s(LE);NTe=n(PIe,"STRONG",{});var VAt=s(NTe);zyr=r(VAt,"vision-encoder-decoder"),VAt.forEach(t),Wyr=r(PIe," \u2014 "),HJ=n(PIe,"A",{href:!0});var XAt=s(HJ);Qyr=r(XAt,"TFVisionEncoderDecoderModel"),XAt.forEach(t),Hyr=r(PIe," (Vision Encoder decoder model)"),PIe.forEach(t),OAt.forEach(t),Uyr=i(jl),T(xE.$$.fragment,jl),jl.forEach(t),ql.forEach(t),mDe=i(f),yc=n(f,"H2",{class:!0});var vOe=s(yc);$E=n(vOe,"A",{id:!0,class:!0,href:!0});var zAt=s($E);qTe=n(zAt,"SPAN",{});var WAt=s(qTe);T(x9.$$.fragment,WAt),WAt.forEach(t),zAt.forEach(t),Jyr=i(vOe),jTe=n(vOe,"SPAN",{});var QAt=s(jTe);Yyr=r(QAt,"TFAutoModelForSpeechSeq2Seq"),QAt.forEach(t),vOe.forEach(t),gDe=i(f),cr=n(f,"DIV",{class:!0});var Dl=s(cr);T($9.$$.fragment,Dl),Kyr=i(Dl),Lc=n(Dl,"P",{});var eoe=s(Lc);Zyr=r(eoe,`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 `),UJ=n(eoe,"A",{href:!0});var HAt=s(UJ);eLr=r(HAt,"from_pretrained()"),HAt.forEach(t),oLr=r(eoe," class method or the "),JJ=n(eoe,"A",{href:!0});var UAt=s(JJ);rLr=r(UAt,"from_config()"),UAt.forEach(t),tLr=r(eoe,` class
method.`),eoe.forEach(t),aLr=i(Dl),k9=n(Dl,"P",{});var FOe=s(k9);nLr=r(FOe,"This class cannot be instantiated directly using "),DTe=n(FOe,"CODE",{});var JAt=s(DTe);sLr=r(JAt,"__init__()"),JAt.forEach(t),lLr=r(FOe," (throws an error)."),FOe.forEach(t),iLr=i(Dl),Dt=n(Dl,"DIV",{class:!0});var sA=s(Dt);T(S9.$$.fragment,sA),dLr=i(sA),GTe=n(sA,"P",{});var YAt=s(GTe);cLr=r(YAt,"Instantiates one of the model classes of the library (with a sequence-to-sequence speech-to-text modeling head) from a configuration."),YAt.forEach(t),fLr=i(sA),xc=n(sA,"P",{});var ooe=s(xc);mLr=r(ooe,`Note:
Loading a model from its configuration file does `),OTe=n(ooe,"STRONG",{});var KAt=s(OTe);gLr=r(KAt,"not"),KAt.forEach(t),hLr=r(ooe,` load the model weights. It only affects the
model\u2019s configuration. Use `),YJ=n(ooe,"A",{href:!0});var ZAt=s(YJ);pLr=r(ZAt,"from_pretrained()"),ZAt.forEach(t),_Lr=r(ooe," to load the model weights."),ooe.forEach(t),uLr=i(sA),T(kE.$$.fragment,sA),sA.forEach(t),bLr=i(Dl),qr=n(Dl,"DIV",{class:!0});var Gl=s(qr);T(R9.$$.fragment,Gl),vLr=i(Gl),VTe=n(Gl,"P",{});var e6t=s(VTe);FLr=r(e6t,"Instantiate one of the model classes of the library (with a sequence-to-sequence speech-to-text modeling head) from a pretrained model."),e6t.forEach(t),TLr=i(Gl),gn=n(Gl,"P",{});var lA=s(gn);MLr=r(lA,"The model class to instantiate is selected based on the "),XTe=n(lA,"CODE",{});var o6t=s(XTe);ELr=r(o6t,"model_type"),o6t.forEach(t),CLr=r(lA,` property of the config object (either
passed as an argument or loaded from `),zTe=n(lA,"CODE",{});var r6t=s(zTe);wLr=r(r6t,"pretrained_model_name_or_path"),r6t.forEach(t),ALr=r(lA,` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),WTe=n(lA,"CODE",{});var t6t=s(WTe);yLr=r(t6t,"pretrained_model_name_or_path"),t6t.forEach(t),LLr=r(lA,":"),lA.forEach(t),xLr=i(Gl),QTe=n(Gl,"UL",{});var a6t=s(QTe);SE=n(a6t,"LI",{});var BIe=s(SE);HTe=n(BIe,"STRONG",{});var n6t=s(HTe);$Lr=r(n6t,"speech_to_text"),n6t.forEach(t),kLr=r(BIe," \u2014 "),KJ=n(BIe,"A",{href:!0});var s6t=s(KJ);SLr=r(s6t,"TFSpeech2TextForConditionalGeneration"),s6t.forEach(t),RLr=r(BIe," (Speech2Text model)"),BIe.forEach(t),a6t.forEach(t),PLr=i(Gl),T(RE.$$.fragment,Gl),Gl.forEach(t),Dl.forEach(t),hDe=i(f),$c=n(f,"H2",{class:!0});var TOe=s($c);PE=n(TOe,"A",{id:!0,class:!0,href:!0});var l6t=s(PE);UTe=n(l6t,"SPAN",{});var i6t=s(UTe);T(P9.$$.fragment,i6t),i6t.forEach(t),l6t.forEach(t),BLr=i(TOe),JTe=n(TOe,"SPAN",{});var d6t=s(JTe);ILr=r(d6t,"FlaxAutoModel"),d6t.forEach(t),TOe.forEach(t),pDe=i(f),fr=n(f,"DIV",{class:!0});var Ol=s(fr);T(B9.$$.fragment,Ol),NLr=i(Ol),kc=n(Ol,"P",{});var roe=s(kc);qLr=r(roe,`This is a generic model class that will be instantiated as one of the base model classes of the library when created
with the `),ZJ=n(roe,"A",{href:!0});var c6t=s(ZJ);jLr=r(c6t,"from_pretrained()"),c6t.forEach(t),DLr=r(roe," class method or the "),eY=n(roe,"A",{href:!0});var f6t=s(eY);GLr=r(f6t,"from_config()"),f6t.forEach(t),OLr=r(roe,` class
method.`),roe.forEach(t),VLr=i(Ol),I9=n(Ol,"P",{});var MOe=s(I9);XLr=r(MOe,"This class cannot be instantiated directly using "),YTe=n(MOe,"CODE",{});var m6t=s(YTe);zLr=r(m6t,"__init__()"),m6t.forEach(t),WLr=r(MOe," (throws an error)."),MOe.forEach(t),QLr=i(Ol),Gt=n(Ol,"DIV",{class:!0});var iA=s(Gt);T(N9.$$.fragment,iA),HLr=i(iA),KTe=n(iA,"P",{});var g6t=s(KTe);ULr=r(g6t,"Instantiates one of the base model classes of the library from a configuration."),g6t.forEach(t),JLr=i(iA),Sc=n(iA,"P",{});var toe=s(Sc);YLr=r(toe,`Note:
Loading a model from its configuration file does `),ZTe=n(toe,"STRONG",{});var h6t=s(ZTe);KLr=r(h6t,"not"),h6t.forEach(t),ZLr=r(toe,` load the model weights. It only affects the
