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fe=new X({}),qa=new w({props:{code:'model = AutoModel.from_pretrained("bert-base-cased")',highlighted:'model = AutoModel.from_pretrained(<span class="hljs-string">&quot;bert-base-cased&quot;</span>)'}}),YM=new X({}),KM=new w({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)`}}),Of=new iBr({props:{warning:"&lcub;true}",$$slots:{default:[NTt]},$$scope:{ctx:Ii}}}),ZM=new X({}),e4=new M({props:{name:"class transformers.AutoConfig",anchor:"transformers.AutoConfig",parameters:[],source:"https://github.com/huggingface/transformers/blob/pr_15839/src/transformers/models/auto/configuration_auto.py#L527"}}),t4=new M({props:{name:"from_pretrained",anchor:"transformers.AutoConfig.from_pretrained",parameters:[{name:"pretrained_model_name_or_path",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/pr_15839/src/transformers/models/auto/configuration_auto.py#L550",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_15839/en/main_classes/configuration#transformers.PretrainedConfig.save_pretrained">save_pretrained()</a> method, or the <a href="/docs/transformers/pr_15839/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(str,",description:`<strong>revision(<code>str</code>,</strong> <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a
git-based system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any
identifier allowed by git.`,name:"revision(str,"},{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"}]}}),a4=new w({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

config.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>config.unused_kwargs
{<span class="hljs-string">&#x27;foo&#x27;</span>: <span class="hljs-literal">False</span>}`}}),n4=new M({props:{name:"register",anchor:"transformers.AutoConfig.register",parameters:[{name:"model_type",val:""},{name:"config",val:""}],source:"https://github.com/huggingface/transformers/blob/pr_15839/src/transformers/models/auto/configuration_auto.py#L672",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_15839/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>) &#x2014; The config to register.',name:"config"}]}}),s4=new X({}),l4=new M({props:{name:"class transformers.AutoTokenizer",anchor:"transformers.AutoTokenizer",parameters:[],source:"https://github.com/huggingface/transformers/blob/pr_15839/src/transformers/models/auto/tokenization_auto.py#L352"}}),c4=new M({props:{name:"from_pretrained",anchor:"transformers.AutoTokenizer.from_pretrained",parameters:[{name:"pretrained_model_name_or_path",val:""},{name:"*inputs",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/pr_15839/src/transformers/models/auto/tokenization_auto.py#L366",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_15839/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_15839/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(str,",description:`<strong>revision(<code>str</code>,</strong> <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a
git-based system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any
identifier allowed by git.`,name:"revision(str,"},{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"}]}}),f4=new w({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/")`,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>)`}}),m4=new M({props:{name:"register",anchor:"transformers.AutoTokenizer.register",parameters:[{name:"config_class",val:""},{name:"slow_tokenizer_class",val:" = None"},{name:"fast_tokenizer_class",val:" = None"}],source:"https://github.com/huggingface/transformers/blob/pr_15839/src/transformers/models/auto/tokenization_auto.py#L562",parametersDescription:[{anchor:"transformers.AutoTokenizer.register.config_class",description:`<strong>config_class</strong> (<a href="/docs/transformers/pr_15839/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"}]}}),g4=new X({}),h4=new M({props:{name:"class transformers.AutoFeatureExtractor",anchor:"transformers.AutoFeatureExtractor",parameters:[],source:"https://github.com/huggingface/transformers/blob/pr_15839/src/transformers/models/auto/feature_extraction_auto.py#L170"}}),u4=new M({props:{name:"from_pretrained",anchor:"transformers.AutoFeatureExtractor.from_pretrained",parameters:[{name:"pretrained_model_name_or_path",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/pr_15839/src/transformers/models/auto/feature_extraction_auto.py#L184",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_15839/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(str,",description:`<strong>revision(<code>str</code>,</strong> <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a
git-based system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any
identifier allowed by git.`,name:"revision(str,"},{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"}]}}),Eh=new iBr({props:{$$slots:{default:[qTt]},$$scope:{ctx:Ii}}}),b4=new w({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>)`}}),v4=new M({props:{name:"register",anchor:"transformers.AutoFeatureExtractor.register",parameters:[{name:"config_class",val:""},{name:"feature_extractor_class",val:""}],source:"https://github.com/huggingface/transformers/blob/pr_15839/src/transformers/models/auto/feature_extraction_auto.py#L311",parametersDescription:[{anchor:"transformers.AutoFeatureExtractor.register.config_class",description:`<strong>config_class</strong> (<a href="/docs/transformers/pr_15839/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"}]}}),T4=new X({}),F4=new M({props:{name:"class transformers.AutoProcessor",anchor:"transformers.AutoProcessor",parameters:[],source:"https://github.com/huggingface/transformers/blob/pr_15839/src/transformers/models/auto/processing_auto.py#L71"}}),E4=new M({props:{name:"from_pretrained",anchor:"transformers.AutoProcessor.from_pretrained",parameters:[{name:"pretrained_model_name_or_path",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/pr_15839/src/transformers/models/auto/processing_auto.py#L85",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"}]}}),$h=new iBr({props:{$$slots:{default:[OTt]},$$scope:{ctx:Ii}}}),y4=new w({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>)`}}),w4=new M({props:{name:"register",anchor:"transformers.AutoProcessor.register",parameters:[{name:"config_class",val:""},{name:"processor_class",val:""}],source:"https://github.com/huggingface/transformers/blob/pr_15839/src/transformers/models/auto/processing_auto.py#L238",parametersDescription:[{anchor:"transformers.AutoProcessor.register.config_class",description:`<strong>config_class</strong> (<a href="/docs/transformers/pr_15839/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"}]}}),A4=new X({}),L4=new M({props:{name:"class transformers.AutoModel",anchor:"transformers.AutoModel",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/pr_15839/src/transformers/models/auto/modeling_auto.py#L697"}}),x4=new M({props:{name:"from_config",anchor:"transformers.models.auto.auto_factory._BaseAutoModelClass.from_config",parameters:[{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/pr_15839/src/transformers/models/auto/auto_factory.py#L390",parametersDescription:[{anchor:"transformers.models.auto.auto_factory._BaseAutoModelClass.from_config.config",description:`<strong>config</strong> (<a href="/docs/transformers/pr_15839/en/main_classes/configuration#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_15839/en/model_doc/albert#transformers.AlbertConfig">AlbertConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/albert#transformers.AlbertModel">AlbertModel</a> (ALBERT model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/bart#transformers.BartConfig">BartConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/bart#transformers.BartModel">BartModel</a> (BART model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/beit#transformers.BeitConfig">BeitConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/beit#transformers.BeitModel">BeitModel</a> (BEiT model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/bert#transformers.BertConfig">BertConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/bert#transformers.BertModel">BertModel</a> (BERT model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/bert-generation#transformers.BertGenerationConfig">BertGenerationConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/bert-generation#transformers.BertGenerationEncoder">BertGenerationEncoder</a> (Bert Generation model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/big_bird#transformers.BigBirdConfig">BigBirdConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/big_bird#transformers.BigBirdModel">BigBirdModel</a> (BigBird model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/bigbird_pegasus#transformers.BigBirdPegasusConfig">BigBirdPegasusConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/bigbird_pegasus#transformers.BigBirdPegasusModel">BigBirdPegasusModel</a> (BigBirdPegasus model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/blenderbot#transformers.BlenderbotConfig">BlenderbotConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/blenderbot#transformers.BlenderbotModel">BlenderbotModel</a> (Blenderbot model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/blenderbot-small#transformers.BlenderbotSmallConfig">BlenderbotSmallConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/blenderbot-small#transformers.BlenderbotSmallModel">BlenderbotSmallModel</a> (BlenderbotSmall model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/clip#transformers.CLIPConfig">CLIPConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/clip#transformers.CLIPModel">CLIPModel</a> (CLIP model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/ctrl#transformers.CTRLConfig">CTRLConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/ctrl#transformers.CTRLModel">CTRLModel</a> (CTRL model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/camembert#transformers.CamembertConfig">CamembertConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/camembert#transformers.CamembertModel">CamembertModel</a> (CamemBERT model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/canine#transformers.CanineConfig">CanineConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/canine#transformers.CanineModel">CanineModel</a> (Canine model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/convbert#transformers.ConvBertConfig">ConvBertConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/convbert#transformers.ConvBertModel">ConvBertModel</a> (ConvBERT model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/convnext#transformers.ConvNextConfig">ConvNextConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/convnext#transformers.ConvNextModel">ConvNextModel</a> (ConvNext model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/dpr#transformers.DPRConfig">DPRConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/dpr#transformers.DPRQuestionEncoder">DPRQuestionEncoder</a> (DPR model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/data2vec#transformers.Data2VecAudioConfig">Data2VecAudioConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/data2vec#transformers.Data2VecAudioModel">Data2VecAudioModel</a> (Data2VecAudio model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/data2vec#transformers.Data2VecTextConfig">Data2VecTextConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/data2vec#transformers.Data2VecTextModel">Data2VecTextModel</a> (Data2VecText model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/deberta#transformers.DebertaConfig">DebertaConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/deberta#transformers.DebertaModel">DebertaModel</a> (DeBERTa model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/deberta-v2#transformers.DebertaV2Config">DebertaV2Config</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/deberta-v2#transformers.DebertaV2Model">DebertaV2Model</a> (DeBERTa-v2 model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/deit#transformers.DeiTConfig">DeiTConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/deit#transformers.DeiTModel">DeiTModel</a> (DeiT model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/detr#transformers.DetrConfig">DetrConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/detr#transformers.DetrModel">DetrModel</a> (DETR model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/distilbert#transformers.DistilBertConfig">DistilBertConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/distilbert#transformers.DistilBertModel">DistilBertModel</a> (DistilBERT model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/electra#transformers.ElectraConfig">ElectraConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/electra#transformers.ElectraModel">ElectraModel</a> (ELECTRA model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/fnet#transformers.FNetConfig">FNetConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/fnet#transformers.FNetModel">FNetModel</a> (FNet model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/fsmt#transformers.FSMTConfig">FSMTConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/fsmt#transformers.FSMTModel">FSMTModel</a> (FairSeq Machine-Translation model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/flaubert#transformers.FlaubertConfig">FlaubertConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/flaubert#transformers.FlaubertModel">FlaubertModel</a> (FlauBERT model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/funnel#transformers.FunnelConfig">FunnelConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/funnel#transformers.FunnelModel">FunnelModel</a> or <a href="/docs/transformers/pr_15839/en/model_doc/funnel#transformers.FunnelBaseModel">FunnelBaseModel</a> (Funnel Transformer model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/gpt2#transformers.GPT2Config">GPT2Config</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/gpt2#transformers.GPT2Model">GPT2Model</a> (OpenAI GPT-2 model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/gptj#transformers.GPTJConfig">GPTJConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/gptj#transformers.GPTJModel">GPTJModel</a> (GPT-J model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/gpt_neo#transformers.GPTNeoConfig">GPTNeoConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/gpt_neo#transformers.GPTNeoModel">GPTNeoModel</a> (GPT Neo model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/hubert#transformers.HubertConfig">HubertConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/hubert#transformers.HubertModel">HubertModel</a> (Hubert model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/ibert#transformers.IBertConfig">IBertConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/ibert#transformers.IBertModel">IBertModel</a> (I-BERT model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/imagegpt#transformers.ImageGPTConfig">ImageGPTConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/imagegpt#transformers.ImageGPTModel">ImageGPTModel</a> (ImageGPT model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/led#transformers.LEDConfig">LEDConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/led#transformers.LEDModel">LEDModel</a> (LED model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/layoutlm#transformers.LayoutLMConfig">LayoutLMConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/layoutlm#transformers.LayoutLMModel">LayoutLMModel</a> (LayoutLM model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/layoutlmv2#transformers.LayoutLMv2Config">LayoutLMv2Config</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/layoutlmv2#transformers.LayoutLMv2Model">LayoutLMv2Model</a> (LayoutLMv2 model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/longformer#transformers.LongformerConfig">LongformerConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/longformer#transformers.LongformerModel">LongformerModel</a> (Longformer model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/luke#transformers.LukeConfig">LukeConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/luke#transformers.LukeModel">LukeModel</a> (LUKE model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/lxmert#transformers.LxmertConfig">LxmertConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/lxmert#transformers.LxmertModel">LxmertModel</a> (LXMERT model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/m2m_100#transformers.M2M100Config">M2M100Config</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/m2m_100#transformers.M2M100Model">M2M100Model</a> (M2M100 model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/mbart#transformers.MBartConfig">MBartConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/mbart#transformers.MBartModel">MBartModel</a> (mBART model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/mpnet#transformers.MPNetConfig">MPNetConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/mpnet#transformers.MPNetModel">MPNetModel</a> (MPNet model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/mt5#transformers.MT5Config">MT5Config</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/mt5#transformers.MT5Model">MT5Model</a> (mT5 model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/marian#transformers.MarianConfig">MarianConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/marian#transformers.MarianModel">MarianModel</a> (Marian model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/maskformer#transformers.MaskFormerConfig">MaskFormerConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/maskformer#transformers.MaskFormerModel">MaskFormerModel</a> (MaskFormer model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/megatron-bert#transformers.MegatronBertConfig">MegatronBertConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/megatron-bert#transformers.MegatronBertModel">MegatronBertModel</a> (MegatronBert model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/mobilebert#transformers.MobileBertConfig">MobileBertConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/mobilebert#transformers.MobileBertModel">MobileBertModel</a> (MobileBERT model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/nystromformer#transformers.NystromformerConfig">NystromformerConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/nystromformer#transformers.NystromformerModel">NystromformerModel</a> (Nystromformer model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/openai-gpt#transformers.OpenAIGPTConfig">OpenAIGPTConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/openai-gpt#transformers.OpenAIGPTModel">OpenAIGPTModel</a> (OpenAI GPT model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/plbart#transformers.PLBartConfig">PLBartConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/plbart#transformers.PLBartModel">PLBartModel</a> (PLBart model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/pegasus#transformers.PegasusConfig">PegasusConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/pegasus#transformers.PegasusModel">PegasusModel</a> (Pegasus model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/perceiver#transformers.PerceiverConfig">PerceiverConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/perceiver#transformers.PerceiverModel">PerceiverModel</a> (Perceiver model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/poolformer#transformers.PoolFormerConfig">PoolFormerConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/poolformer#transformers.PoolFormerModel">PoolFormerModel</a> (PoolFormer model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/prophetnet#transformers.ProphetNetConfig">ProphetNetConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/prophetnet#transformers.ProphetNetModel">ProphetNetModel</a> (ProphetNet model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/qdqbert#transformers.QDQBertConfig">QDQBertConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/qdqbert#transformers.QDQBertModel">QDQBertModel</a> (QDQBert model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/reformer#transformers.ReformerConfig">ReformerConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/reformer#transformers.ReformerModel">ReformerModel</a> (Reformer model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/rembert#transformers.RemBertConfig">RemBertConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/rembert#transformers.RemBertModel">RemBertModel</a> (RemBERT model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/retribert#transformers.RetriBertConfig">RetriBertConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/retribert#transformers.RetriBertModel">RetriBertModel</a> (RetriBERT model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/roformer#transformers.RoFormerConfig">RoFormerConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/roformer#transformers.RoFormerModel">RoFormerModel</a> (RoFormer model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/roberta#transformers.RobertaConfig">RobertaConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/roberta#transformers.RobertaModel">RobertaModel</a> (RoBERTa model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/sew#transformers.SEWConfig">SEWConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/sew#transformers.SEWModel">SEWModel</a> (SEW model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/sew-d#transformers.SEWDConfig">SEWDConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/sew-d#transformers.SEWDModel">SEWDModel</a> (SEW-D model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/segformer#transformers.SegformerConfig">SegformerConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/segformer#transformers.SegformerModel">SegformerModel</a> (SegFormer model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/speech_to_text#transformers.Speech2TextConfig">Speech2TextConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/speech_to_text#transformers.Speech2TextModel">Speech2TextModel</a> (Speech2Text model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/splinter#transformers.SplinterConfig">SplinterConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/splinter#transformers.SplinterModel">SplinterModel</a> (Splinter model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/squeezebert#transformers.SqueezeBertConfig">SqueezeBertConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/squeezebert#transformers.SqueezeBertModel">SqueezeBertModel</a> (SqueezeBERT model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/swin#transformers.SwinConfig">SwinConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/swin#transformers.SwinModel">SwinModel</a> (Swin model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/t5#transformers.T5Config">T5Config</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/t5#transformers.T5Model">T5Model</a> (T5 model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/tapas#transformers.TapasConfig">TapasConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/tapas#transformers.TapasModel">TapasModel</a> (TAPAS model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/transfo-xl#transformers.TransfoXLConfig">TransfoXLConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/transfo-xl#transformers.TransfoXLModel">TransfoXLModel</a> (Transformer-XL model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/unispeech#transformers.UniSpeechConfig">UniSpeechConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/unispeech#transformers.UniSpeechModel">UniSpeechModel</a> (UniSpeech model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/unispeech-sat#transformers.UniSpeechSatConfig">UniSpeechSatConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/unispeech-sat#transformers.UniSpeechSatModel">UniSpeechSatModel</a> (UniSpeechSat model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/vit#transformers.ViTConfig">ViTConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/vit#transformers.ViTModel">ViTModel</a> (ViT model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/vit_mae#transformers.ViTMAEConfig">ViTMAEConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/vit_mae#transformers.ViTMAEModel">ViTMAEModel</a> (ViTMAE model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/vilt#transformers.ViltConfig">ViltConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/vilt#transformers.ViltModel">ViltModel</a> (ViLT model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/vision-text-dual-encoder#transformers.VisionTextDualEncoderConfig">VisionTextDualEncoderConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/vision-text-dual-encoder#transformers.VisionTextDualEncoderModel">VisionTextDualEncoderModel</a> (VisionTextDualEncoder model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/visual_bert#transformers.VisualBertConfig">VisualBertConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/visual_bert#transformers.VisualBertModel">VisualBertModel</a> (VisualBert model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/wav2vec2#transformers.Wav2Vec2Config">Wav2Vec2Config</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/wav2vec2#transformers.Wav2Vec2Model">Wav2Vec2Model</a> (Wav2Vec2 model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/wavlm#transformers.WavLMConfig">WavLMConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/wavlm#transformers.WavLMModel">WavLMModel</a> (WavLM model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/xglm#transformers.XGLMConfig">XGLMConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/xglm#transformers.XGLMModel">XGLMModel</a> (XGLM model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/xlm#transformers.XLMConfig">XLMConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/xlm#transformers.XLMModel">XLMModel</a> (XLM model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/xlm-prophetnet#transformers.XLMProphetNetConfig">XLMProphetNetConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/xlm-prophetnet#transformers.XLMProphetNetModel">XLMProphetNetModel</a> (XLMProphetNet model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/xlm-roberta#transformers.XLMRobertaConfig">XLMRobertaConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/xlm-roberta#transformers.XLMRobertaModel">XLMRobertaModel</a> (XLM-RoBERTa model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/xlm-roberta-xl#transformers.XLMRobertaXLConfig">XLMRobertaXLConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/xlm-roberta-xl#transformers.XLMRobertaXLModel">XLMRobertaXLModel</a> (XLM-RoBERTa-XL model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/xlnet#transformers.XLNetConfig">XLNetConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/xlnet#transformers.XLNetModel">XLNetModel</a> (XLNet model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/yoso#transformers.YosoConfig">YosoConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/yoso#transformers.YosoModel">YosoModel</a> (YOSO model)</li>
</ul>`,name:"config"}]}}),k4=new w({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)`}}),R4=new M({props:{name:"from_pretrained",anchor:"transformers.models.auto.auto_factory._BaseAutoModelClass.from_pretrained",parameters:[{name:"*model_args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/pr_15839/src/transformers/models/auto/auto_factory.py#L418",parametersDescription:[{anchor:"transformers.models.auto.auto_factory._BaseAutoModelClass.from_pretrained.pretrained_model_name_or_path",description:`<strong>pretrained_model_name_or_path</strong> (<code>str</code> or <code>os.PathLike</code>) &#x2014;
Can be either:</p>
<ul>
<li>A string, the <em>model id</em> of a pretrained model hosted inside a model repo on huggingface.co.
Valid model ids can be located at the root-level, like <code>bert-base-uncased</code>, or namespaced under a
user or organization name, like <code>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_15839/en/main_classes/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.models.auto.auto_factory._BaseAutoModelClass.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.models.auto.auto_factory._BaseAutoModelClass.from_pretrained.config",description:`<strong>config</strong> (<a href="/docs/transformers/pr_15839/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>, <em>optional</em>) &#x2014;
Configuration for the model to use instead of an automatically loaded configuration. Configuration can
be automatically loaded when:</p>
<ul>
<li>The model is a model provided by the 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_15839/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and is reloaded by supplying the
save directory.</li>
<li>The model is loaded by supplying a local directory 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.models.auto.auto_factory._BaseAutoModelClass.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_15839/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and
<a href="/docs/transformers/pr_15839/en/main_classes/model#transformers.PreTrainedModel.from_pretrained">from_pretrained()</a> is not a simpler option.`,name:"state_dict"},{anchor:"transformers.models.auto.auto_factory._BaseAutoModelClass.from_pretrained.cache_dir",description:`<strong>cache_dir</strong> (<code>str</code> or <code>os.PathLike</code>, <em>optional</em>) &#x2014;
Path to a directory in which a downloaded pretrained model configuration should be cached if the
standard cache should not be used.`,name:"cache_dir"},{anchor:"transformers.models.auto.auto_factory._BaseAutoModelClass.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.models.auto.auto_factory._BaseAutoModelClass.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.models.auto.auto_factory._BaseAutoModelClass.from_pretrained.resume_download",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.models.auto.auto_factory._BaseAutoModelClass.from_pretrained.proxies",description:`<strong>proxies</strong> (<code>Dict[str, str]</code>, <em>optional</em>) &#x2014;
A dictionary of proxy servers to use by protocol or endpoint, e.g., <code>{&apos;http&apos;: &apos;foo.bar:3128&apos;, &apos;http://hostname&apos;: &apos;foo.bar:4012&apos;}</code>. The proxies are used on each request.`,name:"proxies"},{anchor:"transformers.models.auto.auto_factory._BaseAutoModelClass.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.models.auto.auto_factory._BaseAutoModelClass.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.models.auto.auto_factory._BaseAutoModelClass.from_pretrained.revision(str,",description:`<strong>revision(<code>str</code>,</strong> <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a
git-based system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any
identifier allowed by git.`,name:"revision(str,"},{anchor:"transformers.models.auto.auto_factory._BaseAutoModelClass.from_pretrained.trust_remote_code",description:`<strong>trust_remote_code</strong> (<code>bool</code>, <em>optional</em>, defaults 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.models.auto.auto_factory._BaseAutoModelClass.from_pretrained.kwargs",description:`<strong>kwargs</strong> (additional keyword arguments, <em>optional</em>) &#x2014;
Can be used to update the configuration object (after it being loaded) and initiate the model (e.g.,
<code>output_attentions=True</code>). Behaves differently depending on whether a <code>config</code> is provided or
automatically loaded:</p>
<ul>
<li>If a configuration is provided with <code>config</code>, <code>**kwargs</code> will be directly passed to the
underlying model&#x2019;s <code>__init__</code> method (we assume all relevant updates to the configuration have
already been done)</li>
<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_15839/en/main_classes/configuration#transformers.PretrainedConfig.from_pretrained">from_pretrained()</a>). Each key of <code>kwargs</code> that
corresponds to a configuration attribute will be used to override said attribute with the
supplied <code>kwargs</code> value. 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"}]}}),S4=new w({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>)`}}),P4=new X({}),$4=new M({props:{name:"class transformers.AutoModelForPreTraining",anchor:"transformers.AutoModelForPreTraining",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/pr_15839/src/transformers/models/auto/modeling_auto.py#L704"}}),j4=new M({props:{name:"from_config",anchor:"transformers.models.auto.auto_factory._BaseAutoModelClass.from_config",parameters:[{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/pr_15839/src/transformers/models/auto/auto_factory.py#L390",parametersDescription:[{anchor:"transformers.models.auto.auto_factory._BaseAutoModelClass.from_config.config",description:`<strong>config</strong> (<a href="/docs/transformers/pr_15839/en/main_classes/configuration#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_15839/en/model_doc/albert#transformers.AlbertConfig">AlbertConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/albert#transformers.AlbertForPreTraining">AlbertForPreTraining</a> (ALBERT model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/bart#transformers.BartConfig">BartConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/bart#transformers.BartForConditionalGeneration">BartForConditionalGeneration</a> (BART model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/bert#transformers.BertConfig">BertConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/bert#transformers.BertForPreTraining">BertForPreTraining</a> (BERT model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/big_bird#transformers.BigBirdConfig">BigBirdConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/big_bird#transformers.BigBirdForPreTraining">BigBirdForPreTraining</a> (BigBird model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/ctrl#transformers.CTRLConfig">CTRLConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/ctrl#transformers.CTRLLMHeadModel">CTRLLMHeadModel</a> (CTRL model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/camembert#transformers.CamembertConfig">CamembertConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/camembert#transformers.CamembertForMaskedLM">CamembertForMaskedLM</a> (CamemBERT model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/data2vec#transformers.Data2VecTextConfig">Data2VecTextConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/data2vec#transformers.Data2VecTextForMaskedLM">Data2VecTextForMaskedLM</a> (Data2VecText model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/deberta#transformers.DebertaConfig">DebertaConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/deberta#transformers.DebertaForMaskedLM">DebertaForMaskedLM</a> (DeBERTa model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/deberta-v2#transformers.DebertaV2Config">DebertaV2Config</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/deberta-v2#transformers.DebertaV2ForMaskedLM">DebertaV2ForMaskedLM</a> (DeBERTa-v2 model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/distilbert#transformers.DistilBertConfig">DistilBertConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/distilbert#transformers.DistilBertForMaskedLM">DistilBertForMaskedLM</a> (DistilBERT model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/electra#transformers.ElectraConfig">ElectraConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/electra#transformers.ElectraForPreTraining">ElectraForPreTraining</a> (ELECTRA model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/fnet#transformers.FNetConfig">FNetConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/fnet#transformers.FNetForPreTraining">FNetForPreTraining</a> (FNet model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/fsmt#transformers.FSMTConfig">FSMTConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/fsmt#transformers.FSMTForConditionalGeneration">FSMTForConditionalGeneration</a> (FairSeq Machine-Translation model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/flaubert#transformers.FlaubertConfig">FlaubertConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/flaubert#transformers.FlaubertWithLMHeadModel">FlaubertWithLMHeadModel</a> (FlauBERT model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/funnel#transformers.FunnelConfig">FunnelConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/funnel#transformers.FunnelForPreTraining">FunnelForPreTraining</a> (Funnel Transformer model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/gpt2#transformers.GPT2Config">GPT2Config</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/gpt2#transformers.GPT2LMHeadModel">GPT2LMHeadModel</a> (OpenAI GPT-2 model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/ibert#transformers.IBertConfig">IBertConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/ibert#transformers.IBertForMaskedLM">IBertForMaskedLM</a> (I-BERT model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/layoutlm#transformers.LayoutLMConfig">LayoutLMConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/layoutlm#transformers.LayoutLMForMaskedLM">LayoutLMForMaskedLM</a> (LayoutLM model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/longformer#transformers.LongformerConfig">LongformerConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/longformer#transformers.LongformerForMaskedLM">LongformerForMaskedLM</a> (Longformer model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/lxmert#transformers.LxmertConfig">LxmertConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/lxmert#transformers.LxmertForPreTraining">LxmertForPreTraining</a> (LXMERT model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/mpnet#transformers.MPNetConfig">MPNetConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/mpnet#transformers.MPNetForMaskedLM">MPNetForMaskedLM</a> (MPNet model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/megatron-bert#transformers.MegatronBertConfig">MegatronBertConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/megatron-bert#transformers.MegatronBertForPreTraining">MegatronBertForPreTraining</a> (MegatronBert model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/mobilebert#transformers.MobileBertConfig">MobileBertConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/mobilebert#transformers.MobileBertForPreTraining">MobileBertForPreTraining</a> (MobileBERT model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/openai-gpt#transformers.OpenAIGPTConfig">OpenAIGPTConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/openai-gpt#transformers.OpenAIGPTLMHeadModel">OpenAIGPTLMHeadModel</a> (OpenAI GPT model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/retribert#transformers.RetriBertConfig">RetriBertConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/retribert#transformers.RetriBertModel">RetriBertModel</a> (RetriBERT model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/roberta#transformers.RobertaConfig">RobertaConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/roberta#transformers.RobertaForMaskedLM">RobertaForMaskedLM</a> (RoBERTa model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/squeezebert#transformers.SqueezeBertConfig">SqueezeBertConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/squeezebert#transformers.SqueezeBertForMaskedLM">SqueezeBertForMaskedLM</a> (SqueezeBERT model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/t5#transformers.T5Config">T5Config</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/t5#transformers.T5ForConditionalGeneration">T5ForConditionalGeneration</a> (T5 model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/tapas#transformers.TapasConfig">TapasConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/tapas#transformers.TapasForMaskedLM">TapasForMaskedLM</a> (TAPAS model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/transfo-xl#transformers.TransfoXLConfig">TransfoXLConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/transfo-xl#transformers.TransfoXLLMHeadModel">TransfoXLLMHeadModel</a> (Transformer-XL model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/unispeech#transformers.UniSpeechConfig">UniSpeechConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/unispeech#transformers.UniSpeechForPreTraining">UniSpeechForPreTraining</a> (UniSpeech model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/unispeech-sat#transformers.UniSpeechSatConfig">UniSpeechSatConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/unispeech-sat#transformers.UniSpeechSatForPreTraining">UniSpeechSatForPreTraining</a> (UniSpeechSat model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/vit_mae#transformers.ViTMAEConfig">ViTMAEConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/vit_mae#transformers.ViTMAEForPreTraining">ViTMAEForPreTraining</a> (ViTMAE model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/visual_bert#transformers.VisualBertConfig">VisualBertConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/visual_bert#transformers.VisualBertForPreTraining">VisualBertForPreTraining</a> (VisualBert model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/wav2vec2#transformers.Wav2Vec2Config">Wav2Vec2Config</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/wav2vec2#transformers.Wav2Vec2ForPreTraining">Wav2Vec2ForPreTraining</a> (Wav2Vec2 model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/xlm#transformers.XLMConfig">XLMConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/xlm#transformers.XLMWithLMHeadModel">XLMWithLMHeadModel</a> (XLM model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/xlm-roberta#transformers.XLMRobertaConfig">XLMRobertaConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/xlm-roberta#transformers.XLMRobertaForMaskedLM">XLMRobertaForMaskedLM</a> (XLM-RoBERTa model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/xlm-roberta-xl#transformers.XLMRobertaXLConfig">XLMRobertaXLConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/xlm-roberta-xl#transformers.XLMRobertaXLForMaskedLM">XLMRobertaXLForMaskedLM</a> (XLM-RoBERTa-XL model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/xlnet#transformers.XLNetConfig">XLNetConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/xlnet#transformers.XLNetLMHeadModel">XLNetLMHeadModel</a> (XLNet model)</li>
</ul>`,name:"config"}]}}),D4=new w({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)`}}),N4=new M({props:{name:"from_pretrained",anchor:"transformers.models.auto.auto_factory._BaseAutoModelClass.from_pretrained",parameters:[{name:"*model_args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/pr_15839/src/transformers/models/auto/auto_factory.py#L418",parametersDescription:[{anchor:"transformers.models.auto.auto_factory._BaseAutoModelClass.from_pretrained.pretrained_model_name_or_path",description:`<strong>pretrained_model_name_or_path</strong> (<code>str</code> or <code>os.PathLike</code>) &#x2014;
Can be either:</p>
<ul>
<li>A string, the <em>model id</em> of a pretrained model hosted inside a model repo on huggingface.co.
Valid model ids can be located at the root-level, like <code>bert-base-uncased</code>, or namespaced under a
user or organization name, like <code>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_15839/en/main_classes/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.models.auto.auto_factory._BaseAutoModelClass.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.models.auto.auto_factory._BaseAutoModelClass.from_pretrained.config",description:`<strong>config</strong> (<a href="/docs/transformers/pr_15839/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>, <em>optional</em>) &#x2014;
Configuration for the model to use instead of an automatically loaded configuration. Configuration can
be automatically loaded when:</p>
<ul>
<li>The model is a model provided by the 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_15839/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and is reloaded by supplying the
save directory.</li>
<li>The model is loaded by supplying a local directory 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.models.auto.auto_factory._BaseAutoModelClass.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_15839/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and
<a href="/docs/transformers/pr_15839/en/main_classes/model#transformers.PreTrainedModel.from_pretrained">from_pretrained()</a> is not a simpler option.`,name:"state_dict"},{anchor:"transformers.models.auto.auto_factory._BaseAutoModelClass.from_pretrained.cache_dir",description:`<strong>cache_dir</strong> (<code>str</code> or <code>os.PathLike</code>, <em>optional</em>) &#x2014;
Path to a directory in which a downloaded pretrained model configuration should be cached if the
standard cache should not be used.`,name:"cache_dir"},{anchor:"transformers.models.auto.auto_factory._BaseAutoModelClass.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.models.auto.auto_factory._BaseAutoModelClass.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.models.auto.auto_factory._BaseAutoModelClass.from_pretrained.resume_download",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.models.auto.auto_factory._BaseAutoModelClass.from_pretrained.proxies",description:`<strong>proxies</strong> (<code>Dict[str, str]</code>, <em>optional</em>) &#x2014;
A dictionary of proxy servers to use by protocol or endpoint, e.g., <code>{&apos;http&apos;: &apos;foo.bar:3128&apos;, &apos;http://hostname&apos;: &apos;foo.bar:4012&apos;}</code>. The proxies are used on each request.`,name:"proxies"},{anchor:"transformers.models.auto.auto_factory._BaseAutoModelClass.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.models.auto.auto_factory._BaseAutoModelClass.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.models.auto.auto_factory._BaseAutoModelClass.from_pretrained.revision(str,",description:`<strong>revision(<code>str</code>,</strong> <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a
git-based system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any
identifier allowed by git.`,name:"revision(str,"},{anchor:"transformers.models.auto.auto_factory._BaseAutoModelClass.from_pretrained.trust_remote_code",description:`<strong>trust_remote_code</strong> (<code>bool</code>, <em>optional</em>, defaults 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.models.auto.auto_factory._BaseAutoModelClass.from_pretrained.kwargs",description:`<strong>kwargs</strong> (additional keyword arguments, <em>optional</em>) &#x2014;
Can be used to update the configuration object (after it being loaded) and initiate the model (e.g.,
<code>output_attentions=True</code>). Behaves differently depending on whether a <code>config</code> is provided or
automatically loaded:</p>
<ul>
<li>If a configuration is provided with <code>config</code>, <code>**kwargs</code> will be directly passed to the
underlying model&#x2019;s <code>__init__</code> method (we assume all relevant updates to the configuration have
already been done)</li>
<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_15839/en/main_classes/configuration#transformers.PretrainedConfig.from_pretrained">from_pretrained()</a>). Each key of <code>kwargs</code> that
corresponds to a configuration attribute will be used to override said attribute with the
supplied <code>kwargs</code> value. 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"}]}}),q4=new w({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>)`}}),O4=new X({}),G4=new M({props:{name:"class transformers.AutoModelForCausalLM",anchor:"transformers.AutoModelForCausalLM",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/pr_15839/src/transformers/models/auto/modeling_auto.py#L719"}}),V4=new M({props:{name:"from_config",anchor:"transformers.models.auto.auto_factory._BaseAutoModelClass.from_config",parameters:[{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/pr_15839/src/transformers/models/auto/auto_factory.py#L390",parametersDescription:[{anchor:"transformers.models.auto.auto_factory._BaseAutoModelClass.from_config.config",description:`<strong>config</strong> (<a href="/docs/transformers/pr_15839/en/main_classes/configuration#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_15839/en/model_doc/bart#transformers.BartConfig">BartConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/bart#transformers.BartForCausalLM">BartForCausalLM</a> (BART model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/bert#transformers.BertConfig">BertConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/bert#transformers.BertLMHeadModel">BertLMHeadModel</a> (BERT model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/bert-generation#transformers.BertGenerationConfig">BertGenerationConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/bert-generation#transformers.BertGenerationDecoder">BertGenerationDecoder</a> (Bert Generation model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/big_bird#transformers.BigBirdConfig">BigBirdConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/big_bird#transformers.BigBirdForCausalLM">BigBirdForCausalLM</a> (BigBird model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/bigbird_pegasus#transformers.BigBirdPegasusConfig">BigBirdPegasusConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/bigbird_pegasus#transformers.BigBirdPegasusForCausalLM">BigBirdPegasusForCausalLM</a> (BigBirdPegasus model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/blenderbot#transformers.BlenderbotConfig">BlenderbotConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/blenderbot#transformers.BlenderbotForCausalLM">BlenderbotForCausalLM</a> (Blenderbot model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/blenderbot-small#transformers.BlenderbotSmallConfig">BlenderbotSmallConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/blenderbot-small#transformers.BlenderbotSmallForCausalLM">BlenderbotSmallForCausalLM</a> (BlenderbotSmall model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/ctrl#transformers.CTRLConfig">CTRLConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/ctrl#transformers.CTRLLMHeadModel">CTRLLMHeadModel</a> (CTRL model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/camembert#transformers.CamembertConfig">CamembertConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/camembert#transformers.CamembertForCausalLM">CamembertForCausalLM</a> (CamemBERT model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/data2vec#transformers.Data2VecTextConfig">Data2VecTextConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/data2vec#transformers.Data2VecTextForCausalLM">Data2VecTextForCausalLM</a> (Data2VecText model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/electra#transformers.ElectraConfig">ElectraConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/electra#transformers.ElectraForCausalLM">ElectraForCausalLM</a> (ELECTRA model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/gpt2#transformers.GPT2Config">GPT2Config</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/gpt2#transformers.GPT2LMHeadModel">GPT2LMHeadModel</a> (OpenAI GPT-2 model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/gptj#transformers.GPTJConfig">GPTJConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/gptj#transformers.GPTJForCausalLM">GPTJForCausalLM</a> (GPT-J model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/gpt_neo#transformers.GPTNeoConfig">GPTNeoConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/gpt_neo#transformers.GPTNeoForCausalLM">GPTNeoForCausalLM</a> (GPT Neo model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/mbart#transformers.MBartConfig">MBartConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/mbart#transformers.MBartForCausalLM">MBartForCausalLM</a> (mBART model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/marian#transformers.MarianConfig">MarianConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/marian#transformers.MarianForCausalLM">MarianForCausalLM</a> (Marian model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/megatron-bert#transformers.MegatronBertConfig">MegatronBertConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/megatron-bert#transformers.MegatronBertForCausalLM">MegatronBertForCausalLM</a> (MegatronBert model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/openai-gpt#transformers.OpenAIGPTConfig">OpenAIGPTConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/openai-gpt#transformers.OpenAIGPTLMHeadModel">OpenAIGPTLMHeadModel</a> (OpenAI GPT model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/plbart#transformers.PLBartConfig">PLBartConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/plbart#transformers.PLBartForCausalLM">PLBartForCausalLM</a> (PLBart model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/pegasus#transformers.PegasusConfig">PegasusConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/pegasus#transformers.PegasusForCausalLM">PegasusForCausalLM</a> (Pegasus model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/prophetnet#transformers.ProphetNetConfig">ProphetNetConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/prophetnet#transformers.ProphetNetForCausalLM">ProphetNetForCausalLM</a> (ProphetNet model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/qdqbert#transformers.QDQBertConfig">QDQBertConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/qdqbert#transformers.QDQBertLMHeadModel">QDQBertLMHeadModel</a> (QDQBert model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/reformer#transformers.ReformerConfig">ReformerConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/reformer#transformers.ReformerModelWithLMHead">ReformerModelWithLMHead</a> (Reformer model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/rembert#transformers.RemBertConfig">RemBertConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/rembert#transformers.RemBertForCausalLM">RemBertForCausalLM</a> (RemBERT model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/roformer#transformers.RoFormerConfig">RoFormerConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/roformer#transformers.RoFormerForCausalLM">RoFormerForCausalLM</a> (RoFormer model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/roberta#transformers.RobertaConfig">RobertaConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/roberta#transformers.RobertaForCausalLM">RobertaForCausalLM</a> (RoBERTa model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/speech_to_text_2#transformers.Speech2Text2Config">Speech2Text2Config</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/speech_to_text_2#transformers.Speech2Text2ForCausalLM">Speech2Text2ForCausalLM</a> (Speech2Text2 model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/trocr#transformers.TrOCRConfig">TrOCRConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/trocr#transformers.TrOCRForCausalLM">TrOCRForCausalLM</a> (TrOCR model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/transfo-xl#transformers.TransfoXLConfig">TransfoXLConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/transfo-xl#transformers.TransfoXLLMHeadModel">TransfoXLLMHeadModel</a> (Transformer-XL model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/xglm#transformers.XGLMConfig">XGLMConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/xglm#transformers.XGLMForCausalLM">XGLMForCausalLM</a> (XGLM model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/xlm#transformers.XLMConfig">XLMConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/xlm#transformers.XLMWithLMHeadModel">XLMWithLMHeadModel</a> (XLM model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/xlm-prophetnet#transformers.XLMProphetNetConfig">XLMProphetNetConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/xlm-prophetnet#transformers.XLMProphetNetForCausalLM">XLMProphetNetForCausalLM</a> (XLMProphetNet model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/xlm-roberta#transformers.XLMRobertaConfig">XLMRobertaConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/xlm-roberta#transformers.XLMRobertaForCausalLM">XLMRobertaForCausalLM</a> (XLM-RoBERTa model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/xlm-roberta-xl#transformers.XLMRobertaXLConfig">XLMRobertaXLConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/xlm-roberta-xl#transformers.XLMRobertaXLForCausalLM">XLMRobertaXLForCausalLM</a> (XLM-RoBERTa-XL model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/xlnet#transformers.XLNetConfig">XLNetConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/xlnet#transformers.XLNetLMHeadModel">XLNetLMHeadModel</a> (XLNet model)</li>
</ul>`,name:"config"}]}}),z4=new w({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)`}}),W4=new M({props:{name:"from_pretrained",anchor:"transformers.models.auto.auto_factory._BaseAutoModelClass.from_pretrained",parameters:[{name:"*model_args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/pr_15839/src/transformers/models/auto/auto_factory.py#L418",parametersDescription:[{anchor:"transformers.models.auto.auto_factory._BaseAutoModelClass.from_pretrained.pretrained_model_name_or_path",description:`<strong>pretrained_model_name_or_path</strong> (<code>str</code> or <code>os.PathLike</code>) &#x2014;
Can be either:</p>
<ul>
<li>A string, the <em>model id</em> of a pretrained model hosted inside a model repo on huggingface.co.
Valid model ids can be located at the root-level, like <code>bert-base-uncased</code>, or namespaced under a
user or organization name, like <code>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_15839/en/main_classes/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.models.auto.auto_factory._BaseAutoModelClass.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.models.auto.auto_factory._BaseAutoModelClass.from_pretrained.config",description:`<strong>config</strong> (<a href="/docs/transformers/pr_15839/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>, <em>optional</em>) &#x2014;
Configuration for the model to use instead of an automatically loaded configuration. Configuration can
be automatically loaded when:</p>
<ul>
<li>The model is a model provided by the 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_15839/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and is reloaded by supplying the
save directory.</li>
<li>The model is loaded by supplying a local directory 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.models.auto.auto_factory._BaseAutoModelClass.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_15839/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and
<a href="/docs/transformers/pr_15839/en/main_classes/model#transformers.PreTrainedModel.from_pretrained">from_pretrained()</a> is not a simpler option.`,name:"state_dict"},{anchor:"transformers.models.auto.auto_factory._BaseAutoModelClass.from_pretrained.cache_dir",description:`<strong>cache_dir</strong> (<code>str</code> or <code>os.PathLike</code>, <em>optional</em>) &#x2014;
Path to a directory in which a downloaded pretrained model configuration should be cached if the
standard cache should not be used.`,name:"cache_dir"},{anchor:"transformers.models.auto.auto_factory._BaseAutoModelClass.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.models.auto.auto_factory._BaseAutoModelClass.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.models.auto.auto_factory._BaseAutoModelClass.from_pretrained.resume_download",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.models.auto.auto_factory._BaseAutoModelClass.from_pretrained.proxies",description:`<strong>proxies</strong> (<code>Dict[str, str]</code>, <em>optional</em>) &#x2014;
A dictionary of proxy servers to use by protocol or endpoint, e.g., <code>{&apos;http&apos;: &apos;foo.bar:3128&apos;, &apos;http://hostname&apos;: &apos;foo.bar:4012&apos;}</code>. The proxies are used on each request.`,name:"proxies"},{anchor:"transformers.models.auto.auto_factory._BaseAutoModelClass.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.models.auto.auto_factory._BaseAutoModelClass.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.models.auto.auto_factory._BaseAutoModelClass.from_pretrained.revision(str,",description:`<strong>revision(<code>str</code>,</strong> <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a
git-based system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any
identifier allowed by git.`,name:"revision(str,"},{anchor:"transformers.models.auto.auto_factory._BaseAutoModelClass.from_pretrained.trust_remote_code",description:`<strong>trust_remote_code</strong> (<code>bool</code>, <em>optional</em>, defaults 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.models.auto.auto_factory._BaseAutoModelClass.from_pretrained.kwargs",description:`<strong>kwargs</strong> (additional keyword arguments, <em>optional</em>) &#x2014;
Can be used to update the configuration object (after it being loaded) and initiate the model (e.g.,
<code>output_attentions=True</code>). Behaves differently depending on whether a <code>config</code> is provided or
automatically loaded:</p>
<ul>
<li>If a configuration is provided with <code>config</code>, <code>**kwargs</code> will be directly passed to the
underlying model&#x2019;s <code>__init__</code> method (we assume all relevant updates to the configuration have
already been done)</li>
<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_15839/en/main_classes/configuration#transformers.PretrainedConfig.from_pretrained">from_pretrained()</a>). Each key of <code>kwargs</code> that
corresponds to a configuration attribute will be used to override said attribute with the
supplied <code>kwargs</code> value. 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"}]}}),Q4=new w({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>)`}}),H4=new X({}),U4=new M({props:{name:"class transformers.AutoModelForMaskedLM",anchor:"transformers.AutoModelForMaskedLM",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/pr_15839/src/transformers/models/auto/modeling_auto.py#L726"}}),Y4=new M({props:{name:"from_config",anchor:"transformers.models.auto.auto_factory._BaseAutoModelClass.from_config",parameters:[{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/pr_15839/src/transformers/models/auto/auto_factory.py#L390",parametersDescription:[{anchor:"transformers.models.auto.auto_factory._BaseAutoModelClass.from_config.config",description:`<strong>config</strong> (<a href="/docs/transformers/pr_15839/en/main_classes/configuration#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_15839/en/model_doc/albert#transformers.AlbertConfig">AlbertConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/albert#transformers.AlbertForMaskedLM">AlbertForMaskedLM</a> (ALBERT model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/bart#transformers.BartConfig">BartConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/bart#transformers.BartForConditionalGeneration">BartForConditionalGeneration</a> (BART model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/bert#transformers.BertConfig">BertConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/bert#transformers.BertForMaskedLM">BertForMaskedLM</a> (BERT model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/big_bird#transformers.BigBirdConfig">BigBirdConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/big_bird#transformers.BigBirdForMaskedLM">BigBirdForMaskedLM</a> (BigBird model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/camembert#transformers.CamembertConfig">CamembertConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/camembert#transformers.CamembertForMaskedLM">CamembertForMaskedLM</a> (CamemBERT model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/convbert#transformers.ConvBertConfig">ConvBertConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/convbert#transformers.ConvBertForMaskedLM">ConvBertForMaskedLM</a> (ConvBERT model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/data2vec#transformers.Data2VecTextConfig">Data2VecTextConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/data2vec#transformers.Data2VecTextForMaskedLM">Data2VecTextForMaskedLM</a> (Data2VecText model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/deberta#transformers.DebertaConfig">DebertaConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/deberta#transformers.DebertaForMaskedLM">DebertaForMaskedLM</a> (DeBERTa model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/deberta-v2#transformers.DebertaV2Config">DebertaV2Config</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/deberta-v2#transformers.DebertaV2ForMaskedLM">DebertaV2ForMaskedLM</a> (DeBERTa-v2 model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/distilbert#transformers.DistilBertConfig">DistilBertConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/distilbert#transformers.DistilBertForMaskedLM">DistilBertForMaskedLM</a> (DistilBERT model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/electra#transformers.ElectraConfig">ElectraConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/electra#transformers.ElectraForMaskedLM">ElectraForMaskedLM</a> (ELECTRA model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/fnet#transformers.FNetConfig">FNetConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/fnet#transformers.FNetForMaskedLM">FNetForMaskedLM</a> (FNet model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/flaubert#transformers.FlaubertConfig">FlaubertConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/flaubert#transformers.FlaubertWithLMHeadModel">FlaubertWithLMHeadModel</a> (FlauBERT model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/funnel#transformers.FunnelConfig">FunnelConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/funnel#transformers.FunnelForMaskedLM">FunnelForMaskedLM</a> (Funnel Transformer model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/ibert#transformers.IBertConfig">IBertConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/ibert#transformers.IBertForMaskedLM">IBertForMaskedLM</a> (I-BERT model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/layoutlm#transformers.LayoutLMConfig">LayoutLMConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/layoutlm#transformers.LayoutLMForMaskedLM">LayoutLMForMaskedLM</a> (LayoutLM model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/longformer#transformers.LongformerConfig">LongformerConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/longformer#transformers.LongformerForMaskedLM">LongformerForMaskedLM</a> (Longformer model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/mbart#transformers.MBartConfig">MBartConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/mbart#transformers.MBartForConditionalGeneration">MBartForConditionalGeneration</a> (mBART model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/mpnet#transformers.MPNetConfig">MPNetConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/mpnet#transformers.MPNetForMaskedLM">MPNetForMaskedLM</a> (MPNet model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/megatron-bert#transformers.MegatronBertConfig">MegatronBertConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/megatron-bert#transformers.MegatronBertForMaskedLM">MegatronBertForMaskedLM</a> (MegatronBert model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/mobilebert#transformers.MobileBertConfig">MobileBertConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/mobilebert#transformers.MobileBertForMaskedLM">MobileBertForMaskedLM</a> (MobileBERT model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/nystromformer#transformers.NystromformerConfig">NystromformerConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/nystromformer#transformers.NystromformerForMaskedLM">NystromformerForMaskedLM</a> (Nystromformer model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/perceiver#transformers.PerceiverConfig">PerceiverConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/perceiver#transformers.PerceiverForMaskedLM">PerceiverForMaskedLM</a> (Perceiver model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/qdqbert#transformers.QDQBertConfig">QDQBertConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/qdqbert#transformers.QDQBertForMaskedLM">QDQBertForMaskedLM</a> (QDQBert model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/reformer#transformers.ReformerConfig">ReformerConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/reformer#transformers.ReformerForMaskedLM">ReformerForMaskedLM</a> (Reformer model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/rembert#transformers.RemBertConfig">RemBertConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/rembert#transformers.RemBertForMaskedLM">RemBertForMaskedLM</a> (RemBERT model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/roformer#transformers.RoFormerConfig">RoFormerConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/roformer#transformers.RoFormerForMaskedLM">RoFormerForMaskedLM</a> (RoFormer model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/roberta#transformers.RobertaConfig">RobertaConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/roberta#transformers.RobertaForMaskedLM">RobertaForMaskedLM</a> (RoBERTa model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/squeezebert#transformers.SqueezeBertConfig">SqueezeBertConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/squeezebert#transformers.SqueezeBertForMaskedLM">SqueezeBertForMaskedLM</a> (SqueezeBERT model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/tapas#transformers.TapasConfig">TapasConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/tapas#transformers.TapasForMaskedLM">TapasForMaskedLM</a> (TAPAS model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/wav2vec2#transformers.Wav2Vec2Config">Wav2Vec2Config</a> configuration class: <code>Wav2Vec2ForMaskedLM</code>(Wav2Vec2 model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/xlm#transformers.XLMConfig">XLMConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/xlm#transformers.XLMWithLMHeadModel">XLMWithLMHeadModel</a> (XLM model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/xlm-roberta#transformers.XLMRobertaConfig">XLMRobertaConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/xlm-roberta#transformers.XLMRobertaForMaskedLM">XLMRobertaForMaskedLM</a> (XLM-RoBERTa model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/xlm-roberta-xl#transformers.XLMRobertaXLConfig">XLMRobertaXLConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/xlm-roberta-xl#transformers.XLMRobertaXLForMaskedLM">XLMRobertaXLForMaskedLM</a> (XLM-RoBERTa-XL model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/yoso#transformers.YosoConfig">YosoConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/yoso#transformers.YosoForMaskedLM">YosoForMaskedLM</a> (YOSO model)</li>
</ul>`,name:"config"}]}}),K4=new w({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)`}}),Z4=new M({props:{name:"from_pretrained",anchor:"transformers.models.auto.auto_factory._BaseAutoModelClass.from_pretrained",parameters:[{name:"*model_args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/pr_15839/src/transformers/models/auto/auto_factory.py#L418",parametersDescription:[{anchor:"transformers.models.auto.auto_factory._BaseAutoModelClass.from_pretrained.pretrained_model_name_or_path",description:`<strong>pretrained_model_name_or_path</strong> (<code>str</code> or <code>os.PathLike</code>) &#x2014;
Can be either:</p>
<ul>
<li>A string, the <em>model id</em> of a pretrained model hosted inside a model repo on huggingface.co.