model\u2019s configuration. Use `),oY=n(toe,"A",{href:!0});var p6t=s(oY);e9r=r(p6t,"from_pretrained()"),p6t.forEach(t),o9r=r(toe," to load the model weights."),toe.forEach(t),r9r=i(iA),T(BE.$$.fragment,iA),iA.forEach(t),t9r=i(Ol),jr=n(Ol,"DIV",{class:!0});var Vl=s(jr);T(q9.$$.fragment,Vl),a9r=i(Vl),e8e=n(Vl,"P",{});var _6t=s(e8e);n9r=r(_6t,"Instantiate one of the base model classes of the library from a pretrained model."),_6t.forEach(t),s9r=i(Vl),hn=n(Vl,"P",{});var dA=s(hn);l9r=r(dA,"The model class to instantiate is selected based on the "),o8e=n(dA,"CODE",{});var u6t=s(o8e);i9r=r(u6t,"model_type"),u6t.forEach(t),d9r=r(dA,` property of the config object (either
passed as an argument or loaded from `),r8e=n(dA,"CODE",{});var b6t=s(r8e);c9r=r(b6t,"pretrained_model_name_or_path"),b6t.forEach(t),f9r=r(dA,` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),t8e=n(dA,"CODE",{});var v6t=s(t8e);m9r=r(v6t,"pretrained_model_name_or_path"),v6t.forEach(t),g9r=r(dA,":"),dA.forEach(t),h9r=i(Vl),re=n(Vl,"UL",{});var ne=s(re);IE=n(ne,"LI",{});var IIe=s(IE);a8e=n(IIe,"STRONG",{});var F6t=s(a8e);p9r=r(F6t,"albert"),F6t.forEach(t),_9r=r(IIe," \u2014 "),rY=n(IIe,"A",{href:!0});var T6t=s(rY);u9r=r(T6t,"FlaxAlbertModel"),T6t.forEach(t),b9r=r(IIe," (ALBERT model)"),IIe.forEach(t),v9r=i(ne),NE=n(ne,"LI",{});var NIe=s(NE);n8e=n(NIe,"STRONG",{});var M6t=s(n8e);F9r=r(M6t,"bart"),M6t.forEach(t),T9r=r(NIe," \u2014 "),tY=n(NIe,"A",{href:!0});var E6t=s(tY);M9r=r(E6t,"FlaxBartModel"),E6t.forEach(t),E9r=r(NIe," (BART model)"),NIe.forEach(t),C9r=i(ne),qE=n(ne,"LI",{});var qIe=s(qE);s8e=n(qIe,"STRONG",{});var C6t=s(s8e);w9r=r(C6t,"beit"),C6t.forEach(t),A9r=r(qIe," \u2014 "),aY=n(qIe,"A",{href:!0});var w6t=s(aY);y9r=r(w6t,"FlaxBeitModel"),w6t.forEach(t),L9r=r(qIe," (BEiT model)"),qIe.forEach(t),x9r=i(ne),jE=n(ne,"LI",{});var jIe=s(jE);l8e=n(jIe,"STRONG",{});var A6t=s(l8e);$9r=r(A6t,"bert"),A6t.forEach(t),k9r=r(jIe," \u2014 "),nY=n(jIe,"A",{href:!0});var y6t=s(nY);S9r=r(y6t,"FlaxBertModel"),y6t.forEach(t),R9r=r(jIe," (BERT model)"),jIe.forEach(t),P9r=i(ne),DE=n(ne,"LI",{});var DIe=s(DE);i8e=n(DIe,"STRONG",{});var L6t=s(i8e);B9r=r(L6t,"big_bird"),L6t.forEach(t),I9r=r(DIe," \u2014 "),sY=n(DIe,"A",{href:!0});var x6t=s(sY);N9r=r(x6t,"FlaxBigBirdModel"),x6t.forEach(t),q9r=r(DIe," (BigBird model)"),DIe.forEach(t),j9r=i(ne),GE=n(ne,"LI",{});var GIe=s(GE);d8e=n(GIe,"STRONG",{});var $6t=s(d8e);D9r=r($6t,"blenderbot"),$6t.forEach(t),G9r=r(GIe," \u2014 "),lY=n(GIe,"A",{href:!0});var k6t=s(lY);O9r=r(k6t,"FlaxBlenderbotModel"),k6t.forEach(t),V9r=r(GIe," (Blenderbot model)"),GIe.forEach(t),X9r=i(ne),OE=n(ne,"LI",{});var OIe=s(OE);c8e=n(OIe,"STRONG",{});var S6t=s(c8e);z9r=r(S6t,"blenderbot-small"),S6t.forEach(t),W9r=r(OIe," \u2014 "),iY=n(OIe,"A",{href:!0});var R6t=s(iY);Q9r=r(R6t,"FlaxBlenderbotSmallModel"),R6t.forEach(t),H9r=r(OIe," (BlenderbotSmall model)"),OIe.forEach(t),U9r=i(ne),VE=n(ne,"LI",{});var VIe=s(VE);f8e=n(VIe,"STRONG",{});var P6t=s(f8e);J9r=r(P6t,"clip"),P6t.forEach(t),Y9r=r(VIe," \u2014 "),dY=n(VIe,"A",{href:!0});var B6t=s(dY);K9r=r(B6t,"FlaxCLIPModel"),B6t.forEach(t),Z9r=r(VIe," (CLIP model)"),VIe.forEach(t),exr=i(ne),XE=n(ne,"LI",{});var XIe=s(XE);m8e=n(XIe,"STRONG",{});var I6t=s(m8e);oxr=r(I6t,"distilbert"),I6t.forEach(t),rxr=r(XIe," \u2014 "),cY=n(XIe,"A",{href:!0});var N6t=s(cY);txr=r(N6t,"FlaxDistilBertModel"),N6t.forEach(t),axr=r(XIe," (DistilBERT model)"),XIe.forEach(t),nxr=i(ne),zE=n(ne,"LI",{});var zIe=s(zE);g8e=n(zIe,"STRONG",{});var q6t=s(g8e);sxr=r(q6t,"electra"),q6t.forEach(t),lxr=r(zIe," \u2014 "),fY=n(zIe,"A",{href:!0});var j6t=s(fY);ixr=r(j6t,"FlaxElectraModel"),j6t.forEach(t),dxr=r(zIe," (ELECTRA model)"),zIe.forEach(t),cxr=i(ne),WE=n(ne,"LI",{});var WIe=s(WE);h8e=n(WIe,"STRONG",{});var D6t=s(h8e);fxr=r(D6t,"gpt2"),D6t.forEach(t),mxr=r(WIe," \u2014 "),mY=n(WIe,"A",{href:!0});var G6t=s(mY);gxr=r(G6t,"FlaxGPT2Model"),G6t.forEach(t),hxr=r(WIe," (OpenAI GPT-2 model)"),WIe.forEach(t),pxr=i(ne),QE=n(ne,"LI",{});var QIe=s(QE);p8e=n(QIe,"STRONG",{});var O6t=s(p8e);_xr=r(O6t,"gpt_neo"),O6t.forEach(t),uxr=r(QIe," \u2014 "),gY=n(QIe,"A",{href:!0});var V6t=s(gY);bxr=r(V6t,"FlaxGPTNeoModel"),V6t.forEach(t),vxr=r(QIe," (GPT Neo model)"),QIe.forEach(t),Fxr=i(ne),HE=n(ne,"LI",{});var HIe=s(HE);_8e=n(HIe,"STRONG",{});var X6t=s(_8e);Txr=r(X6t,"gptj"),X6t.forEach(t),Mxr=r(HIe," \u2014 "),hY=n(HIe,"A",{href:!0});var z6t=s(hY);Exr=r(z6t,"FlaxGPTJModel"),z6t.forEach(t),Cxr=r(HIe," (GPT-J model)"),HIe.forEach(t),wxr=i(ne),UE=n(ne,"LI",{});var UIe=s(UE);u8e=n(UIe,"STRONG",{});var W6t=s(u8e);Axr=r(W6t,"marian"),W6t.forEach(t),yxr=r(UIe," \u2014 "),pY=n(UIe,"A",{href:!0});var Q6t=s(pY);Lxr=r(Q6t,"FlaxMarianModel"),Q6t.forEach(t),xxr=r(UIe," (Marian model)"),UIe.forEach(t),$xr=i(ne),JE=n(ne,"LI",{});var JIe=s(JE);b8e=n(JIe,"STRONG",{});var H6t=s(b8e);kxr=r(H6t,"mbart"),H6t.forEach(t),Sxr=r(JIe," \u2014 "),_Y=n(JIe,"A",{href:!0});var U6t=s(_Y);Rxr=r(U6t,"FlaxMBartModel"),U6t.forEach(t),Pxr=r(JIe," (mBART model)"),JIe.forEach(t),Bxr=i(ne),YE=n(ne,"LI",{});var YIe=s(YE);v8e=n(YIe,"STRONG",{});var J6t=s(v8e);Ixr=r(J6t,"mt5"),J6t.forEach(t),Nxr=r(YIe," \u2014 "),uY=n(YIe,"A",{href:!0});var Y6t=s(uY);qxr=r(Y6t,"FlaxMT5Model"),Y6t.forEach(t),jxr=r(YIe," (MT5 model)"),YIe.forEach(t),Dxr=i(ne),KE=n(ne,"LI",{});var KIe=s(KE);F8e=n(KIe,"STRONG",{});var K6t=s(F8e);Gxr=r(K6t,"opt"),K6t.forEach(t),Oxr=r(KIe," \u2014 "),bY=n(KIe,"A",{href:!0});var Z6t=s(bY);Vxr=r(Z6t,"FlaxOPTModel"),Z6t.forEach(t),Xxr=r(KIe," (OPT model)"),KIe.forEach(t),zxr=i(ne),ZE=n(ne,"LI",{});var ZIe=s(ZE);T8e=n(ZIe,"STRONG",{});var eyt=s(T8e);Wxr=r(eyt,"pegasus"),eyt.forEach(t),Qxr=r(ZIe," \u2014 "),vY=n(ZIe,"A",{href:!0});var oyt=s(vY);Hxr=r(oyt,"FlaxPegasusModel"),oyt.forEach(t),Uxr=r(ZIe," (Pegasus model)"),ZIe.forEach(t),Jxr=i(ne),eC=n(ne,"LI",{});var eNe=s(eC);M8e=n(eNe,"STRONG",{});var ryt=s(M8e);Yxr=r(ryt,"roberta"),ryt.forEach(t),Kxr=r(eNe," \u2014 "),FY=n(eNe,"A",{href:!0});var tyt=s(FY);Zxr=r(tyt,"FlaxRobertaModel"),tyt.forEach(t),e$r=r(eNe," (RoBERTa model)"),eNe.forEach(t),o$r=i(ne),oC=n(ne,"LI",{});var oNe=s(oC);E8e=n(oNe,"STRONG",{});var ayt=s(E8e);r$r=r(ayt,"roformer"),ayt.forEach(t),t$r=r(oNe," \u2014 "),TY=n(oNe,"A",{href:!0});var nyt=s(TY);a$r=r(nyt,"FlaxRoFormerModel"),nyt.forEach(t),n$r=r(oNe," (RoFormer model)"),oNe.forEach(t),s$r=i(ne),rC=n(ne,"LI",{});var rNe=s(rC);C8e=n(rNe,"STRONG",{});var syt=s(C8e);l$r=r(syt,"t5"),syt.forEach(t),i$r=r(rNe," \u2014 "),MY=n(rNe,"A",{href:!0});var lyt=s(MY);d$r=r(lyt,"FlaxT5Model"),lyt.forEach(t),c$r=r(rNe," (T5 model)"),rNe.forEach(t),f$r=i(ne),tC=n(ne,"LI",{});var tNe=s(tC);w8e=n(tNe,"STRONG",{});var iyt=s(w8e);m$r=r(iyt,"vision-text-dual-encoder"),iyt.forEach(t),g$r=r(tNe," \u2014 "),EY=n(tNe,"A",{href:!0});var dyt=s(EY);h$r=r(dyt,"FlaxVisionTextDualEncoderModel"),dyt.forEach(t),p$r=r(tNe," (VisionTextDualEncoder model)"),tNe.forEach(t),_$r=i(ne),aC=n(ne,"LI",{});var aNe=s(aC);A8e=n(aNe,"STRONG",{});var cyt=s(A8e);u$r=r(cyt,"vit"),cyt.forEach(t),b$r=r(aNe," \u2014 "),CY=n(aNe,"A",{href:!0});var fyt=s(CY);v$r=r(fyt,"FlaxViTModel"),fyt.forEach(t),F$r=r(aNe," (ViT model)"),aNe.forEach(t),T$r=i(ne),nC=n(ne,"LI",{});var nNe=s(nC);y8e=n(nNe,"STRONG",{});var myt=s(y8e);M$r=r(myt,"wav2vec2"),myt.forEach(t),E$r=r(nNe," \u2014 "),wY=n(nNe,"A",{href:!0});var gyt=s(wY);C$r=r(gyt,"FlaxWav2Vec2Model"),gyt.forEach(t),w$r=r(nNe," (Wav2Vec2 model)"),nNe.forEach(t),A$r=i(ne),sC=n(ne,"LI",{});var sNe=s(sC);L8e=n(sNe,"STRONG",{});var hyt=s(L8e);y$r=r(hyt,"xglm"),hyt.forEach(t),L$r=r(sNe," \u2014 "),AY=n(sNe,"A",{href:!0});var pyt=s(AY);x$r=r(pyt,"FlaxXGLMModel"),pyt.forEach(t),$$r=r(sNe," (XGLM model)"),sNe.forEach(t),k$r=i(ne),lC=n(ne,"LI",{});var lNe=s(lC);x8e=n(lNe,"STRONG",{});var _yt=s(x8e);S$r=r(_yt,"xlm-roberta"),_yt.forEach(t),R$r=r(lNe," \u2014 "),yY=n(lNe,"A",{href:!0});var uyt=s(yY);P$r=r(uyt,"FlaxXLMRobertaModel"),uyt.forEach(t),B$r=r(lNe," (XLM-RoBERTa model)"),lNe.forEach(t),ne.forEach(t),I$r=i(Vl),T(iC.$$.fragment,Vl),Vl.forEach(t),Ol.forEach(t),_De=i(f),Rc=n(f,"H2",{class:!0});var EOe=s(Rc);dC=n(EOe,"A",{id:!0,class:!0,href:!0});var byt=s(dC);$8e=n(byt,"SPAN",{});var vyt=s($8e);T(j9.$$.fragment,vyt),vyt.forEach(t),byt.forEach(t),N$r=i(EOe),k8e=n(EOe,"SPAN",{});var Fyt=s(k8e);q$r=r(Fyt,"FlaxAutoModelForCausalLM"),Fyt.forEach(t),EOe.forEach(t),uDe=i(f),mr=n(f,"DIV",{class:!0});var Xl=s(mr);T(D9.$$.fragment,Xl),j$r=i(Xl),Pc=n(Xl,"P",{});var aoe=s(Pc);D$r=r(aoe,`This is a generic model class that will be instantiated as one of the model classes of the library (with a causal language modeling head) when created
with the `),LY=n(aoe,"A",{href:!0});var Tyt=s(LY);G$r=r(Tyt,"from_pretrained()"),Tyt.forEach(t),O$r=r(aoe," class method or the "),xY=n(aoe,"A",{href:!0});var Myt=s(xY);V$r=r(Myt,"from_config()"),Myt.forEach(t),X$r=r(aoe,` class
method.`),aoe.forEach(t),z$r=i(Xl),G9=n(Xl,"P",{});var COe=s(G9);W$r=r(COe,"This class cannot be instantiated directly using "),S8e=n(COe,"CODE",{});var Eyt=s(S8e);Q$r=r(Eyt,"__init__()"),Eyt.forEach(t),H$r=r(COe," (throws an error)."),COe.forEach(t),U$r=i(Xl),Ot=n(Xl,"DIV",{class:!0});var cA=s(Ot);T(O9.$$.fragment,cA),J$r=i(cA),R8e=n(cA,"P",{});var Cyt=s(R8e);Y$r=r(Cyt,"Instantiates one of the model classes of the library (with a causal language modeling head) from a configuration."),Cyt.forEach(t),K$r=i(cA),Bc=n(cA,"P",{});var noe=s(Bc);Z$r=r(noe,`Note:
Loading a model from its configuration file does `),P8e=n(noe,"STRONG",{});var wyt=s(P8e);ekr=r(wyt,"not"),wyt.forEach(t),okr=r(noe,` load the model weights. It only affects the