Valid model ids can be located at the root-level, like <code>bert-base-uncased</code>, or namespaced under a
user or organization name, like <code>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_15839/en/main_classes/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.models.auto.auto_factory._BaseAutoModelClass.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.models.auto.auto_factory._BaseAutoModelClass.from_pretrained.config",description:`<strong>config</strong> (<a href="/docs/transformers/pr_15839/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>, <em>optional</em>) &#x2014;
Configuration for the model to use instead of an automatically loaded configuration. Configuration can
be automatically loaded when:</p>
<ul>
<li>The model is a model provided by the 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_15839/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and is reloaded by supplying the
save directory.</li>
<li>The model is loaded by supplying a local directory 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.models.auto.auto_factory._BaseAutoModelClass.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_15839/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and
<a href="/docs/transformers/pr_15839/en/main_classes/model#transformers.PreTrainedModel.from_pretrained">from_pretrained()</a> is not a simpler option.`,name:"state_dict"},{anchor:"transformers.models.auto.auto_factory._BaseAutoModelClass.from_pretrained.cache_dir",description:`<strong>cache_dir</strong> (<code>str</code> or <code>os.PathLike</code>, <em>optional</em>) &#x2014;
Path to a directory in which a downloaded pretrained model configuration should be cached if the
standard cache should not be used.`,name:"cache_dir"},{anchor:"transformers.models.auto.auto_factory._BaseAutoModelClass.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.models.auto.auto_factory._BaseAutoModelClass.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.models.auto.auto_factory._BaseAutoModelClass.from_pretrained.resume_download",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.models.auto.auto_factory._BaseAutoModelClass.from_pretrained.proxies",description:`<strong>proxies</strong> (<code>Dict[str, str]</code>, <em>optional</em>) &#x2014;
A dictionary of proxy servers to use by protocol or endpoint, e.g., <code>{&apos;http&apos;: &apos;foo.bar:3128&apos;, &apos;http://hostname&apos;: &apos;foo.bar:4012&apos;}</code>. The proxies are used on each request.`,name:"proxies"},{anchor:"transformers.models.auto.auto_factory._BaseAutoModelClass.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.models.auto.auto_factory._BaseAutoModelClass.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.models.auto.auto_factory._BaseAutoModelClass.from_pretrained.revision(str,",description:`<strong>revision(<code>str</code>,</strong> <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a
git-based system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any
identifier allowed by git.`,name:"revision(str,"},{anchor:"transformers.models.auto.auto_factory._BaseAutoModelClass.from_pretrained.trust_remote_code",description:`<strong>trust_remote_code</strong> (<code>bool</code>, <em>optional</em>, defaults 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.models.auto.auto_factory._BaseAutoModelClass.from_pretrained.kwargs",description:`<strong>kwargs</strong> (additional keyword arguments, <em>optional</em>) &#x2014;
Can be used to update the configuration object (after it being loaded) and initiate the model (e.g.,
<code>output_attentions=True</code>). Behaves differently depending on whether a <code>config</code> is provided or
automatically loaded:</p>
<ul>
<li>If a configuration is provided with <code>config</code>, <code>**kwargs</code> will be directly passed to the
underlying model&#x2019;s <code>__init__</code> method (we assume all relevant updates to the configuration have
already been done)</li>
<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_15839/en/main_classes/configuration#transformers.PretrainedConfig.from_pretrained">from_pretrained()</a>). Each key of <code>kwargs</code> that
corresponds to a configuration attribute will be used to override said attribute with the
supplied <code>kwargs</code> value. 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"}]}}),eE=new w({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>)`}}),oE=new X({}),rE=new M({props:{name:"class transformers.AutoModelForSeq2SeqLM",anchor:"transformers.AutoModelForSeq2SeqLM",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/pr_15839/src/transformers/models/auto/modeling_auto.py#L733"}}),aE=new M({props:{name:"from_config",anchor:"transformers.models.auto.auto_factory._BaseAutoModelClass.from_config",parameters:[{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/pr_15839/src/transformers/models/auto/auto_factory.py#L390",parametersDescription:[{anchor:"transformers.models.auto.auto_factory._BaseAutoModelClass.from_config.config",description:`<strong>config</strong> (<a href="/docs/transformers/pr_15839/en/main_classes/configuration#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_15839/en/model_doc/bart#transformers.BartConfig">BartConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/bart#transformers.BartForConditionalGeneration">BartForConditionalGeneration</a> (BART model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/bigbird_pegasus#transformers.BigBirdPegasusConfig">BigBirdPegasusConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/bigbird_pegasus#transformers.BigBirdPegasusForConditionalGeneration">BigBirdPegasusForConditionalGeneration</a> (BigBirdPegasus model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/blenderbot#transformers.BlenderbotConfig">BlenderbotConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/blenderbot#transformers.BlenderbotForConditionalGeneration">BlenderbotForConditionalGeneration</a> (Blenderbot model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/blenderbot-small#transformers.BlenderbotSmallConfig">BlenderbotSmallConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/blenderbot-small#transformers.BlenderbotSmallForConditionalGeneration">BlenderbotSmallForConditionalGeneration</a> (BlenderbotSmall model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/encoder-decoder#transformers.EncoderDecoderConfig">EncoderDecoderConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/encoder-decoder#transformers.EncoderDecoderModel">EncoderDecoderModel</a> (Encoder decoder model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/fsmt#transformers.FSMTConfig">FSMTConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/fsmt#transformers.FSMTForConditionalGeneration">FSMTForConditionalGeneration</a> (FairSeq Machine-Translation model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/led#transformers.LEDConfig">LEDConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/led#transformers.LEDForConditionalGeneration">LEDForConditionalGeneration</a> (LED model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/m2m_100#transformers.M2M100Config">M2M100Config</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/m2m_100#transformers.M2M100ForConditionalGeneration">M2M100ForConditionalGeneration</a> (M2M100 model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/mbart#transformers.MBartConfig">MBartConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/mbart#transformers.MBartForConditionalGeneration">MBartForConditionalGeneration</a> (mBART model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/mt5#transformers.MT5Config">MT5Config</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/mt5#transformers.MT5ForConditionalGeneration">MT5ForConditionalGeneration</a> (mT5 model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/marian#transformers.MarianConfig">MarianConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/marian#transformers.MarianMTModel">MarianMTModel</a> (Marian model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/plbart#transformers.PLBartConfig">PLBartConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/plbart#transformers.PLBartForConditionalGeneration">PLBartForConditionalGeneration</a> (PLBart model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/pegasus#transformers.PegasusConfig">PegasusConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/pegasus#transformers.PegasusForConditionalGeneration">PegasusForConditionalGeneration</a> (Pegasus model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/prophetnet#transformers.ProphetNetConfig">ProphetNetConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/prophetnet#transformers.ProphetNetForConditionalGeneration">ProphetNetForConditionalGeneration</a> (ProphetNet model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/t5#transformers.T5Config">T5Config</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/t5#transformers.T5ForConditionalGeneration">T5ForConditionalGeneration</a> (T5 model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/xlm-prophetnet#transformers.XLMProphetNetConfig">XLMProphetNetConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/xlm-prophetnet#transformers.XLMProphetNetForConditionalGeneration">XLMProphetNetForConditionalGeneration</a> (XLMProphetNet model)</li>
</ul>`,name:"config"}]}}),nE=new w({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)`}}),sE=new M({props:{name:"from_pretrained",anchor:"transformers.models.auto.auto_factory._BaseAutoModelClass.from_pretrained",parameters:[{name:"*model_args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/pr_15839/src/transformers/models/auto/auto_factory.py#L418",parametersDescription:[{anchor:"transformers.models.auto.auto_factory._BaseAutoModelClass.from_pretrained.pretrained_model_name_or_path",description:`<strong>pretrained_model_name_or_path</strong> (<code>str</code> or <code>os.PathLike</code>) &#x2014;
Can be either:</p>
<ul>
<li>A string, the <em>model id</em> of a pretrained model hosted inside a model repo on huggingface.co.
Valid model ids can be located at the root-level, like <code>bert-base-uncased</code>, or namespaced under a
user or organization name, like <code>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_15839/en/main_classes/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.models.auto.auto_factory._BaseAutoModelClass.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.models.auto.auto_factory._BaseAutoModelClass.from_pretrained.config",description:`<strong>config</strong> (<a href="/docs/transformers/pr_15839/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>, <em>optional</em>) &#x2014;
Configuration for the model to use instead of an automatically loaded configuration. Configuration can
be automatically loaded when:</p>
<ul>
<li>The model is a model provided by the 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_15839/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and is reloaded by supplying the
save directory.</li>
<li>The model is loaded by supplying a local directory 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.models.auto.auto_factory._BaseAutoModelClass.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_15839/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and
<a href="/docs/transformers/pr_15839/en/main_classes/model#transformers.PreTrainedModel.from_pretrained">from_pretrained()</a> is not a simpler option.`,name:"state_dict"},{anchor:"transformers.models.auto.auto_factory._BaseAutoModelClass.from_pretrained.cache_dir",description:`<strong>cache_dir</strong> (<code>str</code> or <code>os.PathLike</code>, <em>optional</em>) &#x2014;
Path to a directory in which a downloaded pretrained model configuration should be cached if the
standard cache should not be used.`,name:"cache_dir"},{anchor:"transformers.models.auto.auto_factory._BaseAutoModelClass.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.models.auto.auto_factory._BaseAutoModelClass.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.models.auto.auto_factory._BaseAutoModelClass.from_pretrained.resume_download",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.models.auto.auto_factory._BaseAutoModelClass.from_pretrained.proxies",description:`<strong>proxies</strong> (<code>Dict[str, str]</code>, <em>optional</em>) &#x2014;
A dictionary of proxy servers to use by protocol or endpoint, e.g., <code>{&apos;http&apos;: &apos;foo.bar:3128&apos;, &apos;http://hostname&apos;: &apos;foo.bar:4012&apos;}</code>. The proxies are used on each request.`,name:"proxies"},{anchor:"transformers.models.auto.auto_factory._BaseAutoModelClass.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.models.auto.auto_factory._BaseAutoModelClass.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.models.auto.auto_factory._BaseAutoModelClass.from_pretrained.revision(str,",description:`<strong>revision(<code>str</code>,</strong> <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a
git-based system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any
identifier allowed by git.`,name:"revision(str,"},{anchor:"transformers.models.auto.auto_factory._BaseAutoModelClass.from_pretrained.trust_remote_code",description:`<strong>trust_remote_code</strong> (<code>bool</code>, <em>optional</em>, defaults 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.models.auto.auto_factory._BaseAutoModelClass.from_pretrained.kwargs",description:`<strong>kwargs</strong> (additional keyword arguments, <em>optional</em>) &#x2014;
Can be used to update the configuration object (after it being loaded) and initiate the model (e.g.,
<code>output_attentions=True</code>). Behaves differently depending on whether a <code>config</code> is provided or
automatically loaded:</p>
<ul>
<li>If a configuration is provided with <code>config</code>, <code>**kwargs</code> will be directly passed to the
underlying model&#x2019;s <code>__init__</code> method (we assume all relevant updates to the configuration have
already been done)</li>
<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_15839/en/main_classes/configuration#transformers.PretrainedConfig.from_pretrained">from_pretrained()</a>). Each key of <code>kwargs</code> that
corresponds to a configuration attribute will be used to override said attribute with the
supplied <code>kwargs</code> value. 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"}]}}),lE=new w({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>)`}}),iE=new X({}),dE=new M({props:{name:"class transformers.AutoModelForSequenceClassification",anchor:"transformers.AutoModelForSequenceClassification",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/pr_15839/src/transformers/models/auto/modeling_auto.py#L742"}}),fE=new M({props:{name:"from_config",anchor:"transformers.models.auto.auto_factory._BaseAutoModelClass.from_config",parameters:[{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/pr_15839/src/transformers/models/auto/auto_factory.py#L390",parametersDescription:[{anchor:"transformers.models.auto.auto_factory._BaseAutoModelClass.from_config.config",description:`<strong>config</strong> (<a href="/docs/transformers/pr_15839/en/main_classes/configuration#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_15839/en/model_doc/albert#transformers.AlbertConfig">AlbertConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/albert#transformers.AlbertForSequenceClassification">AlbertForSequenceClassification</a> (ALBERT model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/bart#transformers.BartConfig">BartConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/bart#transformers.BartForSequenceClassification">BartForSequenceClassification</a> (BART model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/bert#transformers.BertConfig">BertConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/bert#transformers.BertForSequenceClassification">BertForSequenceClassification</a> (BERT model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/big_bird#transformers.BigBirdConfig">BigBirdConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/big_bird#transformers.BigBirdForSequenceClassification">BigBirdForSequenceClassification</a> (BigBird model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/bigbird_pegasus#transformers.BigBirdPegasusConfig">BigBirdPegasusConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/bigbird_pegasus#transformers.BigBirdPegasusForSequenceClassification">BigBirdPegasusForSequenceClassification</a> (BigBirdPegasus model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/ctrl#transformers.CTRLConfig">CTRLConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/ctrl#transformers.CTRLForSequenceClassification">CTRLForSequenceClassification</a> (CTRL model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/camembert#transformers.CamembertConfig">CamembertConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/camembert#transformers.CamembertForSequenceClassification">CamembertForSequenceClassification</a> (CamemBERT model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/canine#transformers.CanineConfig">CanineConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/canine#transformers.CanineForSequenceClassification">CanineForSequenceClassification</a> (Canine model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/convbert#transformers.ConvBertConfig">ConvBertConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/convbert#transformers.ConvBertForSequenceClassification">ConvBertForSequenceClassification</a> (ConvBERT model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/data2vec#transformers.Data2VecTextConfig">Data2VecTextConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/data2vec#transformers.Data2VecTextForSequenceClassification">Data2VecTextForSequenceClassification</a> (Data2VecText model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/deberta#transformers.DebertaConfig">DebertaConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/deberta#transformers.DebertaForSequenceClassification">DebertaForSequenceClassification</a> (DeBERTa model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/deberta-v2#transformers.DebertaV2Config">DebertaV2Config</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/deberta-v2#transformers.DebertaV2ForSequenceClassification">DebertaV2ForSequenceClassification</a> (DeBERTa-v2 model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/distilbert#transformers.DistilBertConfig">DistilBertConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/distilbert#transformers.DistilBertForSequenceClassification">DistilBertForSequenceClassification</a> (DistilBERT model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/electra#transformers.ElectraConfig">ElectraConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/electra#transformers.ElectraForSequenceClassification">ElectraForSequenceClassification</a> (ELECTRA model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/fnet#transformers.FNetConfig">FNetConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/fnet#transformers.FNetForSequenceClassification">FNetForSequenceClassification</a> (FNet model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/flaubert#transformers.FlaubertConfig">FlaubertConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/flaubert#transformers.FlaubertForSequenceClassification">FlaubertForSequenceClassification</a> (FlauBERT model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/funnel#transformers.FunnelConfig">FunnelConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/funnel#transformers.FunnelForSequenceClassification">FunnelForSequenceClassification</a> (Funnel Transformer model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/gpt2#transformers.GPT2Config">GPT2Config</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/gpt2#transformers.GPT2ForSequenceClassification">GPT2ForSequenceClassification</a> (OpenAI GPT-2 model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/gptj#transformers.GPTJConfig">GPTJConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/gptj#transformers.GPTJForSequenceClassification">GPTJForSequenceClassification</a> (GPT-J model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/gpt_neo#transformers.GPTNeoConfig">GPTNeoConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/gpt_neo#transformers.GPTNeoForSequenceClassification">GPTNeoForSequenceClassification</a> (GPT Neo model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/ibert#transformers.IBertConfig">IBertConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/ibert#transformers.IBertForSequenceClassification">IBertForSequenceClassification</a> (I-BERT model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/led#transformers.LEDConfig">LEDConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/led#transformers.LEDForSequenceClassification">LEDForSequenceClassification</a> (LED model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/layoutlm#transformers.LayoutLMConfig">LayoutLMConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/layoutlm#transformers.LayoutLMForSequenceClassification">LayoutLMForSequenceClassification</a> (LayoutLM model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/layoutlmv2#transformers.LayoutLMv2Config">LayoutLMv2Config</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/layoutlmv2#transformers.LayoutLMv2ForSequenceClassification">LayoutLMv2ForSequenceClassification</a> (LayoutLMv2 model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/longformer#transformers.LongformerConfig">LongformerConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/longformer#transformers.LongformerForSequenceClassification">LongformerForSequenceClassification</a> (Longformer model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/mbart#transformers.MBartConfig">MBartConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/mbart#transformers.MBartForSequenceClassification">MBartForSequenceClassification</a> (mBART model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/mpnet#transformers.MPNetConfig">MPNetConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/mpnet#transformers.MPNetForSequenceClassification">MPNetForSequenceClassification</a> (MPNet model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/megatron-bert#transformers.MegatronBertConfig">MegatronBertConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/megatron-bert#transformers.MegatronBertForSequenceClassification">MegatronBertForSequenceClassification</a> (MegatronBert model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/mobilebert#transformers.MobileBertConfig">MobileBertConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/mobilebert#transformers.MobileBertForSequenceClassification">MobileBertForSequenceClassification</a> (MobileBERT model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/nystromformer#transformers.NystromformerConfig">NystromformerConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/nystromformer#transformers.NystromformerForSequenceClassification">NystromformerForSequenceClassification</a> (Nystromformer model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/openai-gpt#transformers.OpenAIGPTConfig">OpenAIGPTConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/openai-gpt#transformers.OpenAIGPTForSequenceClassification">OpenAIGPTForSequenceClassification</a> (OpenAI GPT model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/plbart#transformers.PLBartConfig">PLBartConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/plbart#transformers.PLBartForSequenceClassification">PLBartForSequenceClassification</a> (PLBart model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/perceiver#transformers.PerceiverConfig">PerceiverConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/perceiver#transformers.PerceiverForSequenceClassification">PerceiverForSequenceClassification</a> (Perceiver model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/qdqbert#transformers.QDQBertConfig">QDQBertConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/qdqbert#transformers.QDQBertForSequenceClassification">QDQBertForSequenceClassification</a> (QDQBert model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/reformer#transformers.ReformerConfig">ReformerConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/reformer#transformers.ReformerForSequenceClassification">ReformerForSequenceClassification</a> (Reformer model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/rembert#transformers.RemBertConfig">RemBertConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/rembert#transformers.RemBertForSequenceClassification">RemBertForSequenceClassification</a> (RemBERT model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/roformer#transformers.RoFormerConfig">RoFormerConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/roformer#transformers.RoFormerForSequenceClassification">RoFormerForSequenceClassification</a> (RoFormer model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/roberta#transformers.RobertaConfig">RobertaConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/roberta#transformers.RobertaForSequenceClassification">RobertaForSequenceClassification</a> (RoBERTa model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/squeezebert#transformers.SqueezeBertConfig">SqueezeBertConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/squeezebert#transformers.SqueezeBertForSequenceClassification">SqueezeBertForSequenceClassification</a> (SqueezeBERT model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/tapas#transformers.TapasConfig">TapasConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/tapas#transformers.TapasForSequenceClassification">TapasForSequenceClassification</a> (TAPAS model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/transfo-xl#transformers.TransfoXLConfig">TransfoXLConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/transfo-xl#transformers.TransfoXLForSequenceClassification">TransfoXLForSequenceClassification</a> (Transformer-XL model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/xlm#transformers.XLMConfig">XLMConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/xlm#transformers.XLMForSequenceClassification">XLMForSequenceClassification</a> (XLM model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/xlm-roberta#transformers.XLMRobertaConfig">XLMRobertaConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/xlm-roberta#transformers.XLMRobertaForSequenceClassification">XLMRobertaForSequenceClassification</a> (XLM-RoBERTa model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/xlm-roberta-xl#transformers.XLMRobertaXLConfig">XLMRobertaXLConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/xlm-roberta-xl#transformers.XLMRobertaXLForSequenceClassification">XLMRobertaXLForSequenceClassification</a> (XLM-RoBERTa-XL model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/xlnet#transformers.XLNetConfig">XLNetConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/xlnet#transformers.XLNetForSequenceClassification">XLNetForSequenceClassification</a> (XLNet model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/yoso#transformers.YosoConfig">YosoConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/yoso#transformers.YosoForSequenceClassification">YosoForSequenceClassification</a> (YOSO model)</li>
</ul>`,name:"config"}]}}),mE=new w({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)`}}),gE=new M({props:{name:"from_pretrained",anchor:"transformers.models.auto.auto_factory._BaseAutoModelClass.from_pretrained",parameters:[{name:"*model_args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/pr_15839/src/transformers/models/auto/auto_factory.py#L418",parametersDescription:[{anchor:"transformers.models.auto.auto_factory._BaseAutoModelClass.from_pretrained.pretrained_model_name_or_path",description:`<strong>pretrained_model_name_or_path</strong> (<code>str</code> or <code>os.PathLike</code>) &#x2014;
Can be either:</p>
<ul>
<li>A string, the <em>model id</em> of a pretrained model hosted inside a model repo on huggingface.co.