model\u2019s configuration. Use `),$Y=n(noe,"A",{href:!0});var Ayt=s($Y);rkr=r(Ayt,"from_pretrained()"),Ayt.forEach(t),tkr=r(noe," to load the model weights."),noe.forEach(t),akr=i(cA),T(cC.$$.fragment,cA),cA.forEach(t),nkr=i(Xl),Dr=n(Xl,"DIV",{class:!0});var zl=s(Dr);T(V9.$$.fragment,zl),skr=i(zl),B8e=n(zl,"P",{});var yyt=s(B8e);lkr=r(yyt,"Instantiate one of the model classes of the library (with a causal language modeling head) from a pretrained model."),yyt.forEach(t),ikr=i(zl),pn=n(zl,"P",{});var fA=s(pn);dkr=r(fA,"The model class to instantiate is selected based on the "),I8e=n(fA,"CODE",{});var Lyt=s(I8e);ckr=r(Lyt,"model_type"),Lyt.forEach(t),fkr=r(fA,` property of the config object (either
passed as an argument or loaded from `),N8e=n(fA,"CODE",{});var xyt=s(N8e);mkr=r(xyt,"pretrained_model_name_or_path"),xyt.forEach(t),gkr=r(fA,` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),q8e=n(fA,"CODE",{});var $yt=s(q8e);hkr=r($yt,"pretrained_model_name_or_path"),$yt.forEach(t),pkr=r(fA,":"),fA.forEach(t),_kr=i(zl),Le=n(zl,"UL",{});var Ie=s(Le);fC=n(Ie,"LI",{});var iNe=s(fC);j8e=n(iNe,"STRONG",{});var kyt=s(j8e);ukr=r(kyt,"bart"),kyt.forEach(t),bkr=r(iNe," \u2014 "),kY=n(iNe,"A",{href:!0});var Syt=s(kY);vkr=r(Syt,"FlaxBartForCausalLM"),Syt.forEach(t),Fkr=r(iNe," (BART model)"),iNe.forEach(t),Tkr=i(Ie),mC=n(Ie,"LI",{});var dNe=s(mC);D8e=n(dNe,"STRONG",{});var Ryt=s(D8e);Mkr=r(Ryt,"bert"),Ryt.forEach(t),Ekr=r(dNe," \u2014 "),SY=n(dNe,"A",{href:!0});var Pyt=s(SY);Ckr=r(Pyt,"FlaxBertForCausalLM"),Pyt.forEach(t),wkr=r(dNe," (BERT model)"),dNe.forEach(t),Akr=i(Ie),gC=n(Ie,"LI",{});var cNe=s(gC);G8e=n(cNe,"STRONG",{});var Byt=s(G8e);ykr=r(Byt,"big_bird"),Byt.forEach(t),Lkr=r(cNe," \u2014 "),RY=n(cNe,"A",{href:!0});var Iyt=s(RY);xkr=r(Iyt,"FlaxBigBirdForCausalLM"),Iyt.forEach(t),$kr=r(cNe," (BigBird model)"),cNe.forEach(t),kkr=i(Ie),hC=n(Ie,"LI",{});var fNe=s(hC);O8e=n(fNe,"STRONG",{});var Nyt=s(O8e);Skr=r(Nyt,"electra"),Nyt.forEach(t),Rkr=r(fNe," \u2014 "),PY=n(fNe,"A",{href:!0});var qyt=s(PY);Pkr=r(qyt,"FlaxElectraForCausalLM"),qyt.forEach(t),Bkr=r(fNe," (ELECTRA model)"),fNe.forEach(t),Ikr=i(Ie),pC=n(Ie,"LI",{});var mNe=s(pC);V8e=n(mNe,"STRONG",{});var jyt=s(V8e);Nkr=r(jyt,"gpt2"),jyt.forEach(t),qkr=r(mNe," \u2014 "),BY=n(mNe,"A",{href:!0});var Dyt=s(BY);jkr=r(Dyt,"FlaxGPT2LMHeadModel"),Dyt.forEach(t),Dkr=r(mNe," (OpenAI GPT-2 model)"),mNe.forEach(t),Gkr=i(Ie),_C=n(Ie,"LI",{});var gNe=s(_C);X8e=n(gNe,"STRONG",{});var Gyt=s(X8e);Okr=r(Gyt,"gpt_neo"),Gyt.forEach(t),Vkr=r(gNe," \u2014 "),IY=n(gNe,"A",{href:!0});var Oyt=s(IY);Xkr=r(Oyt,"FlaxGPTNeoForCausalLM"),Oyt.forEach(t),zkr=r(gNe," (GPT Neo model)"),gNe.forEach(t),Wkr=i(Ie),uC=n(Ie,"LI",{});var hNe=s(uC);z8e=n(hNe,"STRONG",{});var Vyt=s(z8e);Qkr=r(Vyt,"gptj"),Vyt.forEach(t),Hkr=r(hNe," \u2014 "),NY=n(hNe,"A",{href:!0});var Xyt=s(NY);Ukr=r(Xyt,"FlaxGPTJForCausalLM"),Xyt.forEach(t),Jkr=r(hNe," (GPT-J model)"),hNe.forEach(t),Ykr=i(Ie),bC=n(Ie,"LI",{});var pNe=s(bC);W8e=n(pNe,"STRONG",{});var zyt=s(W8e);Kkr=r(zyt,"opt"),zyt.forEach(t),Zkr=r(pNe," \u2014 "),qY=n(pNe,"A",{href:!0});var Wyt=s(qY);eSr=r(Wyt,"FlaxOPTForCausalLM"),Wyt.forEach(t),oSr=r(pNe," (OPT model)"),pNe.forEach(t),rSr=i(Ie),vC=n(Ie,"LI",{});var _Ne=s(vC);Q8e=n(_Ne,"STRONG",{});var Qyt=s(Q8e);tSr=r(Qyt,"roberta"),Qyt.forEach(t),aSr=r(_Ne," \u2014 "),jY=n(_Ne,"A",{href:!0});var Hyt=s(jY);nSr=r(Hyt,"FlaxRobertaForCausalLM"),Hyt.forEach(t),sSr=r(_Ne," (RoBERTa model)"),_Ne.forEach(t),lSr=i(Ie),FC=n(Ie,"LI",{});var uNe=s(FC);H8e=n(uNe,"STRONG",{});var Uyt=s(H8e);iSr=r(Uyt,"xglm"),Uyt.forEach(t),dSr=r(uNe," \u2014 "),DY=n(uNe,"A",{href:!0});var Jyt=s(DY);cSr=r(Jyt,"FlaxXGLMForCausalLM"),Jyt.forEach(t),fSr=r(uNe," (XGLM model)"),uNe.forEach(t),Ie.forEach(t),mSr=i(zl),T(TC.$$.fragment,zl),zl.forEach(t),Xl.forEach(t),bDe=i(f),Ic=n(f,"H2",{class:!0});var wOe=s(Ic);MC=n(wOe,"A",{id:!0,class:!0,href:!0});var Yyt=s(MC);U8e=n(Yyt,"SPAN",{});var Kyt=s(U8e);T(X9.$$.fragment,Kyt),Kyt.forEach(t),Yyt.forEach(t),gSr=i(wOe),J8e=n(wOe,"SPAN",{});var Zyt=s(J8e);hSr=r(Zyt,"FlaxAutoModelForPreTraining"),Zyt.forEach(t),wOe.forEach(t),vDe=i(f),gr=n(f,"DIV",{class:!0});var Wl=s(gr);T(z9.$$.fragment,Wl),pSr=i(Wl),Nc=n(Wl,"P",{});var soe=s(Nc);_Sr=r(soe,`This is a generic model class that will be instantiated as one of the model classes of the library (with a pretraining head) when created
with the `),GY=n(soe,"A",{href:!0});var eLt=s(GY);uSr=r(eLt,"from_pretrained()"),eLt.forEach(t),bSr=r(soe," class method or the "),OY=n(soe,"A",{href:!0});var oLt=s(OY);vSr=r(oLt,"from_config()"),oLt.forEach(t),FSr=r(soe,` class
method.`),soe.forEach(t),TSr=i(Wl),W9=n(Wl,"P",{});var AOe=s(W9);MSr=r(AOe,"This class cannot be instantiated directly using "),Y8e=n(AOe,"CODE",{});var rLt=s(Y8e);ESr=r(rLt,"__init__()"),rLt.forEach(t),CSr=r(AOe," (throws an error)."),AOe.forEach(t),wSr=i(Wl),Vt=n(Wl,"DIV",{class:!0});var mA=s(Vt);T(Q9.$$.fragment,mA),ASr=i(mA),K8e=n(mA,"P",{});var tLt=s(K8e);ySr=r(tLt,"Instantiates one of the model classes of the library (with a pretraining head) from a configuration."),tLt.forEach(t),LSr=i(mA),qc=n(mA,"P",{});var loe=s(qc);xSr=r(loe,`Note:
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falling back to using pattern matching on `),t7e=n(gA,"CODE",{});var dLt=s(t7e);XSr=r(dLt,"pretrained_model_name_or_path"),dLt.forEach(t),zSr=r(gA,":"),gA.forEach(t),WSr=i(Ql),Ee=n(Ql,"UL",{});var we=s(Ee);CC=n(we,"LI",{});var bNe=s(CC);a7e=n(bNe,"STRONG",{});var cLt=s(a7e);QSr=r(cLt,"albert"),cLt.forEach(t),HSr=r(bNe," \u2014 "),XY=n(bNe,"A",{href:!0});var fLt=s(XY);USr=r(fLt,"FlaxAlbertForPreTraining"),fLt.forEach(t),JSr=r(bNe," (ALBERT model)"),bNe.forEach(t),YSr=i(we),wC=n(we,"LI",{});var vNe=s(wC);n7e=n(vNe,"STRONG",{});var mLt=s(n7e);KSr=r(mLt,"bart"),mLt.forEach(t),ZSr=r(vNe," \u2014 "),zY=n(vNe,"A",{href:!0});var gLt=s(zY);eRr=r(gLt,"FlaxBartForConditionalGeneration"),gLt.forEach(t),oRr=r(vNe," (BART model)"),vNe.forEach(t),rRr=i(we),AC=n(we,"LI",{});var FNe=s(AC);s7e=n(FNe,"STRONG",{});var hLt=s(s7e);tRr=r(hLt,"bert"),hLt.forEach(t),aRr=r(FNe," \u2014 "),WY=n(FNe,"A",{href:!0});var pLt=s(WY);nRr=r(pLt,"FlaxBertForPreTraining"),pLt.forEach(t),sRr=r(FNe," (BERT model)"),FNe.forEach(t),lRr=i(we),yC=n(we,"LI",{});var TNe=s(yC);l7e=n(TNe,"STRONG",{});var _Lt=s(l7e);iRr=r(_Lt,"big_bird"),_Lt.forEach(t),dRr=r(TNe," \u2014 "),QY=n(TNe,"A",{href:!0});var uLt=s(QY);cRr=r(uLt,"FlaxBigBirdForPreTraining"),uLt.forEach(t),fRr=r(TNe," (BigBird model)"),TNe.forEach(t),mRr=i(we),LC=n(we,"LI",{});var MNe=s(LC);i7e=n(MNe,"STRONG",{});var bLt=s(i7e);gRr=r(bLt,"electra"),bLt.forEach(t),hRr=r(MNe," \u2014 "),HY=n(MNe,"A",{href:!0});var vLt=s(HY);pRr=r(vLt,"FlaxElectraForPreTraining"),vLt.forEach(t),_Rr=r(MNe," (ELECTRA model)"),MNe.forEach(t),uRr=i(we),xC=n(we,"LI",{});var ENe=s(xC);d7e=n(ENe,"STRONG",{});var FLt=s(d7e);bRr=r(FLt,"mbart"),FLt.forEach(t),vRr=r(ENe," \u2014 "),UY=n(ENe,"A",{href:!0});var TLt=s(UY);FRr=r(TLt,"FlaxMBartForConditionalGeneration"),TLt.forEach(t),TRr=r(ENe," (mBART model)"),ENe.forEach(t),MRr=i(we),$C=n(we,"LI",{});var CNe=s($C);c7e=n(CNe,"STRONG",{});var MLt=s(c7e);ERr=r(MLt,"mt5"),MLt.forEach(t),CRr=r(CNe," \u2014 "),JY=n(CNe,"A",{href:!0});var ELt=s(JY);wRr=r(ELt,"FlaxMT5ForConditionalGeneration"),ELt.forEach(t),ARr=r(CNe," (MT5 model)"),CNe.forEach(t),yRr=i(we),kC=n(we,"LI",{});var wNe=s(kC);f7e=n(wNe,"STRONG",{});var CLt=s(f7e);LRr=r(CLt,"roberta"),CLt.forEach(t),xRr=r(wNe," \u2014 "),YY=n(wNe,"A",{href:!0});var wLt=s(YY);$Rr=r(wLt,"FlaxRobertaForMaskedLM"),wLt.forEach(t),kRr=r(wNe," (RoBERTa model)"),wNe.forEach(t),SRr=i(we),SC=n(we,"LI",{});var ANe=s(SC);m7e=n(ANe,"STRONG",{});var ALt=s(m7e);RRr=r(ALt,"roformer"),ALt.forEach(t),PRr=r(ANe," \u2014 "),KY=n(ANe,"A",{href:!0});var yLt=s(KY);BRr=r(yLt,"FlaxRoFormerForMaskedLM"),yLt.forEach(t),IRr=r(ANe," (RoFormer model)"),ANe.forEach(t),NRr=i(we),RC=n(we,"LI",{});var yNe=s(RC);g7e=n(yNe,"STRONG",{});var LLt=s(g7e);qRr=r(LLt,"t5"),LLt.forEach(t),jRr=r(yNe," \u2014 "),ZY=n(yNe,"A",{href:!0});var xLt=s(ZY);DRr=r(xLt,"FlaxT5ForConditionalGeneration"),xLt.forEach(t),GRr=r(yNe," (T5 model)"),yNe.forEach(t),ORr=i(we),PC=n(we,"LI",{});var LNe=s(PC);h7e=n(LNe,"STRONG",{});var $Lt=s(h7e);VRr=r($Lt,"wav2vec2"),$Lt.forEach(t),XRr=r(LNe," \u2014 "),eK=n(LNe,"A",{href:!0});var kLt=s(eK);zRr=r(kLt,"FlaxWav2Vec2ForPreTraining"),kLt.forEach(t),WRr=r(LNe," (Wav2Vec2 model)"),LNe.forEach(t),QRr=i(we),BC=n(we,"LI",{});var xNe=s(BC);p7e=n(xNe,"STRONG",{});var SLt=s(p7e);HRr=r(SLt,"xlm-roberta"),SLt.forEach(t),URr=r(xNe," \u2014 "),oK=n(xNe,"A",{href:!0});var RLt=s(oK);JRr=r(RLt,"FlaxXLMRobertaForMaskedLM"),RLt.forEach(t),YRr=r(xNe," (XLM-RoBERTa model)"),xNe.forEach(t),we.forEach(t),KRr=i(Ql),T(IC.$$.fragment,Ql),Ql.forEach(t),Wl.forEach(t),FDe=i(f),jc=n(f,"H2",{class:!0});var yOe=s(jc);NC=n(yOe,"A",{id:!0,class:!0,href:!0});var PLt=s(NC);_7e=n(PLt,"SPAN",{});var BLt=s(_7e);T(U9.$$.fragment,BLt),BLt.forEach(t),PLt.forEach(t),ZRr=i(yOe),u7e=n(yOe,"SPAN",{});var ILt=s(u7e);ePr=r(ILt,"FlaxAutoModelForMaskedLM"),ILt.forEach(t),yOe.forEach(t),TDe=i(f),hr=n(f,"DIV",{class:!0});var Hl=s(hr);T(J9.$$.fragment,Hl),oPr=i(Hl),Dc=n(Hl,"P",{});var ioe=s(Dc);rPr=r(ioe,`This is a generic model class that will be instantiated as one of the model classes of the library (with a masked language modeling head) when created