Valid model ids can be located at the root-level, like <code>bert-base-uncased</code>, or namespaced under a
user or organization name, like <code>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_15839/en/main_classes/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.models.auto.auto_factory._BaseAutoModelClass.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.models.auto.auto_factory._BaseAutoModelClass.from_pretrained.config",description:`<strong>config</strong> (<a href="/docs/transformers/pr_15839/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>, <em>optional</em>) &#x2014;
Configuration for the model to use instead of an automatically loaded configuration. Configuration can
be automatically loaded when:</p>
<ul>
<li>The model is a model provided by the 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_15839/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and is reloaded by supplying the
save directory.</li>
<li>The model is loaded by supplying a local directory 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.models.auto.auto_factory._BaseAutoModelClass.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_15839/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and
<a href="/docs/transformers/pr_15839/en/main_classes/model#transformers.PreTrainedModel.from_pretrained">from_pretrained()</a> is not a simpler option.`,name:"state_dict"},{anchor:"transformers.models.auto.auto_factory._BaseAutoModelClass.from_pretrained.cache_dir",description:`<strong>cache_dir</strong> (<code>str</code> or <code>os.PathLike</code>, <em>optional</em>) &#x2014;
Path to a directory in which a downloaded pretrained model configuration should be cached if the
standard cache should not be used.`,name:"cache_dir"},{anchor:"transformers.models.auto.auto_factory._BaseAutoModelClass.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.models.auto.auto_factory._BaseAutoModelClass.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.models.auto.auto_factory._BaseAutoModelClass.from_pretrained.resume_download",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.models.auto.auto_factory._BaseAutoModelClass.from_pretrained.proxies",description:`<strong>proxies</strong> (<code>Dict[str, str]</code>, <em>optional</em>) &#x2014;
A dictionary of proxy servers to use by protocol or endpoint, e.g., <code>{&apos;http&apos;: &apos;foo.bar:3128&apos;, &apos;http://hostname&apos;: &apos;foo.bar:4012&apos;}</code>. The proxies are used on each request.`,name:"proxies"},{anchor:"transformers.models.auto.auto_factory._BaseAutoModelClass.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.models.auto.auto_factory._BaseAutoModelClass.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.models.auto.auto_factory._BaseAutoModelClass.from_pretrained.revision(str,",description:`<strong>revision(<code>str</code>,</strong> <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a
git-based system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any
identifier allowed by git.`,name:"revision(str,"},{anchor:"transformers.models.auto.auto_factory._BaseAutoModelClass.from_pretrained.trust_remote_code",description:`<strong>trust_remote_code</strong> (<code>bool</code>, <em>optional</em>, defaults 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.models.auto.auto_factory._BaseAutoModelClass.from_pretrained.kwargs",description:`<strong>kwargs</strong> (additional keyword arguments, <em>optional</em>) &#x2014;
Can be used to update the configuration object (after it being loaded) and initiate the model (e.g.,
<code>output_attentions=True</code>). Behaves differently depending on whether a <code>config</code> is provided or
automatically loaded:</p>
<ul>
<li>If a configuration is provided with <code>config</code>, <code>**kwargs</code> will be directly passed to the
underlying model&#x2019;s <code>__init__</code> method (we assume all relevant updates to the configuration have
already been done)</li>
<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_15839/en/main_classes/configuration#transformers.PretrainedConfig.from_pretrained">from_pretrained()</a>). Each key of <code>kwargs</code> that
corresponds to a configuration attribute will be used to override said attribute with the
supplied <code>kwargs</code> value. 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"}]}}),hE=new w({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>)`}}),pE=new X({}),_E=new M({props:{name:"class transformers.AutoModelForMultipleChoice",anchor:"transformers.AutoModelForMultipleChoice",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/pr_15839/src/transformers/models/auto/modeling_auto.py#L776"}}),bE=new M({props:{name:"from_config",anchor:"transformers.models.auto.auto_factory._BaseAutoModelClass.from_config",parameters:[{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/pr_15839/src/transformers/models/auto/auto_factory.py#L390",parametersDescription:[{anchor:"transformers.models.auto.auto_factory._BaseAutoModelClass.from_config.config",description:`<strong>config</strong> (<a href="/docs/transformers/pr_15839/en/main_classes/configuration#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_15839/en/model_doc/albert#transformers.AlbertConfig">AlbertConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/albert#transformers.AlbertForMultipleChoice">AlbertForMultipleChoice</a> (ALBERT model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/bert#transformers.BertConfig">BertConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/bert#transformers.BertForMultipleChoice">BertForMultipleChoice</a> (BERT model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/big_bird#transformers.BigBirdConfig">BigBirdConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/big_bird#transformers.BigBirdForMultipleChoice">BigBirdForMultipleChoice</a> (BigBird model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/camembert#transformers.CamembertConfig">CamembertConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/camembert#transformers.CamembertForMultipleChoice">CamembertForMultipleChoice</a> (CamemBERT model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/canine#transformers.CanineConfig">CanineConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/canine#transformers.CanineForMultipleChoice">CanineForMultipleChoice</a> (Canine model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/convbert#transformers.ConvBertConfig">ConvBertConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/convbert#transformers.ConvBertForMultipleChoice">ConvBertForMultipleChoice</a> (ConvBERT model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/data2vec#transformers.Data2VecTextConfig">Data2VecTextConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/data2vec#transformers.Data2VecTextForMultipleChoice">Data2VecTextForMultipleChoice</a> (Data2VecText model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/distilbert#transformers.DistilBertConfig">DistilBertConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/distilbert#transformers.DistilBertForMultipleChoice">DistilBertForMultipleChoice</a> (DistilBERT model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/electra#transformers.ElectraConfig">ElectraConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/electra#transformers.ElectraForMultipleChoice">ElectraForMultipleChoice</a> (ELECTRA model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/fnet#transformers.FNetConfig">FNetConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/fnet#transformers.FNetForMultipleChoice">FNetForMultipleChoice</a> (FNet model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/flaubert#transformers.FlaubertConfig">FlaubertConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/flaubert#transformers.FlaubertForMultipleChoice">FlaubertForMultipleChoice</a> (FlauBERT model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/funnel#transformers.FunnelConfig">FunnelConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/funnel#transformers.FunnelForMultipleChoice">FunnelForMultipleChoice</a> (Funnel Transformer model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/ibert#transformers.IBertConfig">IBertConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/ibert#transformers.IBertForMultipleChoice">IBertForMultipleChoice</a> (I-BERT model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/longformer#transformers.LongformerConfig">LongformerConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/longformer#transformers.LongformerForMultipleChoice">LongformerForMultipleChoice</a> (Longformer model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/mpnet#transformers.MPNetConfig">MPNetConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/mpnet#transformers.MPNetForMultipleChoice">MPNetForMultipleChoice</a> (MPNet model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/megatron-bert#transformers.MegatronBertConfig">MegatronBertConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/megatron-bert#transformers.MegatronBertForMultipleChoice">MegatronBertForMultipleChoice</a> (MegatronBert model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/mobilebert#transformers.MobileBertConfig">MobileBertConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/mobilebert#transformers.MobileBertForMultipleChoice">MobileBertForMultipleChoice</a> (MobileBERT model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/nystromformer#transformers.NystromformerConfig">NystromformerConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/nystromformer#transformers.NystromformerForMultipleChoice">NystromformerForMultipleChoice</a> (Nystromformer model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/qdqbert#transformers.QDQBertConfig">QDQBertConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/qdqbert#transformers.QDQBertForMultipleChoice">QDQBertForMultipleChoice</a> (QDQBert model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/rembert#transformers.RemBertConfig">RemBertConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/rembert#transformers.RemBertForMultipleChoice">RemBertForMultipleChoice</a> (RemBERT model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/roformer#transformers.RoFormerConfig">RoFormerConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/roformer#transformers.RoFormerForMultipleChoice">RoFormerForMultipleChoice</a> (RoFormer model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/roberta#transformers.RobertaConfig">RobertaConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/roberta#transformers.RobertaForMultipleChoice">RobertaForMultipleChoice</a> (RoBERTa model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/squeezebert#transformers.SqueezeBertConfig">SqueezeBertConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/squeezebert#transformers.SqueezeBertForMultipleChoice">SqueezeBertForMultipleChoice</a> (SqueezeBERT model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/xlm#transformers.XLMConfig">XLMConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/xlm#transformers.XLMForMultipleChoice">XLMForMultipleChoice</a> (XLM model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/xlm-roberta#transformers.XLMRobertaConfig">XLMRobertaConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/xlm-roberta#transformers.XLMRobertaForMultipleChoice">XLMRobertaForMultipleChoice</a> (XLM-RoBERTa model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/xlm-roberta-xl#transformers.XLMRobertaXLConfig">XLMRobertaXLConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/xlm-roberta-xl#transformers.XLMRobertaXLForMultipleChoice">XLMRobertaXLForMultipleChoice</a> (XLM-RoBERTa-XL model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/xlnet#transformers.XLNetConfig">XLNetConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/xlnet#transformers.XLNetForMultipleChoice">XLNetForMultipleChoice</a> (XLNet model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/yoso#transformers.YosoConfig">YosoConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/yoso#transformers.YosoForMultipleChoice">YosoForMultipleChoice</a> (YOSO model)</li>
</ul>`,name:"config"}]}}),vE=new w({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)`}}),TE=new M({props:{name:"from_pretrained",anchor:"transformers.models.auto.auto_factory._BaseAutoModelClass.from_pretrained",parameters:[{name:"*model_args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/pr_15839/src/transformers/models/auto/auto_factory.py#L418",parametersDescription:[{anchor:"transformers.models.auto.auto_factory._BaseAutoModelClass.from_pretrained.pretrained_model_name_or_path",description:`<strong>pretrained_model_name_or_path</strong> (<code>str</code> or <code>os.PathLike</code>) &#x2014;
Can be either:</p>
<ul>
<li>A string, the <em>model id</em> of a pretrained model hosted inside a model repo on huggingface.co.
Valid model ids can be located at the root-level, like <code>bert-base-uncased</code>, or namespaced under a
user or organization name, like <code>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_15839/en/main_classes/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.models.auto.auto_factory._BaseAutoModelClass.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.models.auto.auto_factory._BaseAutoModelClass.from_pretrained.config",description:`<strong>config</strong> (<a href="/docs/transformers/pr_15839/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>, <em>optional</em>) &#x2014;
Configuration for the model to use instead of an automatically loaded configuration. Configuration can
be automatically loaded when:</p>
<ul>
<li>The model is a model provided by the 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_15839/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and is reloaded by supplying the
save directory.</li>
<li>The model is loaded by supplying a local directory 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.models.auto.auto_factory._BaseAutoModelClass.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_15839/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and
<a href="/docs/transformers/pr_15839/en/main_classes/model#transformers.PreTrainedModel.from_pretrained">from_pretrained()</a> is not a simpler option.`,name:"state_dict"},{anchor:"transformers.models.auto.auto_factory._BaseAutoModelClass.from_pretrained.cache_dir",description:`<strong>cache_dir</strong> (<code>str</code> or <code>os.PathLike</code>, <em>optional</em>) &#x2014;
Path to a directory in which a downloaded pretrained model configuration should be cached if the
standard cache should not be used.`,name:"cache_dir"},{anchor:"transformers.models.auto.auto_factory._BaseAutoModelClass.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.models.auto.auto_factory._BaseAutoModelClass.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.models.auto.auto_factory._BaseAutoModelClass.from_pretrained.resume_download",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.models.auto.auto_factory._BaseAutoModelClass.from_pretrained.proxies",description:`<strong>proxies</strong> (<code>Dict[str, str]</code>, <em>optional</em>) &#x2014;
A dictionary of proxy servers to use by protocol or endpoint, e.g., <code>{&apos;http&apos;: &apos;foo.bar:3128&apos;, &apos;http://hostname&apos;: &apos;foo.bar:4012&apos;}</code>. The proxies are used on each request.`,name:"proxies"},{anchor:"transformers.models.auto.auto_factory._BaseAutoModelClass.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.models.auto.auto_factory._BaseAutoModelClass.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.models.auto.auto_factory._BaseAutoModelClass.from_pretrained.revision(str,",description:`<strong>revision(<code>str</code>,</strong> <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a
git-based system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any
identifier allowed by git.`,name:"revision(str,"},{anchor:"transformers.models.auto.auto_factory._BaseAutoModelClass.from_pretrained.trust_remote_code",description:`<strong>trust_remote_code</strong> (<code>bool</code>, <em>optional</em>, defaults 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.models.auto.auto_factory._BaseAutoModelClass.from_pretrained.kwargs",description:`<strong>kwargs</strong> (additional keyword arguments, <em>optional</em>) &#x2014;
Can be used to update the configuration object (after it being loaded) and initiate the model (e.g.,
<code>output_attentions=True</code>). Behaves differently depending on whether a <code>config</code> is provided or
automatically loaded:</p>
<ul>
<li>If a configuration is provided with <code>config</code>, <code>**kwargs</code> will be directly passed to the
underlying model&#x2019;s <code>__init__</code> method (we assume all relevant updates to the configuration have
already been done)</li>
<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_15839/en/main_classes/configuration#transformers.PretrainedConfig.from_pretrained">from_pretrained()</a>). Each key of <code>kwargs</code> that
corresponds to a configuration attribute will be used to override said attribute with the
supplied <code>kwargs</code> value. 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"}]}}),FE=new w({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>)`}}),CE=new X({}),ME=new M({props:{name:"class transformers.AutoModelForNextSentencePrediction",anchor:"transformers.AutoModelForNextSentencePrediction",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/pr_15839/src/transformers/models/auto/modeling_auto.py#L783"}}),yE=new M({props:{name:"from_config",anchor:"transformers.models.auto.auto_factory._BaseAutoModelClass.from_config",parameters:[{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/pr_15839/src/transformers/models/auto/auto_factory.py#L390",parametersDescription:[{anchor:"transformers.models.auto.auto_factory._BaseAutoModelClass.from_config.config",description:`<strong>config</strong> (<a href="/docs/transformers/pr_15839/en/main_classes/configuration#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_15839/en/model_doc/bert#transformers.BertConfig">BertConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/bert#transformers.BertForNextSentencePrediction">BertForNextSentencePrediction</a> (BERT model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/fnet#transformers.FNetConfig">FNetConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/fnet#transformers.FNetForNextSentencePrediction">FNetForNextSentencePrediction</a> (FNet model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/megatron-bert#transformers.MegatronBertConfig">MegatronBertConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/megatron-bert#transformers.MegatronBertForNextSentencePrediction">MegatronBertForNextSentencePrediction</a> (MegatronBert model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/mobilebert#transformers.MobileBertConfig">MobileBertConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/mobilebert#transformers.MobileBertForNextSentencePrediction">MobileBertForNextSentencePrediction</a> (MobileBERT model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/qdqbert#transformers.QDQBertConfig">QDQBertConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/qdqbert#transformers.QDQBertForNextSentencePrediction">QDQBertForNextSentencePrediction</a> (QDQBert model)</li>
</ul>`,name:"config"}]}}),wE=new w({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)`}}),AE=new M({props:{name:"from_pretrained",anchor:"transformers.models.auto.auto_factory._BaseAutoModelClass.from_pretrained",parameters:[{name:"*model_args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/pr_15839/src/transformers/models/auto/auto_factory.py#L418",parametersDescription:[{anchor:"transformers.models.auto.auto_factory._BaseAutoModelClass.from_pretrained.pretrained_model_name_or_path",description:`<strong>pretrained_model_name_or_path</strong> (<code>str</code> or <code>os.PathLike</code>) &#x2014;
Can be either:</p>
<ul>
<li>A string, the <em>model id</em> of a pretrained model hosted inside a model repo on huggingface.co.
Valid model ids can be located at the root-level, like <code>bert-base-uncased</code>, or namespaced under a
user or organization name, like <code>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_15839/en/main_classes/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.models.auto.auto_factory._BaseAutoModelClass.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.models.auto.auto_factory._BaseAutoModelClass.from_pretrained.config",description:`<strong>config</strong> (<a href="/docs/transformers/pr_15839/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>, <em>optional</em>) &#x2014;
Configuration for the model to use instead of an automatically loaded configuration. Configuration can
be automatically loaded when:</p>
<ul>
<li>The model is a model provided by the 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_15839/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and is reloaded by supplying the
save directory.</li>
<li>The model is loaded by supplying a local directory 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.models.auto.auto_factory._BaseAutoModelClass.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_15839/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and
<a href="/docs/transformers/pr_15839/en/main_classes/model#transformers.PreTrainedModel.from_pretrained">from_pretrained()</a> is not a simpler option.`,name:"state_dict"},{anchor:"transformers.models.auto.auto_factory._BaseAutoModelClass.from_pretrained.cache_dir",description:`<strong>cache_dir</strong> (<code>str</code> or <code>os.PathLike</code>, <em>optional</em>) &#x2014;
Path to a directory in which a downloaded pretrained model configuration should be cached if the
standard cache should not be used.`,name:"cache_dir"},{anchor:"transformers.models.auto.auto_factory._BaseAutoModelClass.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.models.auto.auto_factory._BaseAutoModelClass.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.models.auto.auto_factory._BaseAutoModelClass.from_pretrained.resume_download",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.models.auto.auto_factory._BaseAutoModelClass.from_pretrained.proxies",description:`<strong>proxies</strong> (<code>Dict[str, str]</code>, <em>optional</em>) &#x2014;
A dictionary of proxy servers to use by protocol or endpoint, e.g., <code>{&apos;http&apos;: &apos;foo.bar:3128&apos;, &apos;http://hostname&apos;: &apos;foo.bar:4012&apos;}</code>. The proxies are used on each request.`,name:"proxies"},{anchor:"transformers.models.auto.auto_factory._BaseAutoModelClass.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.models.auto.auto_factory._BaseAutoModelClass.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.models.auto.auto_factory._BaseAutoModelClass.from_pretrained.revision(str,",description:`<strong>revision(<code>str</code>,</strong> <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a
git-based system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any
identifier allowed by git.`,name:"revision(str,"},{anchor:"transformers.models.auto.auto_factory._BaseAutoModelClass.from_pretrained.trust_remote_code",description:`<strong>trust_remote_code</strong> (<code>bool</code>, <em>optional</em>, defaults 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.models.auto.auto_factory._BaseAutoModelClass.from_pretrained.kwargs",description:`<strong>kwargs</strong> (additional keyword arguments, <em>optional</em>) &#x2014;
Can be used to update the configuration object (after it being loaded) and initiate the model (e.g.,
<code>output_attentions=True</code>). Behaves differently depending on whether a <code>config</code> is provided or
automatically loaded:</p>
<ul>
<li>If a configuration is provided with <code>config</code>, <code>**kwargs</code> will be directly passed to the
underlying model&#x2019;s <code>__init__</code> method (we assume all relevant updates to the configuration have
already been done)</li>
<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_15839/en/main_classes/configuration#transformers.PretrainedConfig.from_pretrained">from_pretrained()</a>). Each key of <code>kwargs</code> that
corresponds to a configuration attribute will be used to override said attribute with the
supplied <code>kwargs</code> value. 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"}]}}),LE=new w({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>)`}}),BE=new X({}),xE=new M({props:{name:"class transformers.AutoModelForTokenClassification",anchor:"transformers.AutoModelForTokenClassification",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/pr_15839/src/transformers/models/auto/modeling_auto.py#L769"}}),RE=new M({props:{name:"from_config",anchor:"transformers.models.auto.auto_factory._BaseAutoModelClass.from_config",parameters:[{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/pr_15839/src/transformers/models/auto/auto_factory.py#L390",parametersDescription:[{anchor:"transformers.models.auto.auto_factory._BaseAutoModelClass.from_config.config",description:`<strong>config</strong> (<a href="/docs/transformers/pr_15839/en/main_classes/configuration#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_15839/en/model_doc/albert#transformers.AlbertConfig">AlbertConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/albert#transformers.AlbertForTokenClassification">AlbertForTokenClassification</a> (ALBERT model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/bert#transformers.BertConfig">BertConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/bert#transformers.BertForTokenClassification">BertForTokenClassification</a> (BERT model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/big_bird#transformers.BigBirdConfig">BigBirdConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/big_bird#transformers.BigBirdForTokenClassification">BigBirdForTokenClassification</a> (BigBird model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/camembert#transformers.CamembertConfig">CamembertConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/camembert#transformers.CamembertForTokenClassification">CamembertForTokenClassification</a> (CamemBERT model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/canine#transformers.CanineConfig">CanineConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/canine#transformers.CanineForTokenClassification">CanineForTokenClassification</a> (Canine model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/convbert#transformers.ConvBertConfig">ConvBertConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/convbert#transformers.ConvBertForTokenClassification">ConvBertForTokenClassification</a> (ConvBERT model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/data2vec#transformers.Data2VecTextConfig">Data2VecTextConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/data2vec#transformers.Data2VecTextForTokenClassification">Data2VecTextForTokenClassification</a> (Data2VecText model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/deberta#transformers.DebertaConfig">DebertaConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/deberta#transformers.DebertaForTokenClassification">DebertaForTokenClassification</a> (DeBERTa model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/deberta-v2#transformers.DebertaV2Config">DebertaV2Config</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/deberta-v2#transformers.DebertaV2ForTokenClassification">DebertaV2ForTokenClassification</a> (DeBERTa-v2 model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/distilbert#transformers.DistilBertConfig">DistilBertConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/distilbert#transformers.DistilBertForTokenClassification">DistilBertForTokenClassification</a> (DistilBERT model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/electra#transformers.ElectraConfig">ElectraConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/electra#transformers.ElectraForTokenClassification">ElectraForTokenClassification</a> (ELECTRA model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/fnet#transformers.FNetConfig">FNetConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/fnet#transformers.FNetForTokenClassification">FNetForTokenClassification</a> (FNet model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/flaubert#transformers.FlaubertConfig">FlaubertConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/flaubert#transformers.FlaubertForTokenClassification">FlaubertForTokenClassification</a> (FlauBERT model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/funnel#transformers.FunnelConfig">FunnelConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/funnel#transformers.FunnelForTokenClassification">FunnelForTokenClassification</a> (Funnel Transformer model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/gpt2#transformers.GPT2Config">GPT2Config</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/gpt2#transformers.GPT2ForTokenClassification">GPT2ForTokenClassification</a> (OpenAI GPT-2 model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/ibert#transformers.IBertConfig">IBertConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/ibert#transformers.IBertForTokenClassification">IBertForTokenClassification</a> (I-BERT model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/layoutlm#transformers.LayoutLMConfig">LayoutLMConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/layoutlm#transformers.LayoutLMForTokenClassification">LayoutLMForTokenClassification</a> (LayoutLM model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/layoutlmv2#transformers.LayoutLMv2Config">LayoutLMv2Config</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/layoutlmv2#transformers.LayoutLMv2ForTokenClassification">LayoutLMv2ForTokenClassification</a> (LayoutLMv2 model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/longformer#transformers.LongformerConfig">LongformerConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/longformer#transformers.LongformerForTokenClassification">LongformerForTokenClassification</a> (Longformer model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/mpnet#transformers.MPNetConfig">MPNetConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/mpnet#transformers.MPNetForTokenClassification">MPNetForTokenClassification</a> (MPNet model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/megatron-bert#transformers.MegatronBertConfig">MegatronBertConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/megatron-bert#transformers.MegatronBertForTokenClassification">MegatronBertForTokenClassification</a> (MegatronBert model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/mobilebert#transformers.MobileBertConfig">MobileBertConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/mobilebert#transformers.MobileBertForTokenClassification">MobileBertForTokenClassification</a> (MobileBERT model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/nystromformer#transformers.NystromformerConfig">NystromformerConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/nystromformer#transformers.NystromformerForTokenClassification">NystromformerForTokenClassification</a> (Nystromformer model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/qdqbert#transformers.QDQBertConfig">QDQBertConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/qdqbert#transformers.QDQBertForTokenClassification">QDQBertForTokenClassification</a> (QDQBert model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/rembert#transformers.RemBertConfig">RemBertConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/rembert#transformers.RemBertForTokenClassification">RemBertForTokenClassification</a> (RemBERT model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/roformer#transformers.RoFormerConfig">RoFormerConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/roformer#transformers.RoFormerForTokenClassification">RoFormerForTokenClassification</a> (RoFormer model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/roberta#transformers.RobertaConfig">RobertaConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/roberta#transformers.RobertaForTokenClassification">RobertaForTokenClassification</a> (RoBERTa model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/squeezebert#transformers.SqueezeBertConfig">SqueezeBertConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/squeezebert#transformers.SqueezeBertForTokenClassification">SqueezeBertForTokenClassification</a> (SqueezeBERT model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/xlm#transformers.XLMConfig">XLMConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/xlm#transformers.XLMForTokenClassification">XLMForTokenClassification</a> (XLM model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/xlm-roberta#transformers.XLMRobertaConfig">XLMRobertaConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/xlm-roberta#transformers.XLMRobertaForTokenClassification">XLMRobertaForTokenClassification</a> (XLM-RoBERTa model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/xlm-roberta-xl#transformers.XLMRobertaXLConfig">XLMRobertaXLConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/xlm-roberta-xl#transformers.XLMRobertaXLForTokenClassification">XLMRobertaXLForTokenClassification</a> (XLM-RoBERTa-XL model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/xlnet#transformers.XLNetConfig">XLNetConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/xlnet#transformers.XLNetForTokenClassification">XLNetForTokenClassification</a> (XLNet model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/yoso#transformers.YosoConfig">YosoConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/yoso#transformers.YosoForTokenClassification">YosoForTokenClassification</a> (YOSO model)</li>
</ul>`,name:"config"}]}}),SE=new w({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)`}}),PE=new M({props:{name:"from_pretrained",anchor:"transformers.models.auto.auto_factory._BaseAutoModelClass.from_pretrained",parameters:[{name:"*model_args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/pr_15839/src/transformers/models/auto/auto_factory.py#L418",parametersDescription:[{anchor:"transformers.models.auto.auto_factory._BaseAutoModelClass.from_pretrained.pretrained_model_name_or_path",description:`<strong>pretrained_model_name_or_path</strong> (<code>str</code> or <code>os.PathLike</code>) &#x2014;
Can be either:</p>
<ul>
<li>A string, the <em>model id</em> of a pretrained model hosted inside a model repo on huggingface.co.