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model\u2019s configuration. Use `),OK=n(poe,"A",{href:!0});var kxt=s(OK);Uqr=r(kxt,"from_pretrained()"),kxt.forEach(t),Jqr=r(poe," to load the model weights."),poe.forEach(t),Yqr=i(FA),T(M3.$$.fragment,FA),FA.forEach(t),Kqr=i(ei),zr=n(ei,"DIV",{class:!0});var oi=s(zr);T(hx.$$.fragment,oi),Zqr=i(oi),EMe=n(oi,"P",{});var Sxt=s(EMe);ejr=r(Sxt,"Instantiate one of the model classes of the library (with a question answering head) from a pretrained model."),Sxt.forEach(t),ojr=i(oi),Fn=n(oi,"P",{});var TA=s(Fn);rjr=r(TA,"The model class to instantiate is selected based on the "),CMe=n(TA,"CODE",{});var Rxt=s(CMe);tjr=r(Rxt,"model_type"),Rxt.forEach(t),ajr=r(TA,` property of the config object (either
passed as an argument or loaded from `),wMe=n(TA,"CODE",{});var Pxt=s(wMe);njr=r(Pxt,"pretrained_model_name_or_path"),Pxt.forEach(t),sjr=r(TA,` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),AMe=n(TA,"CODE",{});var Bxt=s(AMe);ljr=r(Bxt,"pretrained_model_name_or_path"),Bxt.forEach(t),ijr=r(TA,":"),TA.forEach(t),djr=i(oi),ke=n(oi,"UL",{});var je=s(ke);E3=n(je,"LI",{});var nqe=s(E3);yMe=n(nqe,"STRONG",{});var Ixt=s(yMe);cjr=r(Ixt,"albert"),Ixt.forEach(t),fjr=r(nqe," \u2014 "),VK=n(nqe,"A",{href:!0});var Nxt=s(VK);mjr=r(Nxt,"FlaxAlbertForQuestionAnswering"),Nxt.forEach(t),gjr=r(nqe," (ALBERT model)"),nqe.forEach(t),hjr=i(je),C3=n(je,"LI",{});var sqe=s(C3);LMe=n(sqe,"STRONG",{});var qxt=s(LMe);pjr=r(qxt,"bart"),qxt.forEach(t),_jr=r(sqe," \u2014 "),XK=n(sqe,"A",{href:!0});var jxt=s(XK);ujr=r(jxt,"FlaxBartForQuestionAnswering"),jxt.forEach(t),bjr=r(sqe," (BART model)"),sqe.forEach(t),vjr=i(je),w3=n(je,"LI",{});var lqe=s(w3);xMe=n(lqe,"STRONG",{});var Dxt=s(xMe);Fjr=r(Dxt,"bert"),Dxt.forEach(t),Tjr=r(lqe," \u2014 "),zK=n(lqe,"A",{href:!0});var Gxt=s(zK);Mjr=r(Gxt,"FlaxBertForQuestionAnswering"),Gxt.forEach(t),Ejr=r(lqe," (BERT model)"),lqe.forEach(t),Cjr=i(je),A3=n(je,"LI",{});var iqe=s(A3);$Me=n(iqe,"STRONG",{});var Oxt=s($Me);wjr=r(Oxt,"big_bird"),Oxt.forEach(t),Ajr=r(iqe," \u2014 "),WK=n(iqe,"A",{href:!0});var Vxt=s(WK);yjr=r(Vxt,"FlaxBigBirdForQuestionAnswering"),Vxt.forEach(t),Ljr=r(iqe," (BigBird model)"),iqe.forEach(t),xjr=i(je),y3=n(je,"LI",{});var dqe=s(y3);kMe=n(dqe,"STRONG",{});var Xxt=s(kMe);$jr=r(Xxt,"distilbert"),Xxt.forEach(t),kjr=r(dqe," \u2014 "),QK=n(dqe,"A",{href:!0});var zxt=s(QK);Sjr=r(zxt,"FlaxDistilBertForQuestionAnswering"),zxt.forEach(t),Rjr=r(dqe," (DistilBERT model)"),dqe.forEach(t),Pjr=i(je),L3=n(je,"LI",{});var cqe=s(L3);SMe=n(cqe,"STRONG",{});var Wxt=s(SMe);Bjr=r(Wxt,"electra"),Wxt.forEach(t),Ijr=r(cqe," \u2014 "),HK=n(cqe,"A",{href:!0});var Qxt=s(HK);Njr=r(Qxt,"FlaxElectraForQuestionAnswering"),Qxt.forEach(t),qjr=r(cqe," (ELECTRA model)"),cqe.forEach(t),jjr=i(je),x3=n(je,"LI",{});var fqe=s(x3);RMe=n(fqe,"STRONG",{});var Hxt=s(RMe);Djr=r(Hxt,"mbart"),Hxt.forEach(t),Gjr=r(fqe," \u2014 "),UK=n(fqe,"A",{href:!0});var Uxt=s(UK);Ojr=r(Uxt,"FlaxMBartForQuestionAnswering"),Uxt.forEach(t),Vjr=r(fqe," (mBART model)"),fqe.forEach(t),Xjr=i(je),$3=n(je,"LI",{});var mqe=s($3);PMe=n(mqe,"STRONG",{});var Jxt=s(PMe);zjr=r(Jxt,"roberta"),Jxt.forEach(t),Wjr=r(mqe," \u2014 "),JK=n(mqe,"A",{href:!0});var Yxt=s(JK);Qjr=r(Yxt,"FlaxRobertaForQuestionAnswering"),Yxt.forEach(t),Hjr=r(mqe," (RoBERTa model)"),mqe.forEach(t),Ujr=i(je),k3=n(je,"LI",{});var gqe=s(k3);BMe=n(gqe,"STRONG",{});var Kxt=s(BMe);Jjr=r(Kxt,"roformer"),Kxt.forEach(t),Yjr=r(gqe," \u2014 "),YK=n(gqe,"A",{href:!0});var Zxt=s(YK);Kjr=r(Zxt,"FlaxRoFormerForQuestionAnswering"),Zxt.forEach(t),Zjr=r(gqe," (RoFormer model)"),gqe.forEach(t),eDr=i(je),S3=n(je,"LI",{});var hqe=s(S3);IMe=n(hqe,"STRONG",{});var e$t=s(IMe);oDr=r(e$t,"xlm-roberta"),e$t.forEach(t),rDr=r(hqe," \u2014 "),KK=n(hqe,"A",{href:!0});var o$t=s(KK);tDr=r(o$t,"FlaxXLMRobertaForQuestionAnswering"),o$t.forEach(t),aDr=r(hqe," (XLM-RoBERTa model)"),hqe.forEach(t),je.forEach(t),nDr=i(oi),T(R3.$$.fragment,oi),oi.forEach(t),ei.forEach(t),LDe=i(f),Yc=n(f,"H2",{class:!0});var BOe=s(Yc);P3=n(BOe,"A",{id:!0,class:!0,href:!0});var r$t=s(P3);NMe=n(r$t,"SPAN",{});var t$t=s(NMe);T(px.$$.fragment,t$t),t$t.forEach(t),r$t.forEach(t),sDr=i(BOe),qMe=n(BOe,"SPAN",{});var a$t=s(qMe);lDr=r(a$t,"FlaxAutoModelForTokenClassification"),a$t.forEach(t),BOe.forEach(t),xDe=i(f),br=n(f,"DIV",{class:!0});var ri=s(br);T(_x.$$.fragment,ri),iDr=i(ri),Kc=n(ri,"P",{});var _oe=s(Kc);dDr=r(_oe,`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 `),ZK=n(_oe,"A",{href:!0});var n$t=s(ZK);cDr=r(n$t,"from_pretrained()"),n$t.forEach(t),fDr=r(_oe," class method or the "),eZ=n(_oe,"A",{href:!0});var s$t=s(eZ);mDr=r(s$t,"from_config()"),s$t.forEach(t),gDr=r(_oe,` class
method.`),_oe.forEach(t),hDr=i(ri),ux=n(ri,"P",{});var IOe=s(ux);pDr=r(IOe,"This class cannot be instantiated directly using "),jMe=n(IOe,"CODE",{});var l$t=s(jMe);_Dr=r(l$t,"__init__()"),l$t.forEach(t),uDr=r(IOe," (throws an error)."),IOe.forEach(t),bDr=i(ri),Ht=n(ri,"DIV",{class:!0});var MA=s(Ht);T(bx.$$.fragment,MA),vDr=i(MA),DMe=n(MA,"P",{});var i$t=s(DMe);FDr=r(i$t,"Instantiates one of the model classes of the library (with a token classification head) from a configuration."),i$t.forEach(t),TDr=i(MA),Zc=n(MA,"P",{});var uoe=s(Zc);MDr=r(uoe,`Note:
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model\u2019s configuration. Use `),oZ=n(uoe,"A",{href:!0});var c$t=s(oZ);wDr=r(c$t,"from_pretrained()"),c$t.forEach(t),ADr=r(uoe," to load the model weights."),uoe.forEach(t),yDr=i(MA),T(B3.$$.fragment,MA),MA.forEach(t),LDr=i(ri),Wr=n(ri,"DIV",{class:!0});var ti=s(Wr);T(vx.$$.fragment,ti),xDr=i(ti),OMe=n(ti,"P",{});var f$t=s(OMe);$Dr=r(f$t,"Instantiate one of the model classes of the library (with a token classification head) from a pretrained model."),f$t.forEach(t),kDr=i(ti),Tn=n(ti,"P",{});var EA=s(Tn);SDr=r(EA,"The model class to instantiate is selected based on the "),VMe=n(EA,"CODE",{});var m$t=s(VMe);RDr=r(m$t,"model_type"),m$t.forEach(t),PDr=r(EA,` property of the config object (either
passed as an argument or loaded from `),XMe=n(EA,"CODE",{});var g$t=s(XMe);BDr=r(g$t,"pretrained_model_name_or_path"),g$t.forEach(t),IDr=r(EA,` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),zMe=n(EA,"CODE",{});var h$t=s(zMe);NDr=r(h$t,"pretrained_model_name_or_path"),h$t.forEach(t),qDr=r(EA,":"),EA.forEach(t),jDr=i(ti),Ge=n(ti,"UL",{});var Fo=s(Ge);I3=n(Fo,"LI",{});var pqe=s(I3);WMe=n(pqe,"STRONG",{});var p$t=s(WMe);DDr=r(p$t,"albert"),p$t.forEach(t),GDr=r(pqe," \u2014 "),rZ=n(pqe,"A",{href:!0});var _$t=s(rZ);ODr=r(_$t,"FlaxAlbertForTokenClassification"),_$t.forEach(t),VDr=r(pqe," (ALBERT model)"),pqe.forEach(t),XDr=i(Fo),N3=n(Fo,"LI",{});var _qe=s(N3);QMe=n(_qe,"STRONG",{});var u$t=s(QMe);zDr=r(u$t,"bert"),u$t.forEach(t),WDr=r(_qe," \u2014 "),tZ=n(_qe,"A",{href:!0});var b$t=s(tZ);QDr=r(b$t,"FlaxBertForTokenClassification"),b$t.forEach(t),HDr=r(_qe," (BERT model)"),_qe.forEach(t),UDr=i(Fo),q3=n(Fo,"LI",{});var uqe=s(q3);HMe=n(uqe,"STRONG",{});var v$t=s(HMe);JDr=r(v$t,"big_bird"),v$t.forEach(t),YDr=r(uqe," \u2014 "),aZ=n(uqe,"A",{href:!0});var F$t=s(aZ);KDr=r(F$t,"FlaxBigBirdForTokenClassification"),F$t.forEach(t),ZDr=r(uqe," (BigBird model)"),uqe.forEach(t),eGr=i(Fo),j3=n(Fo,"LI",{});var bqe=s(j3);UMe=n(bqe,"STRONG",{});var T$t=s(UMe);oGr=r(T$t,"distilbert"),T$t.forEach(t),rGr=r(bqe," \u2014 "),nZ=n(bqe,"A",{href:!0});var M$t=s(nZ);tGr=r(M$t,"FlaxDistilBertForTokenClassification"),M$t.forEach(t),aGr=r(bqe," (DistilBERT model)"),bqe.forEach(t),nGr=i(Fo),D3=n(Fo,"LI",{});var vqe=s(D3);JMe=n(vqe,"STRONG",{});var E$t=s(JMe);sGr=r(E$t,"electra"),E$t.forEach(t),lGr=r(vqe," \u2014 "),sZ=n(vqe,"A",{href:!0});var C$t=s(sZ);iGr=r(C$t,"FlaxElectraForTokenClassification"),C$t.forEach(t),dGr=r(vqe," (ELECTRA model)"),vqe.forEach(t),cGr=i(Fo),G3=n(Fo,"LI",{});var Fqe=s(G3);YMe=n(Fqe,"STRONG",{});var w$t=s(YMe);fGr=r(w$t,"roberta"),w$t.forEach(t),mGr=r(Fqe," \u2014 "),lZ=n(Fqe,"A",{href:!0});var A$t=s(lZ);gGr=r(A$t,"FlaxRobertaForTokenClassification"),A$t.forEach(t),hGr=r(Fqe," (RoBERTa model)"),Fqe.forEach(t),pGr=i(Fo),O3=n(Fo,"LI",{});var Tqe=s(O3);KMe=n(Tqe,"STRONG",{});var y$t=s(KMe);_Gr=r(y$t,"roformer"),y$t.forEach(t),uGr=r(Tqe," \u2014 "),iZ=n(Tqe,"A",{href:!0});var L$t=s(iZ);bGr=r(L$t,"FlaxRoFormerForTokenClassification"),L$t.forEach(t),vGr=r(Tqe," (RoFormer model)"),Tqe.forEach(t),FGr=i(Fo),V3=n(Fo,"LI",{});var Mqe=s(V3);ZMe=n(Mqe,"STRONG",{});var x$t=s(ZMe);TGr=r(x$t,"xlm-roberta"),x$t.forEach(t),MGr=r(Mqe," \u2014 "),dZ=n(Mqe,"A",{href:!0});var $$t=s(dZ);EGr=r($$t,"FlaxXLMRobertaForTokenClassification"),$$t.forEach(t),CGr=r(Mqe," (XLM-RoBERTa model)"),Mqe.forEach(t),Fo.forEach(t),wGr=i(ti),T(X3.$$.fragment,ti),ti.forEach(t),ri.forEach(t),$De=i(f),ef=n(f,"H2",{class:!0});var NOe=s(ef);z3=n(NOe,"A",{id:!0,class:!0,href:!0});var k$t=s(z3);eEe=n(k$t,"SPAN",{});var S$t=s(eEe);T(Fx.$$.fragment,S$t),S$t.forEach(t),k$t.forEach(t),AGr=i(NOe),oEe=n(NOe,"SPAN",{});var R$t=s(oEe);yGr=r(R$t,"FlaxAutoModelForMultipleChoice"),R$t.forEach(t),NOe.forEach(t),kDe=i(f),vr=n(f,"DIV",{class:!0});var ai=s(vr);T(Tx.$$.fragment,ai),LGr=i(ai),of=n(ai,"P",{});var boe=s(of);xGr=r(boe,`This is a generic model class that will be instantiated as one of the model classes of the library (with a multiple choice head) when created
with the `),cZ=n(boe,"A",{href:!0});var P$t=s(cZ);$Gr=r(P$t,"from_pretrained()"),P$t.forEach(t),kGr=r(boe," class method or the "),fZ=n(boe,"A",{href:!0});var B$t=s(fZ);SGr=r(B$t,"from_config()"),B$t.forEach(t),RGr=r(boe,` class