Valid model ids can be located at the root-level, like <code>bert-base-uncased</code>, or namespaced under a
user or organization name, like <code>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_15839/en/main_classes/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.models.auto.auto_factory._BaseAutoModelClass.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.models.auto.auto_factory._BaseAutoModelClass.from_pretrained.config",description:`<strong>config</strong> (<a href="/docs/transformers/pr_15839/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>, <em>optional</em>) &#x2014;
Configuration for the model to use instead of an automatically loaded configuration. Configuration can
be automatically loaded when:</p>
<ul>
<li>The model is a model provided by the 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_15839/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and is reloaded by supplying the
save directory.</li>
<li>The model is loaded by supplying a local directory 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.models.auto.auto_factory._BaseAutoModelClass.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_15839/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and
<a href="/docs/transformers/pr_15839/en/main_classes/model#transformers.PreTrainedModel.from_pretrained">from_pretrained()</a> is not a simpler option.`,name:"state_dict"},{anchor:"transformers.models.auto.auto_factory._BaseAutoModelClass.from_pretrained.cache_dir",description:`<strong>cache_dir</strong> (<code>str</code> or <code>os.PathLike</code>, <em>optional</em>) &#x2014;
Path to a directory in which a downloaded pretrained model configuration should be cached if the
standard cache should not be used.`,name:"cache_dir"},{anchor:"transformers.models.auto.auto_factory._BaseAutoModelClass.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.models.auto.auto_factory._BaseAutoModelClass.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.models.auto.auto_factory._BaseAutoModelClass.from_pretrained.resume_download",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.models.auto.auto_factory._BaseAutoModelClass.from_pretrained.proxies",description:`<strong>proxies</strong> (<code>Dict[str, str]</code>, <em>optional</em>) &#x2014;
A dictionary of proxy servers to use by protocol or endpoint, e.g., <code>{&apos;http&apos;: &apos;foo.bar:3128&apos;, &apos;http://hostname&apos;: &apos;foo.bar:4012&apos;}</code>. The proxies are used on each request.`,name:"proxies"},{anchor:"transformers.models.auto.auto_factory._BaseAutoModelClass.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.models.auto.auto_factory._BaseAutoModelClass.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.models.auto.auto_factory._BaseAutoModelClass.from_pretrained.revision(str,",description:`<strong>revision(<code>str</code>,</strong> <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a
git-based system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any
identifier allowed by git.`,name:"revision(str,"},{anchor:"transformers.models.auto.auto_factory._BaseAutoModelClass.from_pretrained.trust_remote_code",description:`<strong>trust_remote_code</strong> (<code>bool</code>, <em>optional</em>, defaults 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.models.auto.auto_factory._BaseAutoModelClass.from_pretrained.kwargs",description:`<strong>kwargs</strong> (additional keyword arguments, <em>optional</em>) &#x2014;
Can be used to update the configuration object (after it being loaded) and initiate the model (e.g.,
<code>output_attentions=True</code>). Behaves differently depending on whether a <code>config</code> is provided or
automatically loaded:</p>
<ul>
<li>If a configuration is provided with <code>config</code>, <code>**kwargs</code> will be directly passed to the
underlying model&#x2019;s <code>__init__</code> method (we assume all relevant updates to the configuration have
already been done)</li>
<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_15839/en/main_classes/configuration#transformers.PretrainedConfig.from_pretrained">from_pretrained()</a>). Each key of <code>kwargs</code> that
corresponds to a configuration attribute will be used to override said attribute with the
supplied <code>kwargs</code> value. 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"}]}}),$E=new w({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>)`}}),IE=new X({}),jE=new M({props:{name:"class transformers.AutoModelForQuestionAnswering",anchor:"transformers.AutoModelForQuestionAnswering",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/pr_15839/src/transformers/models/auto/modeling_auto.py#L751"}}),NE=new M({props:{name:"from_config",anchor:"transformers.models.auto.auto_factory._BaseAutoModelClass.from_config",parameters:[{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/pr_15839/src/transformers/models/auto/auto_factory.py#L390",parametersDescription:[{anchor:"transformers.models.auto.auto_factory._BaseAutoModelClass.from_config.config",description:`<strong>config</strong> (<a href="/docs/transformers/pr_15839/en/main_classes/configuration#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_15839/en/model_doc/albert#transformers.AlbertConfig">AlbertConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/albert#transformers.AlbertForQuestionAnswering">AlbertForQuestionAnswering</a> (ALBERT model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/bart#transformers.BartConfig">BartConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/bart#transformers.BartForQuestionAnswering">BartForQuestionAnswering</a> (BART model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/bert#transformers.BertConfig">BertConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/bert#transformers.BertForQuestionAnswering">BertForQuestionAnswering</a> (BERT model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/big_bird#transformers.BigBirdConfig">BigBirdConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/big_bird#transformers.BigBirdForQuestionAnswering">BigBirdForQuestionAnswering</a> (BigBird model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/bigbird_pegasus#transformers.BigBirdPegasusConfig">BigBirdPegasusConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/bigbird_pegasus#transformers.BigBirdPegasusForQuestionAnswering">BigBirdPegasusForQuestionAnswering</a> (BigBirdPegasus model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/camembert#transformers.CamembertConfig">CamembertConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/camembert#transformers.CamembertForQuestionAnswering">CamembertForQuestionAnswering</a> (CamemBERT model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/canine#transformers.CanineConfig">CanineConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/canine#transformers.CanineForQuestionAnswering">CanineForQuestionAnswering</a> (Canine model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/convbert#transformers.ConvBertConfig">ConvBertConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/convbert#transformers.ConvBertForQuestionAnswering">ConvBertForQuestionAnswering</a> (ConvBERT model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/data2vec#transformers.Data2VecTextConfig">Data2VecTextConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/data2vec#transformers.Data2VecTextForQuestionAnswering">Data2VecTextForQuestionAnswering</a> (Data2VecText model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/deberta#transformers.DebertaConfig">DebertaConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/deberta#transformers.DebertaForQuestionAnswering">DebertaForQuestionAnswering</a> (DeBERTa model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/deberta-v2#transformers.DebertaV2Config">DebertaV2Config</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/deberta-v2#transformers.DebertaV2ForQuestionAnswering">DebertaV2ForQuestionAnswering</a> (DeBERTa-v2 model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/distilbert#transformers.DistilBertConfig">DistilBertConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/distilbert#transformers.DistilBertForQuestionAnswering">DistilBertForQuestionAnswering</a> (DistilBERT model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/electra#transformers.ElectraConfig">ElectraConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/electra#transformers.ElectraForQuestionAnswering">ElectraForQuestionAnswering</a> (ELECTRA model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/fnet#transformers.FNetConfig">FNetConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/fnet#transformers.FNetForQuestionAnswering">FNetForQuestionAnswering</a> (FNet model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/flaubert#transformers.FlaubertConfig">FlaubertConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/flaubert#transformers.FlaubertForQuestionAnsweringSimple">FlaubertForQuestionAnsweringSimple</a> (FlauBERT model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/funnel#transformers.FunnelConfig">FunnelConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/funnel#transformers.FunnelForQuestionAnswering">FunnelForQuestionAnswering</a> (Funnel Transformer model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/gptj#transformers.GPTJConfig">GPTJConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/gptj#transformers.GPTJForQuestionAnswering">GPTJForQuestionAnswering</a> (GPT-J model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/ibert#transformers.IBertConfig">IBertConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/ibert#transformers.IBertForQuestionAnswering">IBertForQuestionAnswering</a> (I-BERT model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/led#transformers.LEDConfig">LEDConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/led#transformers.LEDForQuestionAnswering">LEDForQuestionAnswering</a> (LED model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/layoutlmv2#transformers.LayoutLMv2Config">LayoutLMv2Config</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/layoutlmv2#transformers.LayoutLMv2ForQuestionAnswering">LayoutLMv2ForQuestionAnswering</a> (LayoutLMv2 model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/longformer#transformers.LongformerConfig">LongformerConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/longformer#transformers.LongformerForQuestionAnswering">LongformerForQuestionAnswering</a> (Longformer model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/lxmert#transformers.LxmertConfig">LxmertConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/lxmert#transformers.LxmertForQuestionAnswering">LxmertForQuestionAnswering</a> (LXMERT model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/mbart#transformers.MBartConfig">MBartConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/mbart#transformers.MBartForQuestionAnswering">MBartForQuestionAnswering</a> (mBART model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/mpnet#transformers.MPNetConfig">MPNetConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/mpnet#transformers.MPNetForQuestionAnswering">MPNetForQuestionAnswering</a> (MPNet model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/megatron-bert#transformers.MegatronBertConfig">MegatronBertConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/megatron-bert#transformers.MegatronBertForQuestionAnswering">MegatronBertForQuestionAnswering</a> (MegatronBert model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/mobilebert#transformers.MobileBertConfig">MobileBertConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/mobilebert#transformers.MobileBertForQuestionAnswering">MobileBertForQuestionAnswering</a> (MobileBERT model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/nystromformer#transformers.NystromformerConfig">NystromformerConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/nystromformer#transformers.NystromformerForQuestionAnswering">NystromformerForQuestionAnswering</a> (Nystromformer model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/qdqbert#transformers.QDQBertConfig">QDQBertConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/qdqbert#transformers.QDQBertForQuestionAnswering">QDQBertForQuestionAnswering</a> (QDQBert model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/reformer#transformers.ReformerConfig">ReformerConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/reformer#transformers.ReformerForQuestionAnswering">ReformerForQuestionAnswering</a> (Reformer model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/rembert#transformers.RemBertConfig">RemBertConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/rembert#transformers.RemBertForQuestionAnswering">RemBertForQuestionAnswering</a> (RemBERT model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/roformer#transformers.RoFormerConfig">RoFormerConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/roformer#transformers.RoFormerForQuestionAnswering">RoFormerForQuestionAnswering</a> (RoFormer model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/roberta#transformers.RobertaConfig">RobertaConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/roberta#transformers.RobertaForQuestionAnswering">RobertaForQuestionAnswering</a> (RoBERTa model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/splinter#transformers.SplinterConfig">SplinterConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/splinter#transformers.SplinterForQuestionAnswering">SplinterForQuestionAnswering</a> (Splinter model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/squeezebert#transformers.SqueezeBertConfig">SqueezeBertConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/squeezebert#transformers.SqueezeBertForQuestionAnswering">SqueezeBertForQuestionAnswering</a> (SqueezeBERT model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/xlm#transformers.XLMConfig">XLMConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/xlm#transformers.XLMForQuestionAnsweringSimple">XLMForQuestionAnsweringSimple</a> (XLM model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/xlm-roberta#transformers.XLMRobertaConfig">XLMRobertaConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/xlm-roberta#transformers.XLMRobertaForQuestionAnswering">XLMRobertaForQuestionAnswering</a> (XLM-RoBERTa model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/xlm-roberta-xl#transformers.XLMRobertaXLConfig">XLMRobertaXLConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/xlm-roberta-xl#transformers.XLMRobertaXLForQuestionAnswering">XLMRobertaXLForQuestionAnswering</a> (XLM-RoBERTa-XL model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/xlnet#transformers.XLNetConfig">XLNetConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/xlnet#transformers.XLNetForQuestionAnsweringSimple">XLNetForQuestionAnsweringSimple</a> (XLNet model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/yoso#transformers.YosoConfig">YosoConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/yoso#transformers.YosoForQuestionAnswering">YosoForQuestionAnswering</a> (YOSO model)</li>
</ul>`,name:"config"}]}}),qE=new w({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)`}}),OE=new M({props:{name:"from_pretrained",anchor:"transformers.models.auto.auto_factory._BaseAutoModelClass.from_pretrained",parameters:[{name:"*model_args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/pr_15839/src/transformers/models/auto/auto_factory.py#L418",parametersDescription:[{anchor:"transformers.models.auto.auto_factory._BaseAutoModelClass.from_pretrained.pretrained_model_name_or_path",description:`<strong>pretrained_model_name_or_path</strong> (<code>str</code> or <code>os.PathLike</code>) &#x2014;
Can be either:</p>
<ul>
<li>A string, the <em>model id</em> of a pretrained model hosted inside a model repo on huggingface.co.
Valid model ids can be located at the root-level, like <code>bert-base-uncased</code>, or namespaced under a
user or organization name, like <code>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_15839/en/main_classes/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.models.auto.auto_factory._BaseAutoModelClass.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.models.auto.auto_factory._BaseAutoModelClass.from_pretrained.config",description:`<strong>config</strong> (<a href="/docs/transformers/pr_15839/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>, <em>optional</em>) &#x2014;
Configuration for the model to use instead of an automatically loaded configuration. Configuration can
be automatically loaded when:</p>
<ul>
<li>The model is a model provided by the 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_15839/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and is reloaded by supplying the
save directory.</li>
<li>The model is loaded by supplying a local directory 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.models.auto.auto_factory._BaseAutoModelClass.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_15839/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and
<a href="/docs/transformers/pr_15839/en/main_classes/model#transformers.PreTrainedModel.from_pretrained">from_pretrained()</a> is not a simpler option.`,name:"state_dict"},{anchor:"transformers.models.auto.auto_factory._BaseAutoModelClass.from_pretrained.cache_dir",description:`<strong>cache_dir</strong> (<code>str</code> or <code>os.PathLike</code>, <em>optional</em>) &#x2014;
Path to a directory in which a downloaded pretrained model configuration should be cached if the
standard cache should not be used.`,name:"cache_dir"},{anchor:"transformers.models.auto.auto_factory._BaseAutoModelClass.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.models.auto.auto_factory._BaseAutoModelClass.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.models.auto.auto_factory._BaseAutoModelClass.from_pretrained.resume_download",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.models.auto.auto_factory._BaseAutoModelClass.from_pretrained.proxies",description:`<strong>proxies</strong> (<code>Dict[str, str]</code>, <em>optional</em>) &#x2014;
A dictionary of proxy servers to use by protocol or endpoint, e.g., <code>{&apos;http&apos;: &apos;foo.bar:3128&apos;, &apos;http://hostname&apos;: &apos;foo.bar:4012&apos;}</code>. The proxies are used on each request.`,name:"proxies"},{anchor:"transformers.models.auto.auto_factory._BaseAutoModelClass.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.models.auto.auto_factory._BaseAutoModelClass.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.models.auto.auto_factory._BaseAutoModelClass.from_pretrained.revision(str,",description:`<strong>revision(<code>str</code>,</strong> <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a
git-based system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any
identifier allowed by git.`,name:"revision(str,"},{anchor:"transformers.models.auto.auto_factory._BaseAutoModelClass.from_pretrained.trust_remote_code",description:`<strong>trust_remote_code</strong> (<code>bool</code>, <em>optional</em>, defaults 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.models.auto.auto_factory._BaseAutoModelClass.from_pretrained.kwargs",description:`<strong>kwargs</strong> (additional keyword arguments, <em>optional</em>) &#x2014;
Can be used to update the configuration object (after it being loaded) and initiate the model (e.g.,
<code>output_attentions=True</code>). Behaves differently depending on whether a <code>config</code> is provided or
automatically loaded:</p>
<ul>
<li>If a configuration is provided with <code>config</code>, <code>**kwargs</code> will be directly passed to the
underlying model&#x2019;s <code>__init__</code> method (we assume all relevant updates to the configuration have
already been done)</li>
<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_15839/en/main_classes/configuration#transformers.PretrainedConfig.from_pretrained">from_pretrained()</a>). Each key of <code>kwargs</code> that
corresponds to a configuration attribute will be used to override said attribute with the
supplied <code>kwargs</code> value. 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"}]}}),GE=new w({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>)`}}),XE=new X({}),VE=new M({props:{name:"class transformers.AutoModelForTableQuestionAnswering",anchor:"transformers.AutoModelForTableQuestionAnswering",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/pr_15839/src/transformers/models/auto/modeling_auto.py#L758"}}),WE=new M({props:{name:"from_config",anchor:"transformers.models.auto.auto_factory._BaseAutoModelClass.from_config",parameters:[{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/pr_15839/src/transformers/models/auto/auto_factory.py#L390",parametersDescription:[{anchor:"transformers.models.auto.auto_factory._BaseAutoModelClass.from_config.config",description:`<strong>config</strong> (<a href="/docs/transformers/pr_15839/en/main_classes/configuration#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_15839/en/model_doc/tapas#transformers.TapasConfig">TapasConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/tapas#transformers.TapasForQuestionAnswering">TapasForQuestionAnswering</a> (TAPAS model)</li>
</ul>`,name:"config"}]}}),QE=new w({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)`}}),HE=new M({props:{name:"from_pretrained",anchor:"transformers.models.auto.auto_factory._BaseAutoModelClass.from_pretrained",parameters:[{name:"*model_args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/pr_15839/src/transformers/models/auto/auto_factory.py#L418",parametersDescription:[{anchor:"transformers.models.auto.auto_factory._BaseAutoModelClass.from_pretrained.pretrained_model_name_or_path",description:`<strong>pretrained_model_name_or_path</strong> (<code>str</code> or <code>os.PathLike</code>) &#x2014;
Can be either:</p>
<ul>
<li>A string, the <em>model id</em> of a pretrained model hosted inside a model repo on huggingface.co.
Valid model ids can be located at the root-level, like <code>bert-base-uncased</code>, or namespaced under a
user or organization name, like <code>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_15839/en/main_classes/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.models.auto.auto_factory._BaseAutoModelClass.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.models.auto.auto_factory._BaseAutoModelClass.from_pretrained.config",description:`<strong>config</strong> (<a href="/docs/transformers/pr_15839/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>, <em>optional</em>) &#x2014;
Configuration for the model to use instead of an automatically loaded configuration. Configuration can
be automatically loaded when:</p>
<ul>
<li>The model is a model provided by the 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_15839/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and is reloaded by supplying the
save directory.</li>
<li>The model is loaded by supplying a local directory 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.models.auto.auto_factory._BaseAutoModelClass.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_15839/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and
<a href="/docs/transformers/pr_15839/en/main_classes/model#transformers.PreTrainedModel.from_pretrained">from_pretrained()</a> is not a simpler option.`,name:"state_dict"},{anchor:"transformers.models.auto.auto_factory._BaseAutoModelClass.from_pretrained.cache_dir",description:`<strong>cache_dir</strong> (<code>str</code> or <code>os.PathLike</code>, <em>optional</em>) &#x2014;
Path to a directory in which a downloaded pretrained model configuration should be cached if the
standard cache should not be used.`,name:"cache_dir"},{anchor:"transformers.models.auto.auto_factory._BaseAutoModelClass.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.models.auto.auto_factory._BaseAutoModelClass.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.models.auto.auto_factory._BaseAutoModelClass.from_pretrained.resume_download",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.models.auto.auto_factory._BaseAutoModelClass.from_pretrained.proxies",description:`<strong>proxies</strong> (<code>Dict[str, str]</code>, <em>optional</em>) &#x2014;
A dictionary of proxy servers to use by protocol or endpoint, e.g., <code>{&apos;http&apos;: &apos;foo.bar:3128&apos;, &apos;http://hostname&apos;: &apos;foo.bar:4012&apos;}</code>. The proxies are used on each request.`,name:"proxies"},{anchor:"transformers.models.auto.auto_factory._BaseAutoModelClass.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.models.auto.auto_factory._BaseAutoModelClass.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.models.auto.auto_factory._BaseAutoModelClass.from_pretrained.revision(str,",description:`<strong>revision(<code>str</code>,</strong> <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a
git-based system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any
identifier allowed by git.`,name:"revision(str,"},{anchor:"transformers.models.auto.auto_factory._BaseAutoModelClass.from_pretrained.trust_remote_code",description:`<strong>trust_remote_code</strong> (<code>bool</code>, <em>optional</em>, defaults 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.models.auto.auto_factory._BaseAutoModelClass.from_pretrained.kwargs",description:`<strong>kwargs</strong> (additional keyword arguments, <em>optional</em>) &#x2014;
Can be used to update the configuration object (after it being loaded) and initiate the model (e.g.,
<code>output_attentions=True</code>). Behaves differently depending on whether a <code>config</code> is provided or
automatically loaded:</p>
<ul>
<li>If a configuration is provided with <code>config</code>, <code>**kwargs</code> will be directly passed to the
underlying model&#x2019;s <code>__init__</code> method (we assume all relevant updates to the configuration have
already been done)</li>
<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_15839/en/main_classes/configuration#transformers.PretrainedConfig.from_pretrained">from_pretrained()</a>). Each key of <code>kwargs</code> that
corresponds to a configuration attribute will be used to override said attribute with the
supplied <code>kwargs</code> value. 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"}]}}),UE=new w({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>)`}}),JE=new X({}),YE=new M({props:{name:"class transformers.AutoModelForImageClassification",anchor:"transformers.AutoModelForImageClassification",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/pr_15839/src/transformers/models/auto/modeling_auto.py#L792"}}),ZE=new M({props:{name:"from_config",anchor:"transformers.models.auto.auto_factory._BaseAutoModelClass.from_config",parameters:[{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/pr_15839/src/transformers/models/auto/auto_factory.py#L390",parametersDescription:[{anchor:"transformers.models.auto.auto_factory._BaseAutoModelClass.from_config.config",description:`<strong>config</strong> (<a href="/docs/transformers/pr_15839/en/main_classes/configuration#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_15839/en/model_doc/beit#transformers.BeitConfig">BeitConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/beit#transformers.BeitForImageClassification">BeitForImageClassification</a> (BEiT model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/convnext#transformers.ConvNextConfig">ConvNextConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/convnext#transformers.ConvNextForImageClassification">ConvNextForImageClassification</a> (ConvNext model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/deit#transformers.DeiTConfig">DeiTConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/deit#transformers.DeiTForImageClassification">DeiTForImageClassification</a> or <a href="/docs/transformers/pr_15839/en/model_doc/deit#transformers.DeiTForImageClassificationWithTeacher">DeiTForImageClassificationWithTeacher</a> (DeiT model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/imagegpt#transformers.ImageGPTConfig">ImageGPTConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/imagegpt#transformers.ImageGPTForImageClassification">ImageGPTForImageClassification</a> (ImageGPT model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/perceiver#transformers.PerceiverConfig">PerceiverConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/perceiver#transformers.PerceiverForImageClassificationLearned">PerceiverForImageClassificationLearned</a> or <a href="/docs/transformers/pr_15839/en/model_doc/perceiver#transformers.PerceiverForImageClassificationFourier">PerceiverForImageClassificationFourier</a> or <a href="/docs/transformers/pr_15839/en/model_doc/perceiver#transformers.PerceiverForImageClassificationConvProcessing">PerceiverForImageClassificationConvProcessing</a> (Perceiver model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/poolformer#transformers.PoolFormerConfig">PoolFormerConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/poolformer#transformers.PoolFormerForImageClassification">PoolFormerForImageClassification</a> (PoolFormer model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/segformer#transformers.SegformerConfig">SegformerConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/segformer#transformers.SegformerForImageClassification">SegformerForImageClassification</a> (SegFormer model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/swin#transformers.SwinConfig">SwinConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/swin#transformers.SwinForImageClassification">SwinForImageClassification</a> (Swin model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/vit#transformers.ViTConfig">ViTConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/vit#transformers.ViTForImageClassification">ViTForImageClassification</a> (ViT model)</li>
</ul>`,name:"config"}]}}),ey=new w({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)`}}),oy=new M({props:{name:"from_pretrained",anchor:"transformers.models.auto.auto_factory._BaseAutoModelClass.from_pretrained",parameters:[{name:"*model_args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/pr_15839/src/transformers/models/auto/auto_factory.py#L418",parametersDescription:[{anchor:"transformers.models.auto.auto_factory._BaseAutoModelClass.from_pretrained.pretrained_model_name_or_path",description:`<strong>pretrained_model_name_or_path</strong> (<code>str</code> or <code>os.PathLike</code>) &#x2014;
Can be either:</p>
<ul>
<li>A string, the <em>model id</em> of a pretrained model hosted inside a model repo on huggingface.co.
Valid model ids can be located at the root-level, like <code>bert-base-uncased</code>, or namespaced under a
user or organization name, like <code>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_15839/en/main_classes/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.models.auto.auto_factory._BaseAutoModelClass.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.models.auto.auto_factory._BaseAutoModelClass.from_pretrained.config",description:`<strong>config</strong> (<a href="/docs/transformers/pr_15839/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>, <em>optional</em>) &#x2014;
Configuration for the model to use instead of an automatically loaded configuration. Configuration can
be automatically loaded when:</p>
<ul>
<li>The model is a model provided by the 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_15839/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and is reloaded by supplying the
save directory.</li>
<li>The model is loaded by supplying a local directory 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.models.auto.auto_factory._BaseAutoModelClass.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_15839/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and
<a href="/docs/transformers/pr_15839/en/main_classes/model#transformers.PreTrainedModel.from_pretrained">from_pretrained()</a> is not a simpler option.`,name:"state_dict"},{anchor:"transformers.models.auto.auto_factory._BaseAutoModelClass.from_pretrained.cache_dir",description:`<strong>cache_dir</strong> (<code>str</code> or <code>os.PathLike</code>, <em>optional</em>) &#x2014;
Path to a directory in which a downloaded pretrained model configuration should be cached if the
standard cache should not be used.`,name:"cache_dir"},{anchor:"transformers.models.auto.auto_factory._BaseAutoModelClass.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.models.auto.auto_factory._BaseAutoModelClass.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.models.auto.auto_factory._BaseAutoModelClass.from_pretrained.resume_download",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.models.auto.auto_factory._BaseAutoModelClass.from_pretrained.proxies",description:`<strong>proxies</strong> (<code>Dict[str, str]</code>, <em>optional</em>) &#x2014;
A dictionary of proxy servers to use by protocol or endpoint, e.g., <code>{&apos;http&apos;: &apos;foo.bar:3128&apos;, &apos;http://hostname&apos;: &apos;foo.bar:4012&apos;}</code>. The proxies are used on each request.`,name:"proxies"},{anchor:"transformers.models.auto.auto_factory._BaseAutoModelClass.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.models.auto.auto_factory._BaseAutoModelClass.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.models.auto.auto_factory._BaseAutoModelClass.from_pretrained.revision(str,",description:`<strong>revision(<code>str</code>,</strong> <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a
git-based system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any
identifier allowed by git.`,name:"revision(str,"},{anchor:"transformers.models.auto.auto_factory._BaseAutoModelClass.from_pretrained.trust_remote_code",description:`<strong>trust_remote_code</strong> (<code>bool</code>, <em>optional</em>, defaults 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.models.auto.auto_factory._BaseAutoModelClass.from_pretrained.kwargs",description:`<strong>kwargs</strong> (additional keyword arguments, <em>optional</em>) &#x2014;
Can be used to update the configuration object (after it being loaded) and initiate the model (e.g.,
<code>output_attentions=True</code>). Behaves differently depending on whether a <code>config</code> is provided or
automatically loaded:</p>
<ul>
<li>If a configuration is provided with <code>config</code>, <code>**kwargs</code> will be directly passed to the
underlying model&#x2019;s <code>__init__</code> method (we assume all relevant updates to the configuration have
already been done)</li>
<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_15839/en/main_classes/configuration#transformers.PretrainedConfig.from_pretrained">from_pretrained()</a>). Each key of <code>kwargs</code> that
corresponds to a configuration attribute will be used to override said attribute with the
supplied <code>kwargs</code> value. 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"}]}}),ry=new w({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>)`}}),ty=new X({}),ay=new M({props:{name:"class transformers.AutoModelForVision2Seq",anchor:"transformers.AutoModelForVision2Seq",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/pr_15839/src/transformers/models/auto/modeling_auto.py#L831"}}),sy=new M({props:{name:"from_config",anchor:"transformers.models.auto.auto_factory._BaseAutoModelClass.from_config",parameters:[{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/pr_15839/src/transformers/models/auto/auto_factory.py#L390",parametersDescription:[{anchor:"transformers.models.auto.auto_factory._BaseAutoModelClass.from_config.config",description:`<strong>config</strong> (<a href="/docs/transformers/pr_15839/en/main_classes/configuration#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_15839/en/model_doc/vision-encoder-decoder#transformers.VisionEncoderDecoderConfig">VisionEncoderDecoderConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/vision-encoder-decoder#transformers.VisionEncoderDecoderModel">VisionEncoderDecoderModel</a> (Vision Encoder decoder model)</li>
</ul>`,name:"config"}]}}),ly=new w({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)`}}),iy=new M({props:{name:"from_pretrained",anchor:"transformers.models.auto.auto_factory._BaseAutoModelClass.from_pretrained",parameters:[{name:"*model_args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/pr_15839/src/transformers/models/auto/auto_factory.py#L418",parametersDescription:[{anchor:"transformers.models.auto.auto_factory._BaseAutoModelClass.from_pretrained.pretrained_model_name_or_path",description:`<strong>pretrained_model_name_or_path</strong> (<code>str</code> or <code>os.PathLike</code>) &#x2014;
Can be either:</p>
<ul>
<li>A string, the <em>model id</em> of a pretrained model hosted inside a model repo on huggingface.co.
Valid model ids can be located at the root-level, like <code>bert-base-uncased</code>, or namespaced under a
user or organization name, like <code>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_15839/en/main_classes/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.models.auto.auto_factory._BaseAutoModelClass.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.models.auto.auto_factory._BaseAutoModelClass.from_pretrained.config",description:`<strong>config</strong> (<a href="/docs/transformers/pr_15839/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>, <em>optional</em>) &#x2014;
Configuration for the model to use instead of an automatically loaded configuration. Configuration can
be automatically loaded when:</p>
<ul>
<li>The model is a model provided by the 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_15839/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and is reloaded by supplying the
save directory.</li>
<li>The model is loaded by supplying a local directory 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.models.auto.auto_factory._BaseAutoModelClass.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_15839/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and
<a href="/docs/transformers/pr_15839/en/main_classes/model#transformers.PreTrainedModel.from_pretrained">from_pretrained()</a> is not a simpler option.`,name:"state_dict"},{anchor:"transformers.models.auto.auto_factory._BaseAutoModelClass.from_pretrained.cache_dir",description:`<strong>cache_dir</strong> (<code>str</code> or <code>os.PathLike</code>, <em>optional</em>) &#x2014;
Path to a directory in which a downloaded pretrained model configuration should be cached if the
standard cache should not be used.`,name:"cache_dir"},{anchor:"transformers.models.auto.auto_factory._BaseAutoModelClass.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.models.auto.auto_factory._BaseAutoModelClass.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.models.auto.auto_factory._BaseAutoModelClass.from_pretrained.resume_download",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.models.auto.auto_factory._BaseAutoModelClass.from_pretrained.proxies",description:`<strong>proxies</strong> (<code>Dict[str, str]</code>, <em>optional</em>) &#x2014;
A dictionary of proxy servers to use by protocol or endpoint, e.g., <code>{&apos;http&apos;: &apos;foo.bar:3128&apos;, &apos;http://hostname&apos;: &apos;foo.bar:4012&apos;}</code>. The proxies are used on each request.`,name:"proxies"},{anchor:"transformers.models.auto.auto_factory._BaseAutoModelClass.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.models.auto.auto_factory._BaseAutoModelClass.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.models.auto.auto_factory._BaseAutoModelClass.from_pretrained.revision(str,",description:`<strong>revision(<code>str</code>,</strong> <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a
git-based system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any
identifier allowed by git.`,name:"revision(str,"},{anchor:"transformers.models.auto.auto_factory._BaseAutoModelClass.from_pretrained.trust_remote_code",description:`<strong>trust_remote_code</strong> (<code>bool</code>, <em>optional</em>, defaults 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.models.auto.auto_factory._BaseAutoModelClass.from_pretrained.kwargs",description:`<strong>kwargs</strong> (additional keyword arguments, <em>optional</em>) &#x2014;
Can be used to update the configuration object (after it being loaded) and initiate the model (e.g.,
<code>output_attentions=True</code>). Behaves differently depending on whether a <code>config</code> is provided or
automatically loaded:</p>
<ul>
<li>If a configuration is provided with <code>config</code>, <code>**kwargs</code> will be directly passed to the
underlying model&#x2019;s <code>__init__</code> method (we assume all relevant updates to the configuration have
already been done)</li>
<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_15839/en/main_classes/configuration#transformers.PretrainedConfig.from_pretrained">from_pretrained()</a>). Each key of <code>kwargs</code> that
corresponds to a configuration attribute will be used to override said attribute with the
supplied <code>kwargs</code> value. 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"}]}}),dy=new w({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>)`}}),cy=new X({}),fy=new M({props:{name:"class transformers.AutoModelForAudioClassification",anchor:"transformers.AutoModelForAudioClassification",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/pr_15839/src/transformers/models/auto/modeling_auto.py#L838"}}),gy=new M({props:{name:"from_config",anchor:"transformers.models.auto.auto_factory._BaseAutoModelClass.from_config",parameters:[{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/pr_15839/src/transformers/models/auto/auto_factory.py#L390",parametersDescription:[{anchor:"transformers.models.auto.auto_factory._BaseAutoModelClass.from_config.config",description:`<strong>config</strong> (<a href="/docs/transformers/pr_15839/en/main_classes/configuration#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_15839/en/model_doc/data2vec#transformers.Data2VecAudioConfig">Data2VecAudioConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/data2vec#transformers.Data2VecAudioForSequenceClassification">Data2VecAudioForSequenceClassification</a> (Data2VecAudio model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/hubert#transformers.HubertConfig">HubertConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/hubert#transformers.HubertForSequenceClassification">HubertForSequenceClassification</a> (Hubert model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/sew#transformers.SEWConfig">SEWConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/sew#transformers.SEWForSequenceClassification">SEWForSequenceClassification</a> (SEW model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/sew-d#transformers.SEWDConfig">SEWDConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/sew-d#transformers.SEWDForSequenceClassification">SEWDForSequenceClassification</a> (SEW-D model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/unispeech#transformers.UniSpeechConfig">UniSpeechConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/unispeech#transformers.UniSpeechForSequenceClassification">UniSpeechForSequenceClassification</a> (UniSpeech model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/unispeech-sat#transformers.UniSpeechSatConfig">UniSpeechSatConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/unispeech-sat#transformers.UniSpeechSatForSequenceClassification">UniSpeechSatForSequenceClassification</a> (UniSpeechSat model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/wav2vec2#transformers.Wav2Vec2Config">Wav2Vec2Config</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/wav2vec2#transformers.Wav2Vec2ForSequenceClassification">Wav2Vec2ForSequenceClassification</a> (Wav2Vec2 model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/wavlm#transformers.WavLMConfig">WavLMConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/wavlm#transformers.WavLMForSequenceClassification">WavLMForSequenceClassification</a> (WavLM model)</li>
</ul>`,name:"config"}]}}),hy=new w({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)`}}),py=new M({props:{name:"from_pretrained",anchor:"transformers.models.auto.auto_factory._BaseAutoModelClass.from_pretrained",parameters:[{name:"*model_args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/pr_15839/src/transformers/models/auto/auto_factory.py#L418",parametersDescription:[{anchor:"transformers.models.auto.auto_factory._BaseAutoModelClass.from_pretrained.pretrained_model_name_or_path",description:`<strong>pretrained_model_name_or_path</strong> (<code>str</code> or <code>os.PathLike</code>) &#x2014;
Can be either:</p>
<ul>
<li>A string, the <em>model id</em> of a pretrained model hosted inside a model repo on huggingface.co.
Valid model ids can be located at the root-level, like <code>bert-base-uncased</code>, or namespaced under a
user or organization name, like <code>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_15839/en/main_classes/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.models.auto.auto_factory._BaseAutoModelClass.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.models.auto.auto_factory._BaseAutoModelClass.from_pretrained.config",description:`<strong>config</strong> (<a href="/docs/transformers/pr_15839/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>, <em>optional</em>) &#x2014;
Configuration for the model to use instead of an automatically loaded configuration. Configuration can
be automatically loaded when:</p>
<ul>
<li>The model is a model provided by the 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_15839/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and is reloaded by supplying the
save directory.</li>
<li>The model is loaded by supplying a local directory 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.models.auto.auto_factory._BaseAutoModelClass.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_15839/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and
<a href="/docs/transformers/pr_15839/en/main_classes/model#transformers.PreTrainedModel.from_pretrained">from_pretrained()</a> is not a simpler option.`,name:"state_dict"},{anchor:"transformers.models.auto.auto_factory._BaseAutoModelClass.from_pretrained.cache_dir",description:`<strong>cache_dir</strong> (<code>str</code> or <code>os.PathLike</code>, <em>optional</em>) &#x2014;
Path to a directory in which a downloaded pretrained model configuration should be cached if the
standard cache should not be used.`,name:"cache_dir"},{anchor:"transformers.models.auto.auto_factory._BaseAutoModelClass.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.models.auto.auto_factory._BaseAutoModelClass.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.models.auto.auto_factory._BaseAutoModelClass.from_pretrained.resume_download",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.models.auto.auto_factory._BaseAutoModelClass.from_pretrained.proxies",description:`<strong>proxies</strong> (<code>Dict[str, str]</code>, <em>optional</em>) &#x2014;
A dictionary of proxy servers to use by protocol or endpoint, e.g., <code>{&apos;http&apos;: &apos;foo.bar:3128&apos;, &apos;http://hostname&apos;: &apos;foo.bar:4012&apos;}</code>. The proxies are used on each request.`,name:"proxies"},{anchor:"transformers.models.auto.auto_factory._BaseAutoModelClass.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.models.auto.auto_factory._BaseAutoModelClass.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.models.auto.auto_factory._BaseAutoModelClass.from_pretrained.revision(str,",description:`<strong>revision(<code>str</code>,</strong> <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a
git-based system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any
identifier allowed by git.`,name:"revision(str,"},{anchor:"transformers.models.auto.auto_factory._BaseAutoModelClass.from_pretrained.trust_remote_code",description:`<strong>trust_remote_code</strong> (<code>bool</code>, <em>optional</em>, defaults 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.models.auto.auto_factory._BaseAutoModelClass.from_pretrained.kwargs",description:`<strong>kwargs</strong> (additional keyword arguments, <em>optional</em>) &#x2014;
Can be used to update the configuration object (after it being loaded) and initiate the model (e.g.,
<code>output_attentions=True</code>). Behaves differently depending on whether a <code>config</code> is provided or
automatically loaded:</p>
<ul>
<li>If a configuration is provided with <code>config</code>, <code>**kwargs</code> will be directly passed to the
underlying model&#x2019;s <code>__init__</code> method (we assume all relevant updates to the configuration have
already been done)</li>
<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_15839/en/main_classes/configuration#transformers.PretrainedConfig.from_pretrained">from_pretrained()</a>). Each key of <code>kwargs</code> that
corresponds to a configuration attribute will be used to override said attribute with the
supplied <code>kwargs</code> value. 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"}]}}),_y=new w({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>)`}}),uy=new X({}),by=new M({props:{name:"class transformers.AutoModelForAudioFrameClassification",anchor:"transformers.AutoModelForAudioFrameClassification",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/pr_15839/src/transformers/models/auto/modeling_auto.py#L861"}}),Ty=new M({props:{name:"from_config",anchor:"transformers.models.auto.auto_factory._BaseAutoModelClass.from_config",parameters:[{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/pr_15839/src/transformers/models/auto/auto_factory.py#L390",parametersDescription:[{anchor:"transformers.models.auto.auto_factory._BaseAutoModelClass.from_config.config",description:`<strong>config</strong> (<a href="/docs/transformers/pr_15839/en/main_classes/configuration#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_15839/en/model_doc/data2vec#transformers.Data2VecAudioConfig">Data2VecAudioConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/data2vec#transformers.Data2VecAudioForAudioFrameClassification">Data2VecAudioForAudioFrameClassification</a> (Data2VecAudio model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/unispeech-sat#transformers.UniSpeechSatConfig">UniSpeechSatConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/unispeech-sat#transformers.UniSpeechSatForAudioFrameClassification">UniSpeechSatForAudioFrameClassification</a> (UniSpeechSat model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/wav2vec2#transformers.Wav2Vec2Config">Wav2Vec2Config</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/wav2vec2#transformers.Wav2Vec2ForAudioFrameClassification">Wav2Vec2ForAudioFrameClassification</a> (Wav2Vec2 model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/wavlm#transformers.WavLMConfig">WavLMConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/wavlm#transformers.WavLMForAudioFrameClassification">WavLMForAudioFrameClassification</a> (WavLM model)</li>
</ul>`,name:"config"}]}}),Fy=new w({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)`}}),Cy=new M({props:{name:"from_pretrained",anchor:"transformers.models.auto.auto_factory._BaseAutoModelClass.from_pretrained",parameters:[{name:"*model_args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/pr_15839/src/transformers/models/auto/auto_factory.py#L418",parametersDescription:[{anchor:"transformers.models.auto.auto_factory._BaseAutoModelClass.from_pretrained.pretrained_model_name_or_path",description:`<strong>pretrained_model_name_or_path</strong> (<code>str</code> or <code>os.PathLike</code>) &#x2014;
Can be either:</p>
<ul>
<li>A string, the <em>model id</em> of a pretrained model hosted inside a model repo on huggingface.co.
Valid model ids can be located at the root-level, like <code>bert-base-uncased</code>, or namespaced under a
user or organization name, like <code>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_15839/en/main_classes/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.models.auto.auto_factory._BaseAutoModelClass.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.models.auto.auto_factory._BaseAutoModelClass.from_pretrained.config",description:`<strong>config</strong> (<a href="/docs/transformers/pr_15839/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>, <em>optional</em>) &#x2014;
Configuration for the model to use instead of an automatically loaded configuration. Configuration can
be automatically loaded when:</p>
<ul>
<li>The model is a model provided by the 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_15839/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and is reloaded by supplying the
save directory.</li>
<li>The model is loaded by supplying a local directory 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.models.auto.auto_factory._BaseAutoModelClass.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_15839/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and
<a href="/docs/transformers/pr_15839/en/main_classes/model#transformers.PreTrainedModel.from_pretrained">from_pretrained()</a> is not a simpler option.`,name:"state_dict"},{anchor:"transformers.models.auto.auto_factory._BaseAutoModelClass.from_pretrained.cache_dir",description:`<strong>cache_dir</strong> (<code>str</code> or <code>os.PathLike</code>, <em>optional</em>) &#x2014;
Path to a directory in which a downloaded pretrained model configuration should be cached if the
standard cache should not be used.`,name:"cache_dir"},{anchor:"transformers.models.auto.auto_factory._BaseAutoModelClass.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.models.auto.auto_factory._BaseAutoModelClass.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.models.auto.auto_factory._BaseAutoModelClass.from_pretrained.resume_download",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.models.auto.auto_factory._BaseAutoModelClass.from_pretrained.proxies",description:`<strong>proxies</strong> (<code>Dict[str, str]</code>, <em>optional</em>) &#x2014;
A dictionary of proxy servers to use by protocol or endpoint, e.g., <code>{&apos;http&apos;: &apos;foo.bar:3128&apos;, &apos;http://hostname&apos;: &apos;foo.bar:4012&apos;}</code>. The proxies are used on each request.`,name:"proxies"},{anchor:"transformers.models.auto.auto_factory._BaseAutoModelClass.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.models.auto.auto_factory._BaseAutoModelClass.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.models.auto.auto_factory._BaseAutoModelClass.from_pretrained.revision(str,",description:`<strong>revision(<code>str</code>,</strong> <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a
git-based system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any
identifier allowed by git.`,name:"revision(str,"},{anchor:"transformers.models.auto.auto_factory._BaseAutoModelClass.from_pretrained.trust_remote_code",description:`<strong>trust_remote_code</strong> (<code>bool</code>, <em>optional</em>, defaults 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.models.auto.auto_factory._BaseAutoModelClass.from_pretrained.kwargs",description:`<strong>kwargs</strong> (additional keyword arguments, <em>optional</em>) &#x2014;
Can be used to update the configuration object (after it being loaded) and initiate the model (e.g.,
<code>output_attentions=True</code>). Behaves differently depending on whether a <code>config</code> is provided or
automatically loaded:</p>
<ul>
<li>If a configuration is provided with <code>config</code>, <code>**kwargs</code> will be directly passed to the
underlying model&#x2019;s <code>__init__</code> method (we assume all relevant updates to the configuration have
already been done)</li>
<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_15839/en/main_classes/configuration#transformers.PretrainedConfig.from_pretrained">from_pretrained()</a>). Each key of <code>kwargs</code> that
corresponds to a configuration attribute will be used to override said attribute with the
supplied <code>kwargs</code> value. 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"}]}}),My=new w({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>)`}}),Ey=new X({}),yy=new M({props:{name:"class transformers.AutoModelForCTC",anchor:"transformers.AutoModelForCTC",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/pr_15839/src/transformers/models/auto/modeling_auto.py#L845"}}),Ay=new M({props:{name:"from_config",anchor:"transformers.models.auto.auto_factory._BaseAutoModelClass.from_config",parameters:[{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/pr_15839/src/transformers/models/auto/auto_factory.py#L390",parametersDescription:[{anchor:"transformers.models.auto.auto_factory._BaseAutoModelClass.from_config.config",description:`<strong>config</strong> (<a href="/docs/transformers/pr_15839/en/main_classes/configuration#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_15839/en/model_doc/data2vec#transformers.Data2VecAudioConfig">Data2VecAudioConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/data2vec#transformers.Data2VecAudioForCTC">Data2VecAudioForCTC</a> (Data2VecAudio model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/hubert#transformers.HubertConfig">HubertConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/hubert#transformers.HubertForCTC">HubertForCTC</a> (Hubert model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/sew#transformers.SEWConfig">SEWConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/sew#transformers.SEWForCTC">SEWForCTC</a> (SEW model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/sew-d#transformers.SEWDConfig">SEWDConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/sew-d#transformers.SEWDForCTC">SEWDForCTC</a> (SEW-D model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/unispeech#transformers.UniSpeechConfig">UniSpeechConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/unispeech#transformers.UniSpeechForCTC">UniSpeechForCTC</a> (UniSpeech model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/unispeech-sat#transformers.UniSpeechSatConfig">UniSpeechSatConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/unispeech-sat#transformers.UniSpeechSatForCTC">UniSpeechSatForCTC</a> (UniSpeechSat model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/wav2vec2#transformers.Wav2Vec2Config">Wav2Vec2Config</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/wav2vec2#transformers.Wav2Vec2ForCTC">Wav2Vec2ForCTC</a> (Wav2Vec2 model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/wavlm#transformers.WavLMConfig">WavLMConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/wavlm#transformers.WavLMForCTC">WavLMForCTC</a> (WavLM model)</li>
</ul>`,name:"config"}]}}),Ly=new w({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)`}}),By=new M({props:{name:"from_pretrained",anchor:"transformers.models.auto.auto_factory._BaseAutoModelClass.from_pretrained",parameters:[{name:"*model_args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/pr_15839/src/transformers/models/auto/auto_factory.py#L418",parametersDescription:[{anchor:"transformers.models.auto.auto_factory._BaseAutoModelClass.from_pretrained.pretrained_model_name_or_path",description:`<strong>pretrained_model_name_or_path</strong> (<code>str</code> or <code>os.PathLike</code>) &#x2014;
Can be either:</p>
<ul>
<li>A string, the <em>model id</em> of a pretrained model hosted inside a model repo on huggingface.co.
Valid model ids can be located at the root-level, like <code>bert-base-uncased</code>, or namespaced under a
user or organization name, like <code>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_15839/en/main_classes/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.models.auto.auto_factory._BaseAutoModelClass.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.models.auto.auto_factory._BaseAutoModelClass.from_pretrained.config",description:`<strong>config</strong> (<a href="/docs/transformers/pr_15839/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>, <em>optional</em>) &#x2014;
Configuration for the model to use instead of an automatically loaded configuration. Configuration can
be automatically loaded when:</p>
<ul>
<li>The model is a model provided by the 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_15839/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and is reloaded by supplying the
save directory.</li>
<li>The model is loaded by supplying a local directory 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.models.auto.auto_factory._BaseAutoModelClass.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_15839/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and
<a href="/docs/transformers/pr_15839/en/main_classes/model#transformers.PreTrainedModel.from_pretrained">from_pretrained()</a> is not a simpler option.`,name:"state_dict"},{anchor:"transformers.models.auto.auto_factory._BaseAutoModelClass.from_pretrained.cache_dir",description:`<strong>cache_dir</strong> (<code>str</code> or <code>os.PathLike</code>, <em>optional</em>) &#x2014;
Path to a directory in which a downloaded pretrained model configuration should be cached if the
standard cache should not be used.`,name:"cache_dir"},{anchor:"transformers.models.auto.auto_factory._BaseAutoModelClass.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.models.auto.auto_factory._BaseAutoModelClass.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.models.auto.auto_factory._BaseAutoModelClass.from_pretrained.resume_download",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.models.auto.auto_factory._BaseAutoModelClass.from_pretrained.proxies",description:`<strong>proxies</strong> (<code>Dict[str, str]</code>, <em>optional</em>) &#x2014;
A dictionary of proxy servers to use by protocol or endpoint, e.g., <code>{&apos;http&apos;: &apos;foo.bar:3128&apos;, &apos;http://hostname&apos;: &apos;foo.bar:4012&apos;}</code>. The proxies are used on each request.`,name:"proxies"},{anchor:"transformers.models.auto.auto_factory._BaseAutoModelClass.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.models.auto.auto_factory._BaseAutoModelClass.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.models.auto.auto_factory._BaseAutoModelClass.from_pretrained.revision(str,",description:`<strong>revision(<code>str</code>,</strong> <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a
git-based system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any
identifier allowed by git.`,name:"revision(str,"},{anchor:"transformers.models.auto.auto_factory._BaseAutoModelClass.from_pretrained.trust_remote_code",description:`<strong>trust_remote_code</strong> (<code>bool</code>, <em>optional</em>, defaults 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.models.auto.auto_factory._BaseAutoModelClass.from_pretrained.kwargs",description:`<strong>kwargs</strong> (additional keyword arguments, <em>optional</em>) &#x2014;
Can be used to update the configuration object (after it being loaded) and initiate the model (e.g.,
<code>output_attentions=True</code>). Behaves differently depending on whether a <code>config</code> is provided or
automatically loaded:</p>
<ul>
<li>If a configuration is provided with <code>config</code>, <code>**kwargs</code> will be directly passed to the
underlying model&#x2019;s <code>__init__</code> method (we assume all relevant updates to the configuration have
already been done)</li>
<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_15839/en/main_classes/configuration#transformers.PretrainedConfig.from_pretrained">from_pretrained()</a>). Each key of <code>kwargs</code> that
corresponds to a configuration attribute will be used to override said attribute with the
supplied <code>kwargs</code> value. 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"}]}}),xy=new w({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>)`}}),ky=new X({}),Ry=new M({props:{name:"class transformers.AutoModelForSpeechSeq2Seq",anchor:"transformers.AutoModelForSpeechSeq2Seq",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/pr_15839/src/transformers/models/auto/modeling_auto.py#L852"}}),Py=new M({props:{name:"from_config",anchor:"transformers.models.auto.auto_factory._BaseAutoModelClass.from_config",parameters:[{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/pr_15839/src/transformers/models/auto/auto_factory.py#L390",parametersDescription:[{anchor:"transformers.models.auto.auto_factory._BaseAutoModelClass.from_config.config",description:`<strong>config</strong> (<a href="/docs/transformers/pr_15839/en/main_classes/configuration#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_15839/en/model_doc/speech_to_text#transformers.Speech2TextConfig">Speech2TextConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/speech_to_text#transformers.Speech2TextForConditionalGeneration">Speech2TextForConditionalGeneration</a> (Speech2Text model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/speech-encoder-decoder#transformers.SpeechEncoderDecoderConfig">SpeechEncoderDecoderConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/speech-encoder-decoder#transformers.SpeechEncoderDecoderModel">SpeechEncoderDecoderModel</a> (Speech Encoder decoder model)</li>
</ul>`,name:"config"}]}}),$y=new w({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)`}}),Iy=new M({props:{name:"from_pretrained",anchor:"transformers.models.auto.auto_factory._BaseAutoModelClass.from_pretrained",parameters:[{name:"*model_args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/pr_15839/src/transformers/models/auto/auto_factory.py#L418",parametersDescription:[{anchor:"transformers.models.auto.auto_factory._BaseAutoModelClass.from_pretrained.pretrained_model_name_or_path",description:`<strong>pretrained_model_name_or_path</strong> (<code>str</code> or <code>os.PathLike</code>) &#x2014;
Can be either:</p>
<ul>
<li>A string, the <em>model id</em> of a pretrained model hosted inside a model repo on huggingface.co.