method.`),boe.forEach(t),PGr=i(ai),Mx=n(ai,"P",{});var qOe=s(Mx);BGr=r(qOe,"This class cannot be instantiated directly using "),rEe=n(qOe,"CODE",{});var I$t=s(rEe);IGr=r(I$t,"__init__()"),I$t.forEach(t),NGr=r(qOe," (throws an error)."),qOe.forEach(t),qGr=i(ai),Ut=n(ai,"DIV",{class:!0});var CA=s(Ut);T(Ex.$$.fragment,CA),jGr=i(CA),tEe=n(CA,"P",{});var N$t=s(tEe);DGr=r(N$t,"Instantiates one of the model classes of the library (with a multiple choice head) from a configuration."),N$t.forEach(t),GGr=i(CA),rf=n(CA,"P",{});var voe=s(rf);OGr=r(voe,`Note:
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model\u2019s configuration. Use `),mZ=n(voe,"A",{href:!0});var j$t=s(mZ);zGr=r(j$t,"from_pretrained()"),j$t.forEach(t),WGr=r(voe," to load the model weights."),voe.forEach(t),QGr=i(CA),T(W3.$$.fragment,CA),CA.forEach(t),HGr=i(ai),Qr=n(ai,"DIV",{class:!0});var ni=s(Qr);T(Cx.$$.fragment,ni),UGr=i(ni),nEe=n(ni,"P",{});var D$t=s(nEe);JGr=r(D$t,"Instantiate one of the model classes of the library (with a multiple choice head) from a pretrained model."),D$t.forEach(t),YGr=i(ni),Mn=n(ni,"P",{});var wA=s(Mn);KGr=r(wA,"The model class to instantiate is selected based on the "),sEe=n(wA,"CODE",{});var G$t=s(sEe);ZGr=r(G$t,"model_type"),G$t.forEach(t),eOr=r(wA,` property of the config object (either
passed as an argument or loaded from `),lEe=n(wA,"CODE",{});var O$t=s(lEe);oOr=r(O$t,"pretrained_model_name_or_path"),O$t.forEach(t),rOr=r(wA,` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),iEe=n(wA,"CODE",{});var V$t=s(iEe);tOr=r(V$t,"pretrained_model_name_or_path"),V$t.forEach(t),aOr=r(wA,":"),wA.forEach(t),nOr=i(ni),Oe=n(ni,"UL",{});var To=s(Oe);Q3=n(To,"LI",{});var Eqe=s(Q3);dEe=n(Eqe,"STRONG",{});var X$t=s(dEe);sOr=r(X$t,"albert"),X$t.forEach(t),lOr=r(Eqe," \u2014 "),gZ=n(Eqe,"A",{href:!0});var z$t=s(gZ);iOr=r(z$t,"FlaxAlbertForMultipleChoice"),z$t.forEach(t),dOr=r(Eqe," (ALBERT model)"),Eqe.forEach(t),cOr=i(To),H3=n(To,"LI",{});var Cqe=s(H3);cEe=n(Cqe,"STRONG",{});var W$t=s(cEe);fOr=r(W$t,"bert"),W$t.forEach(t),mOr=r(Cqe," \u2014 "),hZ=n(Cqe,"A",{href:!0});var Q$t=s(hZ);gOr=r(Q$t,"FlaxBertForMultipleChoice"),Q$t.forEach(t),hOr=r(Cqe," (BERT model)"),Cqe.forEach(t),pOr=i(To),U3=n(To,"LI",{});var wqe=s(U3);fEe=n(wqe,"STRONG",{});var H$t=s(fEe);_Or=r(H$t,"big_bird"),H$t.forEach(t),uOr=r(wqe," \u2014 "),pZ=n(wqe,"A",{href:!0});var U$t=s(pZ);bOr=r(U$t,"FlaxBigBirdForMultipleChoice"),U$t.forEach(t),vOr=r(wqe," (BigBird model)"),wqe.forEach(t),FOr=i(To),J3=n(To,"LI",{});var Aqe=s(J3);mEe=n(Aqe,"STRONG",{});var J$t=s(mEe);TOr=r(J$t,"distilbert"),J$t.forEach(t),MOr=r(Aqe," \u2014 "),_Z=n(Aqe,"A",{href:!0});var Y$t=s(_Z);EOr=r(Y$t,"FlaxDistilBertForMultipleChoice"),Y$t.forEach(t),COr=r(Aqe," (DistilBERT model)"),Aqe.forEach(t),wOr=i(To),Y3=n(To,"LI",{});var yqe=s(Y3);gEe=n(yqe,"STRONG",{});var K$t=s(gEe);AOr=r(K$t,"electra"),K$t.forEach(t),yOr=r(yqe," \u2014 "),uZ=n(yqe,"A",{href:!0});var Z$t=s(uZ);LOr=r(Z$t,"FlaxElectraForMultipleChoice"),Z$t.forEach(t),xOr=r(yqe," (ELECTRA model)"),yqe.forEach(t),$Or=i(To),K3=n(To,"LI",{});var Lqe=s(K3);hEe=n(Lqe,"STRONG",{});var ekt=s(hEe);kOr=r(ekt,"roberta"),ekt.forEach(t),SOr=r(Lqe," \u2014 "),bZ=n(Lqe,"A",{href:!0});var okt=s(bZ);ROr=r(okt,"FlaxRobertaForMultipleChoice"),okt.forEach(t),POr=r(Lqe," (RoBERTa model)"),Lqe.forEach(t),BOr=i(To),Z3=n(To,"LI",{});var xqe=s(Z3);pEe=n(xqe,"STRONG",{});var rkt=s(pEe);IOr=r(rkt,"roformer"),rkt.forEach(t),NOr=r(xqe," \u2014 "),vZ=n(xqe,"A",{href:!0});var tkt=s(vZ);qOr=r(tkt,"FlaxRoFormerForMultipleChoice"),tkt.forEach(t),jOr=r(xqe," (RoFormer model)"),xqe.forEach(t),DOr=i(To),e0=n(To,"LI",{});var $qe=s(e0);_Ee=n($qe,"STRONG",{});var akt=s(_Ee);GOr=r(akt,"xlm-roberta"),akt.forEach(t),OOr=r($qe," \u2014 "),FZ=n($qe,"A",{href:!0});var nkt=s(FZ);VOr=r(nkt,"FlaxXLMRobertaForMultipleChoice"),nkt.forEach(t),XOr=r($qe," (XLM-RoBERTa model)"),$qe.forEach(t),To.forEach(t),zOr=i(ni),T(o0.$$.fragment,ni),ni.forEach(t),ai.forEach(t),SDe=i(f),tf=n(f,"H2",{class:!0});var jOe=s(tf);r0=n(jOe,"A",{id:!0,class:!0,href:!0});var skt=s(r0);uEe=n(skt,"SPAN",{});var lkt=s(uEe);T(wx.$$.fragment,lkt),lkt.forEach(t),skt.forEach(t),WOr=i(jOe),bEe=n(jOe,"SPAN",{});var ikt=s(bEe);QOr=r(ikt,"FlaxAutoModelForNextSentencePrediction"),ikt.forEach(t),jOe.forEach(t),RDe=i(f),Fr=n(f,"DIV",{class:!0});var si=s(Fr);T(Ax.$$.fragment,si),HOr=i(si),af=n(si,"P",{});var Foe=s(af);UOr=r(Foe,`This is a generic model class that will be instantiated as one of the model classes of the library (with a next sentence prediction head) when created
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Classes"};function nPt(L){return nSt(()=>{new URLSearchParams(window.location.search).get("fw")}),[]}class mPt extends oSt{constructor(g){super();rSt(this,g,nPt,tPt,tSt,{})}}export{mPt as default,aPt as metadata};