Valid model ids can be located at the root-level, like <code>bert-base-uncased</code>, or namespaced under a
user or organization name, like <code>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_15839/en/main_classes/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.models.auto.auto_factory._BaseAutoModelClass.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.models.auto.auto_factory._BaseAutoModelClass.from_pretrained.config",description:`<strong>config</strong> (<a href="/docs/transformers/pr_15839/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>, <em>optional</em>) &#x2014;
Configuration for the model to use instead of an automatically loaded configuration. Configuration can
be automatically loaded when:</p>
<ul>
<li>The model is a model provided by the 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_15839/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and is reloaded by supplying the
save directory.</li>
<li>The model is loaded by supplying a local directory 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.models.auto.auto_factory._BaseAutoModelClass.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_15839/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and
<a href="/docs/transformers/pr_15839/en/main_classes/model#transformers.PreTrainedModel.from_pretrained">from_pretrained()</a> is not a simpler option.`,name:"state_dict"},{anchor:"transformers.models.auto.auto_factory._BaseAutoModelClass.from_pretrained.cache_dir",description:`<strong>cache_dir</strong> (<code>str</code> or <code>os.PathLike</code>, <em>optional</em>) &#x2014;
Path to a directory in which a downloaded pretrained model configuration should be cached if the
standard cache should not be used.`,name:"cache_dir"},{anchor:"transformers.models.auto.auto_factory._BaseAutoModelClass.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.models.auto.auto_factory._BaseAutoModelClass.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.models.auto.auto_factory._BaseAutoModelClass.from_pretrained.resume_download",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.models.auto.auto_factory._BaseAutoModelClass.from_pretrained.proxies",description:`<strong>proxies</strong> (<code>Dict[str, str]</code>, <em>optional</em>) &#x2014;
A dictionary of proxy servers to use by protocol or endpoint, e.g., <code>{&apos;http&apos;: &apos;foo.bar:3128&apos;, &apos;http://hostname&apos;: &apos;foo.bar:4012&apos;}</code>. The proxies are used on each request.`,name:"proxies"},{anchor:"transformers.models.auto.auto_factory._BaseAutoModelClass.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.models.auto.auto_factory._BaseAutoModelClass.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.models.auto.auto_factory._BaseAutoModelClass.from_pretrained.revision(str,",description:`<strong>revision(<code>str</code>,</strong> <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a
git-based system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any
identifier allowed by git.`,name:"revision(str,"},{anchor:"transformers.models.auto.auto_factory._BaseAutoModelClass.from_pretrained.trust_remote_code",description:`<strong>trust_remote_code</strong> (<code>bool</code>, <em>optional</em>, defaults 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.models.auto.auto_factory._BaseAutoModelClass.from_pretrained.kwargs",description:`<strong>kwargs</strong> (additional keyword arguments, <em>optional</em>) &#x2014;
Can be used to update the configuration object (after it being loaded) and initiate the model (e.g.,
<code>output_attentions=True</code>). Behaves differently depending on whether a <code>config</code> is provided or
automatically loaded:</p>
<ul>
<li>If a configuration is provided with <code>config</code>, <code>**kwargs</code> will be directly passed to the
underlying model&#x2019;s <code>__init__</code> method (we assume all relevant updates to the configuration have
already been done)</li>
<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_15839/en/main_classes/configuration#transformers.PretrainedConfig.from_pretrained">from_pretrained()</a>). Each key of <code>kwargs</code> that
corresponds to a configuration attribute will be used to override said attribute with the
supplied <code>kwargs</code> value. 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"}]}}),Dy=new w({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>)`}}),Ny=new X({}),qy=new M({props:{name:"class transformers.AutoModelForAudioXVector",anchor:"transformers.AutoModelForAudioXVector",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/pr_15839/src/transformers/models/auto/modeling_auto.py#L870"}}),Gy=new M({props:{name:"from_config",anchor:"transformers.models.auto.auto_factory._BaseAutoModelClass.from_config",parameters:[{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/pr_15839/src/transformers/models/auto/auto_factory.py#L390",parametersDescription:[{anchor:"transformers.models.auto.auto_factory._BaseAutoModelClass.from_config.config",description:`<strong>config</strong> (<a href="/docs/transformers/pr_15839/en/main_classes/configuration#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_15839/en/model_doc/data2vec#transformers.Data2VecAudioConfig">Data2VecAudioConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/data2vec#transformers.Data2VecAudioForXVector">Data2VecAudioForXVector</a> (Data2VecAudio model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/unispeech-sat#transformers.UniSpeechSatConfig">UniSpeechSatConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/unispeech-sat#transformers.UniSpeechSatForXVector">UniSpeechSatForXVector</a> (UniSpeechSat model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/wav2vec2#transformers.Wav2Vec2Config">Wav2Vec2Config</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/wav2vec2#transformers.Wav2Vec2ForXVector">Wav2Vec2ForXVector</a> (Wav2Vec2 model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/wavlm#transformers.WavLMConfig">WavLMConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/wavlm#transformers.WavLMForXVector">WavLMForXVector</a> (WavLM model)</li>
</ul>`,name:"config"}]}}),Xy=new w({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)`}}),Vy=new M({props:{name:"from_pretrained",anchor:"transformers.models.auto.auto_factory._BaseAutoModelClass.from_pretrained",parameters:[{name:"*model_args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/pr_15839/src/transformers/models/auto/auto_factory.py#L418",parametersDescription:[{anchor:"transformers.models.auto.auto_factory._BaseAutoModelClass.from_pretrained.pretrained_model_name_or_path",description:`<strong>pretrained_model_name_or_path</strong> (<code>str</code> or <code>os.PathLike</code>) &#x2014;
Can be either:</p>
<ul>
<li>A string, the <em>model id</em> of a pretrained model hosted inside a model repo on huggingface.co.
Valid model ids can be located at the root-level, like <code>bert-base-uncased</code>, or namespaced under a
user or organization name, like <code>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_15839/en/main_classes/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.models.auto.auto_factory._BaseAutoModelClass.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.models.auto.auto_factory._BaseAutoModelClass.from_pretrained.config",description:`<strong>config</strong> (<a href="/docs/transformers/pr_15839/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>, <em>optional</em>) &#x2014;
Configuration for the model to use instead of an automatically loaded configuration. Configuration can
be automatically loaded when:</p>
<ul>
<li>The model is a model provided by the 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_15839/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and is reloaded by supplying the
save directory.</li>
<li>The model is loaded by supplying a local directory 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.models.auto.auto_factory._BaseAutoModelClass.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_15839/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and
<a href="/docs/transformers/pr_15839/en/main_classes/model#transformers.PreTrainedModel.from_pretrained">from_pretrained()</a> is not a simpler option.`,name:"state_dict"},{anchor:"transformers.models.auto.auto_factory._BaseAutoModelClass.from_pretrained.cache_dir",description:`<strong>cache_dir</strong> (<code>str</code> or <code>os.PathLike</code>, <em>optional</em>) &#x2014;
Path to a directory in which a downloaded pretrained model configuration should be cached if the
standard cache should not be used.`,name:"cache_dir"},{anchor:"transformers.models.auto.auto_factory._BaseAutoModelClass.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.models.auto.auto_factory._BaseAutoModelClass.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.models.auto.auto_factory._BaseAutoModelClass.from_pretrained.resume_download",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.models.auto.auto_factory._BaseAutoModelClass.from_pretrained.proxies",description:`<strong>proxies</strong> (<code>Dict[str, str]</code>, <em>optional</em>) &#x2014;
A dictionary of proxy servers to use by protocol or endpoint, e.g., <code>{&apos;http&apos;: &apos;foo.bar:3128&apos;, &apos;http://hostname&apos;: &apos;foo.bar:4012&apos;}</code>. The proxies are used on each request.`,name:"proxies"},{anchor:"transformers.models.auto.auto_factory._BaseAutoModelClass.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.models.auto.auto_factory._BaseAutoModelClass.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.models.auto.auto_factory._BaseAutoModelClass.from_pretrained.revision(str,",description:`<strong>revision(<code>str</code>,</strong> <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a
git-based system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any
identifier allowed by git.`,name:"revision(str,"},{anchor:"transformers.models.auto.auto_factory._BaseAutoModelClass.from_pretrained.trust_remote_code",description:`<strong>trust_remote_code</strong> (<code>bool</code>, <em>optional</em>, defaults 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.models.auto.auto_factory._BaseAutoModelClass.from_pretrained.kwargs",description:`<strong>kwargs</strong> (additional keyword arguments, <em>optional</em>) &#x2014;
Can be used to update the configuration object (after it being loaded) and initiate the model (e.g.,
<code>output_attentions=True</code>). Behaves differently depending on whether a <code>config</code> is provided or
automatically loaded:</p>
<ul>
<li>If a configuration is provided with <code>config</code>, <code>**kwargs</code> will be directly passed to the
underlying model&#x2019;s <code>__init__</code> method (we assume all relevant updates to the configuration have
already been done)</li>
<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_15839/en/main_classes/configuration#transformers.PretrainedConfig.from_pretrained">from_pretrained()</a>). Each key of <code>kwargs</code> that
corresponds to a configuration attribute will be used to override said attribute with the
supplied <code>kwargs</code> value. 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"}]}}),zy=new w({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>)`}}),Wy=new X({}),Qy=new M({props:{name:"class transformers.AutoModelForMaskedImageModeling",anchor:"transformers.AutoModelForMaskedImageModeling",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/pr_15839/src/transformers/models/auto/modeling_auto.py#L877"}}),Uy=new M({props:{name:"from_config",anchor:"transformers.models.auto.auto_factory._BaseAutoModelClass.from_config",parameters:[{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/pr_15839/src/transformers/models/auto/auto_factory.py#L390",parametersDescription:[{anchor:"transformers.models.auto.auto_factory._BaseAutoModelClass.from_config.config",description:`<strong>config</strong> (<a href="/docs/transformers/pr_15839/en/main_classes/configuration#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_15839/en/model_doc/deit#transformers.DeiTConfig">DeiTConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/deit#transformers.DeiTForMaskedImageModeling">DeiTForMaskedImageModeling</a> (DeiT model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/swin#transformers.SwinConfig">SwinConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/swin#transformers.SwinForMaskedImageModeling">SwinForMaskedImageModeling</a> (Swin model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/vit#transformers.ViTConfig">ViTConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/vit#transformers.ViTForMaskedImageModeling">ViTForMaskedImageModeling</a> (ViT model)</li>
</ul>`,name:"config"}]}}),Jy=new w({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)`}}),Yy=new M({props:{name:"from_pretrained",anchor:"transformers.models.auto.auto_factory._BaseAutoModelClass.from_pretrained",parameters:[{name:"*model_args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/pr_15839/src/transformers/models/auto/auto_factory.py#L418",parametersDescription:[{anchor:"transformers.models.auto.auto_factory._BaseAutoModelClass.from_pretrained.pretrained_model_name_or_path",description:`<strong>pretrained_model_name_or_path</strong> (<code>str</code> or <code>os.PathLike</code>) &#x2014;
Can be either:</p>
<ul>
<li>A string, the <em>model id</em> of a pretrained model hosted inside a model repo on huggingface.co.
Valid model ids can be located at the root-level, like <code>bert-base-uncased</code>, or namespaced under a
user or organization name, like <code>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_15839/en/main_classes/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.models.auto.auto_factory._BaseAutoModelClass.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.models.auto.auto_factory._BaseAutoModelClass.from_pretrained.config",description:`<strong>config</strong> (<a href="/docs/transformers/pr_15839/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>, <em>optional</em>) &#x2014;
Configuration for the model to use instead of an automatically loaded configuration. Configuration can
be automatically loaded when:</p>
<ul>
<li>The model is a model provided by the 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_15839/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and is reloaded by supplying the
save directory.</li>
<li>The model is loaded by supplying a local directory 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.models.auto.auto_factory._BaseAutoModelClass.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_15839/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and
<a href="/docs/transformers/pr_15839/en/main_classes/model#transformers.PreTrainedModel.from_pretrained">from_pretrained()</a> is not a simpler option.`,name:"state_dict"},{anchor:"transformers.models.auto.auto_factory._BaseAutoModelClass.from_pretrained.cache_dir",description:`<strong>cache_dir</strong> (<code>str</code> or <code>os.PathLike</code>, <em>optional</em>) &#x2014;
Path to a directory in which a downloaded pretrained model configuration should be cached if the
standard cache should not be used.`,name:"cache_dir"},{anchor:"transformers.models.auto.auto_factory._BaseAutoModelClass.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.models.auto.auto_factory._BaseAutoModelClass.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.models.auto.auto_factory._BaseAutoModelClass.from_pretrained.resume_download",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.models.auto.auto_factory._BaseAutoModelClass.from_pretrained.proxies",description:`<strong>proxies</strong> (<code>Dict[str, str]</code>, <em>optional</em>) &#x2014;
A dictionary of proxy servers to use by protocol or endpoint, e.g., <code>{&apos;http&apos;: &apos;foo.bar:3128&apos;, &apos;http://hostname&apos;: &apos;foo.bar:4012&apos;}</code>. The proxies are used on each request.`,name:"proxies"},{anchor:"transformers.models.auto.auto_factory._BaseAutoModelClass.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.models.auto.auto_factory._BaseAutoModelClass.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.models.auto.auto_factory._BaseAutoModelClass.from_pretrained.revision(str,",description:`<strong>revision(<code>str</code>,</strong> <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a
git-based system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any
identifier allowed by git.`,name:"revision(str,"},{anchor:"transformers.models.auto.auto_factory._BaseAutoModelClass.from_pretrained.trust_remote_code",description:`<strong>trust_remote_code</strong> (<code>bool</code>, <em>optional</em>, defaults 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.models.auto.auto_factory._BaseAutoModelClass.from_pretrained.kwargs",description:`<strong>kwargs</strong> (additional keyword arguments, <em>optional</em>) &#x2014;
Can be used to update the configuration object (after it being loaded) and initiate the model (e.g.,
<code>output_attentions=True</code>). Behaves differently depending on whether a <code>config</code> is provided or
automatically loaded:</p>
<ul>
<li>If a configuration is provided with <code>config</code>, <code>**kwargs</code> will be directly passed to the
underlying model&#x2019;s <code>__init__</code> method (we assume all relevant updates to the configuration have
already been done)</li>
<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_15839/en/main_classes/configuration#transformers.PretrainedConfig.from_pretrained">from_pretrained()</a>). Each key of <code>kwargs</code> that
corresponds to a configuration attribute will be used to override said attribute with the
supplied <code>kwargs</code> value. 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"}]}}),Ky=new w({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>)`}}),Zy=new X({}),ew=new M({props:{name:"class transformers.AutoModelForObjectDetection",anchor:"transformers.AutoModelForObjectDetection",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/pr_15839/src/transformers/models/auto/modeling_auto.py#L824"}}),rw=new M({props:{name:"from_config",anchor:"transformers.models.auto.auto_factory._BaseAutoModelClass.from_config",parameters:[{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/pr_15839/src/transformers/models/auto/auto_factory.py#L390",parametersDescription:[{anchor:"transformers.models.auto.auto_factory._BaseAutoModelClass.from_config.config",description:`<strong>config</strong> (<a href="/docs/transformers/pr_15839/en/main_classes/configuration#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_15839/en/model_doc/detr#transformers.DetrConfig">DetrConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/detr#transformers.DetrForObjectDetection">DetrForObjectDetection</a> (DETR model)</li>
</ul>`,name:"config"}]}}),tw=new w({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)`}}),aw=new M({props:{name:"from_pretrained",anchor:"transformers.models.auto.auto_factory._BaseAutoModelClass.from_pretrained",parameters:[{name:"*model_args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/pr_15839/src/transformers/models/auto/auto_factory.py#L418",parametersDescription:[{anchor:"transformers.models.auto.auto_factory._BaseAutoModelClass.from_pretrained.pretrained_model_name_or_path",description:`<strong>pretrained_model_name_or_path</strong> (<code>str</code> or <code>os.PathLike</code>) &#x2014;
Can be either:</p>
<ul>
<li>A string, the <em>model id</em> of a pretrained model hosted inside a model repo on huggingface.co.
Valid model ids can be located at the root-level, like <code>bert-base-uncased</code>, or namespaced under a
user or organization name, like <code>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_15839/en/main_classes/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.models.auto.auto_factory._BaseAutoModelClass.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.models.auto.auto_factory._BaseAutoModelClass.from_pretrained.config",description:`<strong>config</strong> (<a href="/docs/transformers/pr_15839/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>, <em>optional</em>) &#x2014;
Configuration for the model to use instead of an automatically loaded configuration. Configuration can
be automatically loaded when:</p>
<ul>
<li>The model is a model provided by the 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_15839/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and is reloaded by supplying the
save directory.</li>
<li>The model is loaded by supplying a local directory 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.models.auto.auto_factory._BaseAutoModelClass.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_15839/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and
<a href="/docs/transformers/pr_15839/en/main_classes/model#transformers.PreTrainedModel.from_pretrained">from_pretrained()</a> is not a simpler option.`,name:"state_dict"},{anchor:"transformers.models.auto.auto_factory._BaseAutoModelClass.from_pretrained.cache_dir",description:`<strong>cache_dir</strong> (<code>str</code> or <code>os.PathLike</code>, <em>optional</em>) &#x2014;
Path to a directory in which a downloaded pretrained model configuration should be cached if the
standard cache should not be used.`,name:"cache_dir"},{anchor:"transformers.models.auto.auto_factory._BaseAutoModelClass.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.models.auto.auto_factory._BaseAutoModelClass.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.models.auto.auto_factory._BaseAutoModelClass.from_pretrained.resume_download",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.models.auto.auto_factory._BaseAutoModelClass.from_pretrained.proxies",description:`<strong>proxies</strong> (<code>Dict[str, str]</code>, <em>optional</em>) &#x2014;
A dictionary of proxy servers to use by protocol or endpoint, e.g., <code>{&apos;http&apos;: &apos;foo.bar:3128&apos;, &apos;http://hostname&apos;: &apos;foo.bar:4012&apos;}</code>. The proxies are used on each request.`,name:"proxies"},{anchor:"transformers.models.auto.auto_factory._BaseAutoModelClass.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.models.auto.auto_factory._BaseAutoModelClass.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.models.auto.auto_factory._BaseAutoModelClass.from_pretrained.revision(str,",description:`<strong>revision(<code>str</code>,</strong> <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a
git-based system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any
identifier allowed by git.`,name:"revision(str,"},{anchor:"transformers.models.auto.auto_factory._BaseAutoModelClass.from_pretrained.trust_remote_code",description:`<strong>trust_remote_code</strong> (<code>bool</code>, <em>optional</em>, defaults 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.models.auto.auto_factory._BaseAutoModelClass.from_pretrained.kwargs",description:`<strong>kwargs</strong> (additional keyword arguments, <em>optional</em>) &#x2014;
Can be used to update the configuration object (after it being loaded) and initiate the model (e.g.,
<code>output_attentions=True</code>). Behaves differently depending on whether a <code>config</code> is provided or
automatically loaded:</p>
<ul>
<li>If a configuration is provided with <code>config</code>, <code>**kwargs</code> will be directly passed to the
underlying model&#x2019;s <code>__init__</code> method (we assume all relevant updates to the configuration have
already been done)</li>
<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_15839/en/main_classes/configuration#transformers.PretrainedConfig.from_pretrained">from_pretrained()</a>). Each key of <code>kwargs</code> that
corresponds to a configuration attribute will be used to override said attribute with the
supplied <code>kwargs</code> value. 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"}]}}),nw=new w({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>)`}}),sw=new X({}),lw=new M({props:{name:"class transformers.AutoModelForImageSegmentation",anchor:"transformers.AutoModelForImageSegmentation",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/pr_15839/src/transformers/models/auto/modeling_auto.py#L799"}}),dw=new M({props:{name:"from_config",anchor:"transformers.models.auto.auto_factory._BaseAutoModelClass.from_config",parameters:[{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/pr_15839/src/transformers/models/auto/auto_factory.py#L390",parametersDescription:[{anchor:"transformers.models.auto.auto_factory._BaseAutoModelClass.from_config.config",description:`<strong>config</strong> (<a href="/docs/transformers/pr_15839/en/main_classes/configuration#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_15839/en/model_doc/detr#transformers.DetrConfig">DetrConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/detr#transformers.DetrForSegmentation">DetrForSegmentation</a> (DETR model)</li>
</ul>`,name:"config"}]}}),cw=new w({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)`}}),fw=new M({props:{name:"from_pretrained",anchor:"transformers.models.auto.auto_factory._BaseAutoModelClass.from_pretrained",parameters:[{name:"*model_args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/pr_15839/src/transformers/models/auto/auto_factory.py#L418",parametersDescription:[{anchor:"transformers.models.auto.auto_factory._BaseAutoModelClass.from_pretrained.pretrained_model_name_or_path",description:`<strong>pretrained_model_name_or_path</strong> (<code>str</code> or <code>os.PathLike</code>) &#x2014;
Can be either:</p>
<ul>
<li>A string, the <em>model id</em> of a pretrained model hosted inside a model repo on huggingface.co.
Valid model ids can be located at the root-level, like <code>bert-base-uncased</code>, or namespaced under a
user or organization name, like <code>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_15839/en/main_classes/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.models.auto.auto_factory._BaseAutoModelClass.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.models.auto.auto_factory._BaseAutoModelClass.from_pretrained.config",description:`<strong>config</strong> (<a href="/docs/transformers/pr_15839/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>, <em>optional</em>) &#x2014;
Configuration for the model to use instead of an automatically loaded configuration. Configuration can
be automatically loaded when:</p>
<ul>
<li>The model is a model provided by the 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_15839/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and is reloaded by supplying the
save directory.</li>
<li>The model is loaded by supplying a local directory 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.models.auto.auto_factory._BaseAutoModelClass.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_15839/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and
<a href="/docs/transformers/pr_15839/en/main_classes/model#transformers.PreTrainedModel.from_pretrained">from_pretrained()</a> is not a simpler option.`,name:"state_dict"},{anchor:"transformers.models.auto.auto_factory._BaseAutoModelClass.from_pretrained.cache_dir",description:`<strong>cache_dir</strong> (<code>str</code> or <code>os.PathLike</code>, <em>optional</em>) &#x2014;
Path to a directory in which a downloaded pretrained model configuration should be cached if the
standard cache should not be used.`,name:"cache_dir"},{anchor:"transformers.models.auto.auto_factory._BaseAutoModelClass.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.models.auto.auto_factory._BaseAutoModelClass.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.models.auto.auto_factory._BaseAutoModelClass.from_pretrained.resume_download",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.models.auto.auto_factory._BaseAutoModelClass.from_pretrained.proxies",description:`<strong>proxies</strong> (<code>Dict[str, str]</code>, <em>optional</em>) &#x2014;
A dictionary of proxy servers to use by protocol or endpoint, e.g., <code>{&apos;http&apos;: &apos;foo.bar:3128&apos;, &apos;http://hostname&apos;: &apos;foo.bar:4012&apos;}</code>. The proxies are used on each request.`,name:"proxies"},{anchor:"transformers.models.auto.auto_factory._BaseAutoModelClass.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.models.auto.auto_factory._BaseAutoModelClass.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.models.auto.auto_factory._BaseAutoModelClass.from_pretrained.revision(str,",description:`<strong>revision(<code>str</code>,</strong> <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a
git-based system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any
identifier allowed by git.`,name:"revision(str,"},{anchor:"transformers.models.auto.auto_factory._BaseAutoModelClass.from_pretrained.trust_remote_code",description:`<strong>trust_remote_code</strong> (<code>bool</code>, <em>optional</em>, defaults 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.models.auto.auto_factory._BaseAutoModelClass.from_pretrained.kwargs",description:`<strong>kwargs</strong> (additional keyword arguments, <em>optional</em>) &#x2014;
Can be used to update the configuration object (after it being loaded) and initiate the model (e.g.,
<code>output_attentions=True</code>). Behaves differently depending on whether a <code>config</code> is provided or
automatically loaded:</p>
<ul>
<li>If a configuration is provided with <code>config</code>, <code>**kwargs</code> will be directly passed to the
underlying model&#x2019;s <code>__init__</code> method (we assume all relevant updates to the configuration have
already been done)</li>
<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_15839/en/main_classes/configuration#transformers.PretrainedConfig.from_pretrained">from_pretrained()</a>). Each key of <code>kwargs</code> that
corresponds to a configuration attribute will be used to override said attribute with the
supplied <code>kwargs</code> value. 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"}]}}),mw=new w({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>)`}}),gw=new X({}),hw=new M({props:{name:"class transformers.AutoModelForSemanticSegmentation",anchor:"transformers.AutoModelForSemanticSegmentation",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/pr_15839/src/transformers/models/auto/modeling_auto.py#L806"}}),_w=new M({props:{name:"from_config",anchor:"transformers.models.auto.auto_factory._BaseAutoModelClass.from_config",parameters:[{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/pr_15839/src/transformers/models/auto/auto_factory.py#L390",parametersDescription:[{anchor:"transformers.models.auto.auto_factory._BaseAutoModelClass.from_config.config",description:`<strong>config</strong> (<a href="/docs/transformers/pr_15839/en/main_classes/configuration#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_15839/en/model_doc/beit#transformers.BeitConfig">BeitConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/beit#transformers.BeitForSemanticSegmentation">BeitForSemanticSegmentation</a> (BEiT model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/segformer#transformers.SegformerConfig">SegformerConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/segformer#transformers.SegformerForSemanticSegmentation">SegformerForSemanticSegmentation</a> (SegFormer model)</li>
</ul>`,name:"config"}]}}),uw=new w({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)`}}),bw=new M({props:{name:"from_pretrained",anchor:"transformers.models.auto.auto_factory._BaseAutoModelClass.from_pretrained",parameters:[{name:"*model_args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/pr_15839/src/transformers/models/auto/auto_factory.py#L418",parametersDescription:[{anchor:"transformers.models.auto.auto_factory._BaseAutoModelClass.from_pretrained.pretrained_model_name_or_path",description:`<strong>pretrained_model_name_or_path</strong> (<code>str</code> or <code>os.PathLike</code>) &#x2014;
Can be either:</p>
<ul>
<li>A string, the <em>model id</em> of a pretrained model hosted inside a model repo on huggingface.co.
Valid model ids can be located at the root-level, like <code>bert-base-uncased</code>, or namespaced under a
user or organization name, like <code>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_15839/en/main_classes/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.models.auto.auto_factory._BaseAutoModelClass.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.models.auto.auto_factory._BaseAutoModelClass.from_pretrained.config",description:`<strong>config</strong> (<a href="/docs/transformers/pr_15839/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>, <em>optional</em>) &#x2014;
Configuration for the model to use instead of an automatically loaded configuration. Configuration can
be automatically loaded when:</p>
<ul>
<li>The model is a model provided by the 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_15839/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and is reloaded by supplying the
save directory.</li>
<li>The model is loaded by supplying a local directory 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.models.auto.auto_factory._BaseAutoModelClass.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_15839/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and
<a href="/docs/transformers/pr_15839/en/main_classes/model#transformers.PreTrainedModel.from_pretrained">from_pretrained()</a> is not a simpler option.`,name:"state_dict"},{anchor:"transformers.models.auto.auto_factory._BaseAutoModelClass.from_pretrained.cache_dir",description:`<strong>cache_dir</strong> (<code>str</code> or <code>os.PathLike</code>, <em>optional</em>) &#x2014;
Path to a directory in which a downloaded pretrained model configuration should be cached if the
standard cache should not be used.`,name:"cache_dir"},{anchor:"transformers.models.auto.auto_factory._BaseAutoModelClass.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.models.auto.auto_factory._BaseAutoModelClass.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.models.auto.auto_factory._BaseAutoModelClass.from_pretrained.resume_download",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.models.auto.auto_factory._BaseAutoModelClass.from_pretrained.proxies",description:`<strong>proxies</strong> (<code>Dict[str, str]</code>, <em>optional</em>) &#x2014;
A dictionary of proxy servers to use by protocol or endpoint, e.g., <code>{&apos;http&apos;: &apos;foo.bar:3128&apos;, &apos;http://hostname&apos;: &apos;foo.bar:4012&apos;}</code>. The proxies are used on each request.`,name:"proxies"},{anchor:"transformers.models.auto.auto_factory._BaseAutoModelClass.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.models.auto.auto_factory._BaseAutoModelClass.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.models.auto.auto_factory._BaseAutoModelClass.from_pretrained.revision(str,",description:`<strong>revision(<code>str</code>,</strong> <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a
git-based system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any
identifier allowed by git.`,name:"revision(str,"},{anchor:"transformers.models.auto.auto_factory._BaseAutoModelClass.from_pretrained.trust_remote_code",description:`<strong>trust_remote_code</strong> (<code>bool</code>, <em>optional</em>, defaults 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.models.auto.auto_factory._BaseAutoModelClass.from_pretrained.kwargs",description:`<strong>kwargs</strong> (additional keyword arguments, <em>optional</em>) &#x2014;
Can be used to update the configuration object (after it being loaded) and initiate the model (e.g.,
<code>output_attentions=True</code>). Behaves differently depending on whether a <code>config</code> is provided or
automatically loaded:</p>
<ul>
<li>If a configuration is provided with <code>config</code>, <code>**kwargs</code> will be directly passed to the
underlying model&#x2019;s <code>__init__</code> method (we assume all relevant updates to the configuration have
already been done)</li>
<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_15839/en/main_classes/configuration#transformers.PretrainedConfig.from_pretrained">from_pretrained()</a>). Each key of <code>kwargs</code> that
corresponds to a configuration attribute will be used to override said attribute with the
supplied <code>kwargs</code> value. 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"}]}}),Tw=new w({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>)`}}),Fw=new X({}),Cw=new M({props:{name:"class transformers.AutoModelForInstanceSegmentation",anchor:"transformers.AutoModelForInstanceSegmentation",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/pr_15839/src/transformers/models/auto/modeling_auto.py#L815"}}),Ew=new M({props:{name:"from_config",anchor:"transformers.models.auto.auto_factory._BaseAutoModelClass.from_config",parameters:[{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/pr_15839/src/transformers/models/auto/auto_factory.py#L390",parametersDescription:[{anchor:"transformers.models.auto.auto_factory._BaseAutoModelClass.from_config.config",description:`<strong>config</strong> (<a href="/docs/transformers/pr_15839/en/main_classes/configuration#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_15839/en/model_doc/maskformer#transformers.MaskFormerConfig">MaskFormerConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/maskformer#transformers.MaskFormerForInstanceSegmentation">MaskFormerForInstanceSegmentation</a> (MaskFormer model)</li>
</ul>`,name:"config"}]}}),yw=new w({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)`}}),ww=new M({props:{name:"from_pretrained",anchor:"transformers.models.auto.auto_factory._BaseAutoModelClass.from_pretrained",parameters:[{name:"*model_args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/pr_15839/src/transformers/models/auto/auto_factory.py#L418",parametersDescription:[{anchor:"transformers.models.auto.auto_factory._BaseAutoModelClass.from_pretrained.pretrained_model_name_or_path",description:`<strong>pretrained_model_name_or_path</strong> (<code>str</code> or <code>os.PathLike</code>) &#x2014;
Can be either:</p>
<ul>
<li>A string, the <em>model id</em> of a pretrained model hosted inside a model repo on huggingface.co.
Valid model ids can be located at the root-level, like <code>bert-base-uncased</code>, or namespaced under a
user or organization name, like <code>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_15839/en/main_classes/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.models.auto.auto_factory._BaseAutoModelClass.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.models.auto.auto_factory._BaseAutoModelClass.from_pretrained.config",description:`<strong>config</strong> (<a href="/docs/transformers/pr_15839/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>, <em>optional</em>) &#x2014;
Configuration for the model to use instead of an automatically loaded configuration. Configuration can
be automatically loaded when:</p>
<ul>
<li>The model is a model provided by the 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_15839/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and is reloaded by supplying the
save directory.</li>
<li>The model is loaded by supplying a local directory 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.models.auto.auto_factory._BaseAutoModelClass.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_15839/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and
<a href="/docs/transformers/pr_15839/en/main_classes/model#transformers.PreTrainedModel.from_pretrained">from_pretrained()</a> is not a simpler option.`,name:"state_dict"},{anchor:"transformers.models.auto.auto_factory._BaseAutoModelClass.from_pretrained.cache_dir",description:`<strong>cache_dir</strong> (<code>str</code> or <code>os.PathLike</code>, <em>optional</em>) &#x2014;
Path to a directory in which a downloaded pretrained model configuration should be cached if the
standard cache should not be used.`,name:"cache_dir"},{anchor:"transformers.models.auto.auto_factory._BaseAutoModelClass.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.models.auto.auto_factory._BaseAutoModelClass.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.models.auto.auto_factory._BaseAutoModelClass.from_pretrained.resume_download",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.models.auto.auto_factory._BaseAutoModelClass.from_pretrained.proxies",description:`<strong>proxies</strong> (<code>Dict[str, str]</code>, <em>optional</em>) &#x2014;
A dictionary of proxy servers to use by protocol or endpoint, e.g., <code>{&apos;http&apos;: &apos;foo.bar:3128&apos;, &apos;http://hostname&apos;: &apos;foo.bar:4012&apos;}</code>. The proxies are used on each request.`,name:"proxies"},{anchor:"transformers.models.auto.auto_factory._BaseAutoModelClass.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.models.auto.auto_factory._BaseAutoModelClass.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.models.auto.auto_factory._BaseAutoModelClass.from_pretrained.revision(str,",description:`<strong>revision(<code>str</code>,</strong> <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a
git-based system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any
identifier allowed by git.`,name:"revision(str,"},{anchor:"transformers.models.auto.auto_factory._BaseAutoModelClass.from_pretrained.trust_remote_code",description:`<strong>trust_remote_code</strong> (<code>bool</code>, <em>optional</em>, defaults 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.models.auto.auto_factory._BaseAutoModelClass.from_pretrained.kwargs",description:`<strong>kwargs</strong> (additional keyword arguments, <em>optional</em>) &#x2014;
Can be used to update the configuration object (after it being loaded) and initiate the model (e.g.,
<code>output_attentions=True</code>). Behaves differently depending on whether a <code>config</code> is provided or
automatically loaded:</p>
<ul>
<li>If a configuration is provided with <code>config</code>, <code>**kwargs</code> will be directly passed to the
underlying model&#x2019;s <code>__init__</code> method (we assume all relevant updates to the configuration have
already been done)</li>
<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_15839/en/main_classes/configuration#transformers.PretrainedConfig.from_pretrained">from_pretrained()</a>). Each key of <code>kwargs</code> that
corresponds to a configuration attribute will be used to override said attribute with the
supplied <code>kwargs</code> value. 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"}]}}),Aw=new w({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>)`}}),Lw=new X({}),Bw=new M({props:{name:"class transformers.TFAutoModel",anchor:"transformers.TFAutoModel",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/pr_15839/src/transformers/models/auto/modeling_tf_auto.py#L374"}}),kw=new M({props:{name:"from_config",anchor:"transformers.models.auto.auto_factory._BaseAutoModelClass.from_config",parameters:[{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/pr_15839/src/transformers/models/auto/auto_factory.py#L390",parametersDescription:[{anchor:"transformers.models.auto.auto_factory._BaseAutoModelClass.from_config.config",description:`<strong>config</strong> (<a href="/docs/transformers/pr_15839/en/main_classes/configuration#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_15839/en/model_doc/albert#transformers.AlbertConfig">AlbertConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/albert#transformers.TFAlbertModel">TFAlbertModel</a> (ALBERT model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/bart#transformers.BartConfig">BartConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/bart#transformers.TFBartModel">TFBartModel</a> (BART model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/bert#transformers.BertConfig">BertConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/bert#transformers.TFBertModel">TFBertModel</a> (BERT model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/blenderbot#transformers.BlenderbotConfig">BlenderbotConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/blenderbot#transformers.TFBlenderbotModel">TFBlenderbotModel</a> (Blenderbot model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/blenderbot-small#transformers.BlenderbotSmallConfig">BlenderbotSmallConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/blenderbot-small#transformers.TFBlenderbotSmallModel">TFBlenderbotSmallModel</a> (BlenderbotSmall model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/clip#transformers.CLIPConfig">CLIPConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/clip#transformers.TFCLIPModel">TFCLIPModel</a> (CLIP model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/ctrl#transformers.CTRLConfig">CTRLConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/ctrl#transformers.TFCTRLModel">TFCTRLModel</a> (CTRL model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/camembert#transformers.CamembertConfig">CamembertConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/camembert#transformers.TFCamembertModel">TFCamembertModel</a> (CamemBERT model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/convbert#transformers.ConvBertConfig">ConvBertConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/convbert#transformers.TFConvBertModel">TFConvBertModel</a> (ConvBERT model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/convnext#transformers.ConvNextConfig">ConvNextConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/convnext#transformers.TFConvNextModel">TFConvNextModel</a> (ConvNext model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/dpr#transformers.DPRConfig">DPRConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/dpr#transformers.TFDPRQuestionEncoder">TFDPRQuestionEncoder</a> (DPR model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/deberta#transformers.DebertaConfig">DebertaConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/deberta#transformers.TFDebertaModel">TFDebertaModel</a> (DeBERTa model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/deberta-v2#transformers.DebertaV2Config">DebertaV2Config</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/deberta-v2#transformers.TFDebertaV2Model">TFDebertaV2Model</a> (DeBERTa-v2 model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/distilbert#transformers.DistilBertConfig">DistilBertConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/distilbert#transformers.TFDistilBertModel">TFDistilBertModel</a> (DistilBERT model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/electra#transformers.ElectraConfig">ElectraConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/electra#transformers.TFElectraModel">TFElectraModel</a> (ELECTRA model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/flaubert#transformers.FlaubertConfig">FlaubertConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/flaubert#transformers.TFFlaubertModel">TFFlaubertModel</a> (FlauBERT model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/funnel#transformers.FunnelConfig">FunnelConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/funnel#transformers.TFFunnelModel">TFFunnelModel</a> or <a href="/docs/transformers/pr_15839/en/model_doc/funnel#transformers.TFFunnelBaseModel">TFFunnelBaseModel</a> (Funnel Transformer model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/gpt2#transformers.GPT2Config">GPT2Config</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/gpt2#transformers.TFGPT2Model">TFGPT2Model</a> (OpenAI GPT-2 model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/hubert#transformers.HubertConfig">HubertConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/hubert#transformers.TFHubertModel">TFHubertModel</a> (Hubert model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/led#transformers.LEDConfig">LEDConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/led#transformers.TFLEDModel">TFLEDModel</a> (LED model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/layoutlm#transformers.LayoutLMConfig">LayoutLMConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/layoutlm#transformers.TFLayoutLMModel">TFLayoutLMModel</a> (LayoutLM model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/longformer#transformers.LongformerConfig">LongformerConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/longformer#transformers.TFLongformerModel">TFLongformerModel</a> (Longformer model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/lxmert#transformers.LxmertConfig">LxmertConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/lxmert#transformers.TFLxmertModel">TFLxmertModel</a> (LXMERT model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/mbart#transformers.MBartConfig">MBartConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/mbart#transformers.TFMBartModel">TFMBartModel</a> (mBART model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/mpnet#transformers.MPNetConfig">MPNetConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/mpnet#transformers.TFMPNetModel">TFMPNetModel</a> (MPNet model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/mt5#transformers.MT5Config">MT5Config</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/mt5#transformers.TFMT5Model">TFMT5Model</a> (mT5 model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/marian#transformers.MarianConfig">MarianConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/marian#transformers.TFMarianModel">TFMarianModel</a> (Marian model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/mobilebert#transformers.MobileBertConfig">MobileBertConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/mobilebert#transformers.TFMobileBertModel">TFMobileBertModel</a> (MobileBERT model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/openai-gpt#transformers.OpenAIGPTConfig">OpenAIGPTConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/openai-gpt#transformers.TFOpenAIGPTModel">TFOpenAIGPTModel</a> (OpenAI GPT model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/pegasus#transformers.PegasusConfig">PegasusConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/pegasus#transformers.TFPegasusModel">TFPegasusModel</a> (Pegasus model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/rembert#transformers.RemBertConfig">RemBertConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/rembert#transformers.TFRemBertModel">TFRemBertModel</a> (RemBERT model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/roformer#transformers.RoFormerConfig">RoFormerConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/roformer#transformers.TFRoFormerModel">TFRoFormerModel</a> (RoFormer model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/roberta#transformers.RobertaConfig">RobertaConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/roberta#transformers.TFRobertaModel">TFRobertaModel</a> (RoBERTa model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/speech_to_text#transformers.Speech2TextConfig">Speech2TextConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/speech_to_text#transformers.TFSpeech2TextModel">TFSpeech2TextModel</a> (Speech2Text model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/t5#transformers.T5Config">T5Config</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/t5#transformers.TFT5Model">TFT5Model</a> (T5 model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/tapas#transformers.TapasConfig">TapasConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/tapas#transformers.TFTapasModel">TFTapasModel</a> (TAPAS model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/transfo-xl#transformers.TransfoXLConfig">TransfoXLConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/transfo-xl#transformers.TFTransfoXLModel">TFTransfoXLModel</a> (Transformer-XL model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/vit#transformers.ViTConfig">ViTConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/vit#transformers.TFViTModel">TFViTModel</a> (ViT model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/wav2vec2#transformers.Wav2Vec2Config">Wav2Vec2Config</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/wav2vec2#transformers.TFWav2Vec2Model">TFWav2Vec2Model</a> (Wav2Vec2 model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/xlm#transformers.XLMConfig">XLMConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/xlm#transformers.TFXLMModel">TFXLMModel</a> (XLM model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/xlm-roberta#transformers.XLMRobertaConfig">XLMRobertaConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/xlm-roberta#transformers.TFXLMRobertaModel">TFXLMRobertaModel</a> (XLM-RoBERTa model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/xlnet#transformers.XLNetConfig">XLNetConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/xlnet#transformers.TFXLNetModel">TFXLNetModel</a> (XLNet model)</li>
</ul>`,name:"config"}]}}),Rw=new w({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)`}}),Sw=new M({props:{name:"from_pretrained",anchor:"transformers.models.auto.auto_factory._BaseAutoModelClass.from_pretrained",parameters:[{name:"*model_args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/pr_15839/src/transformers/models/auto/auto_factory.py#L418",parametersDescription:[{anchor:"transformers.models.auto.auto_factory._BaseAutoModelClass.from_pretrained.pretrained_model_name_or_path",description:`<strong>pretrained_model_name_or_path</strong> (<code>str</code> or <code>os.PathLike</code>) &#x2014;
Can be either:</p>
<ul>
<li>A string, the <em>model id</em> of a pretrained model hosted inside a model repo on huggingface.co.
Valid model ids can be located at the root-level, like <code>bert-base-uncased</code>, or namespaced under a
user or organization name, like <code>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_15839/en/main_classes/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.models.auto.auto_factory._BaseAutoModelClass.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.models.auto.auto_factory._BaseAutoModelClass.from_pretrained.config",description:`<strong>config</strong> (<a href="/docs/transformers/pr_15839/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>, <em>optional</em>) &#x2014;
Configuration for the model to use instead of an automatically loaded configuration. Configuration can
be automatically loaded when:</p>
<ul>
<li>The model is a model provided by the 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_15839/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and is reloaded by supplying the
save directory.</li>
<li>The model is loaded by supplying a local directory 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.models.auto.auto_factory._BaseAutoModelClass.from_pretrained.cache_dir",description:`<strong>cache_dir</strong> (<code>str</code> or <code>os.PathLike</code>, <em>optional</em>) &#x2014;
Path to a directory in which a downloaded pretrained model configuration should be cached if the
standard cache should not be used.`,name:"cache_dir"},{anchor:"transformers.models.auto.auto_factory._BaseAutoModelClass.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.models.auto.auto_factory._BaseAutoModelClass.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.models.auto.auto_factory._BaseAutoModelClass.from_pretrained.resume_download",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.models.auto.auto_factory._BaseAutoModelClass.from_pretrained.proxies",description:`<strong>proxies</strong> (<code>Dict[str, str]</code>, <em>optional</em>) &#x2014;
A dictionary of proxy servers to use by protocol or endpoint, e.g., <code>{&apos;http&apos;: &apos;foo.bar:3128&apos;, &apos;http://hostname&apos;: &apos;foo.bar:4012&apos;}</code>. The proxies are used on each request.`,name:"proxies"},{anchor:"transformers.models.auto.auto_factory._BaseAutoModelClass.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.models.auto.auto_factory._BaseAutoModelClass.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.models.auto.auto_factory._BaseAutoModelClass.from_pretrained.revision(str,",description:`<strong>revision(<code>str</code>,</strong> <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a
git-based system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any
identifier allowed by git.`,name:"revision(str,"},{anchor:"transformers.models.auto.auto_factory._BaseAutoModelClass.from_pretrained.trust_remote_code",description:`<strong>trust_remote_code</strong> (<code>bool</code>, <em>optional</em>, defaults 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.models.auto.auto_factory._BaseAutoModelClass.from_pretrained.kwargs",description:`<strong>kwargs</strong> (additional keyword arguments, <em>optional</em>) &#x2014;
Can be used to update the configuration object (after it being loaded) and initiate the model (e.g.,
<code>output_attentions=True</code>). Behaves differently depending on whether a <code>config</code> is provided or
automatically loaded:</p>
<ul>
<li>If a configuration is provided with <code>config</code>, <code>**kwargs</code> will be directly passed to the
underlying model&#x2019;s <code>__init__</code> method (we assume all relevant updates to the configuration have
already been done)</li>
<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_15839/en/main_classes/configuration#transformers.PretrainedConfig.from_pretrained">from_pretrained()</a>). Each key of <code>kwargs</code> that
corresponds to a configuration attribute will be used to override said attribute with the
supplied <code>kwargs</code> value. 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"}]}}),Pw=new w({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>)`}}),$w=new X({}),Iw=new M({props:{name:"class transformers.TFAutoModelForPreTraining",anchor:"transformers.TFAutoModelForPreTraining",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/pr_15839/src/transformers/models/auto/modeling_tf_auto.py#L381"}}),Dw=new M({props:{name:"from_config",anchor:"transformers.models.auto.auto_factory._BaseAutoModelClass.from_config",parameters:[{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/pr_15839/src/transformers/models/auto/auto_factory.py#L390",parametersDescription:[{anchor:"transformers.models.auto.auto_factory._BaseAutoModelClass.from_config.config",description:`<strong>config</strong> (<a href="/docs/transformers/pr_15839/en/main_classes/configuration#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_15839/en/model_doc/albert#transformers.AlbertConfig">AlbertConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/albert#transformers.TFAlbertForPreTraining">TFAlbertForPreTraining</a> (ALBERT model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/bart#transformers.BartConfig">BartConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/bart#transformers.TFBartForConditionalGeneration">TFBartForConditionalGeneration</a> (BART model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/bert#transformers.BertConfig">BertConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/bert#transformers.TFBertForPreTraining">TFBertForPreTraining</a> (BERT model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/ctrl#transformers.CTRLConfig">CTRLConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/ctrl#transformers.TFCTRLLMHeadModel">TFCTRLLMHeadModel</a> (CTRL model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/camembert#transformers.CamembertConfig">CamembertConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/camembert#transformers.TFCamembertForMaskedLM">TFCamembertForMaskedLM</a> (CamemBERT model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/distilbert#transformers.DistilBertConfig">DistilBertConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/distilbert#transformers.TFDistilBertForMaskedLM">TFDistilBertForMaskedLM</a> (DistilBERT model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/electra#transformers.ElectraConfig">ElectraConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/electra#transformers.TFElectraForPreTraining">TFElectraForPreTraining</a> (ELECTRA model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/flaubert#transformers.FlaubertConfig">FlaubertConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/flaubert#transformers.TFFlaubertWithLMHeadModel">TFFlaubertWithLMHeadModel</a> (FlauBERT model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/funnel#transformers.FunnelConfig">FunnelConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/funnel#transformers.TFFunnelForPreTraining">TFFunnelForPreTraining</a> (Funnel Transformer model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/gpt2#transformers.GPT2Config">GPT2Config</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/gpt2#transformers.TFGPT2LMHeadModel">TFGPT2LMHeadModel</a> (OpenAI GPT-2 model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/layoutlm#transformers.LayoutLMConfig">LayoutLMConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/layoutlm#transformers.TFLayoutLMForMaskedLM">TFLayoutLMForMaskedLM</a> (LayoutLM model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/lxmert#transformers.LxmertConfig">LxmertConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/lxmert#transformers.TFLxmertForPreTraining">TFLxmertForPreTraining</a> (LXMERT model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/mpnet#transformers.MPNetConfig">MPNetConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/mpnet#transformers.TFMPNetForMaskedLM">TFMPNetForMaskedLM</a> (MPNet model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/mobilebert#transformers.MobileBertConfig">MobileBertConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/mobilebert#transformers.TFMobileBertForPreTraining">TFMobileBertForPreTraining</a> (MobileBERT model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/openai-gpt#transformers.OpenAIGPTConfig">OpenAIGPTConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/openai-gpt#transformers.TFOpenAIGPTLMHeadModel">TFOpenAIGPTLMHeadModel</a> (OpenAI GPT model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/roberta#transformers.RobertaConfig">RobertaConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/roberta#transformers.TFRobertaForMaskedLM">TFRobertaForMaskedLM</a> (RoBERTa model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/t5#transformers.T5Config">T5Config</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/t5#transformers.TFT5ForConditionalGeneration">TFT5ForConditionalGeneration</a> (T5 model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/tapas#transformers.TapasConfig">TapasConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/tapas#transformers.TFTapasForMaskedLM">TFTapasForMaskedLM</a> (TAPAS model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/transfo-xl#transformers.TransfoXLConfig">TransfoXLConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/transfo-xl#transformers.TFTransfoXLLMHeadModel">TFTransfoXLLMHeadModel</a> (Transformer-XL model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/xlm#transformers.XLMConfig">XLMConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/xlm#transformers.TFXLMWithLMHeadModel">TFXLMWithLMHeadModel</a> (XLM model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/xlm-roberta#transformers.XLMRobertaConfig">XLMRobertaConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/xlm-roberta#transformers.TFXLMRobertaForMaskedLM">TFXLMRobertaForMaskedLM</a> (XLM-RoBERTa model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/xlnet#transformers.XLNetConfig">XLNetConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/xlnet#transformers.TFXLNetLMHeadModel">TFXLNetLMHeadModel</a> (XLNet model)</li>
</ul>`,name:"config"}]}}),Nw=new w({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)`}}),qw=new M({props:{name:"from_pretrained",anchor:"transformers.models.auto.auto_factory._BaseAutoModelClass.from_pretrained",parameters:[{name:"*model_args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/pr_15839/src/transformers/models/auto/auto_factory.py#L418",parametersDescription:[{anchor:"transformers.models.auto.auto_factory._BaseAutoModelClass.from_pretrained.pretrained_model_name_or_path",description:`<strong>pretrained_model_name_or_path</strong> (<code>str</code> or <code>os.PathLike</code>) &#x2014;
Can be either:</p>
<ul>
<li>A string, the <em>model id</em> of a pretrained model hosted inside a model repo on huggingface.co.
Valid model ids can be located at the root-level, like <code>bert-base-uncased</code>, or namespaced under a
user or organization name, like <code>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_15839/en/main_classes/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.models.auto.auto_factory._BaseAutoModelClass.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.models.auto.auto_factory._BaseAutoModelClass.from_pretrained.config",description:`<strong>config</strong> (<a href="/docs/transformers/pr_15839/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>, <em>optional</em>) &#x2014;
Configuration for the model to use instead of an automatically loaded configuration. Configuration can
be automatically loaded when:</p>
<ul>
<li>The model is a model provided by the 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_15839/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and is reloaded by supplying the
save directory.</li>
<li>The model is loaded by supplying a local directory 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.models.auto.auto_factory._BaseAutoModelClass.from_pretrained.cache_dir",description:`<strong>cache_dir</strong> (<code>str</code> or <code>os.PathLike</code>, <em>optional</em>) &#x2014;
Path to a directory in which a downloaded pretrained model configuration should be cached if the
standard cache should not be used.`,name:"cache_dir"},{anchor:"transformers.models.auto.auto_factory._BaseAutoModelClass.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.models.auto.auto_factory._BaseAutoModelClass.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.models.auto.auto_factory._BaseAutoModelClass.from_pretrained.resume_download",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.models.auto.auto_factory._BaseAutoModelClass.from_pretrained.proxies",description:`<strong>proxies</strong> (<code>Dict[str, str]</code>, <em>optional</em>) &#x2014;
A dictionary of proxy servers to use by protocol or endpoint, e.g., <code>{&apos;http&apos;: &apos;foo.bar:3128&apos;, &apos;http://hostname&apos;: &apos;foo.bar:4012&apos;}</code>. The proxies are used on each request.`,name:"proxies"},{anchor:"transformers.models.auto.auto_factory._BaseAutoModelClass.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.models.auto.auto_factory._BaseAutoModelClass.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.models.auto.auto_factory._BaseAutoModelClass.from_pretrained.revision(str,",description:`<strong>revision(<code>str</code>,</strong> <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a
git-based system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any
identifier allowed by git.`,name:"revision(str,"},{anchor:"transformers.models.auto.auto_factory._BaseAutoModelClass.from_pretrained.trust_remote_code",description:`<strong>trust_remote_code</strong> (<code>bool</code>, <em>optional</em>, defaults 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.models.auto.auto_factory._BaseAutoModelClass.from_pretrained.kwargs",description:`<strong>kwargs</strong> (additional keyword arguments, <em>optional</em>) &#x2014;
Can be used to update the configuration object (after it being loaded) and initiate the model (e.g.,
<code>output_attentions=True</code>). Behaves differently depending on whether a <code>config</code> is provided or
automatically loaded:</p>
<ul>
<li>If a configuration is provided with <code>config</code>, <code>**kwargs</code> will be directly passed to the
underlying model&#x2019;s <code>__init__</code> method (we assume all relevant updates to the configuration have
already been done)</li>
<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_15839/en/main_classes/configuration#transformers.PretrainedConfig.from_pretrained">from_pretrained()</a>). Each key of <code>kwargs</code> that
corresponds to a configuration attribute will be used to override said attribute with the
supplied <code>kwargs</code> value. 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"}]}}),Ow=new w({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>)`}}),Gw=new X({}),Xw=new M({props:{name:"class transformers.TFAutoModelForCausalLM",anchor:"transformers.TFAutoModelForCausalLM",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/pr_15839/src/transformers/models/auto/modeling_tf_auto.py#L396"}}),zw=new M({props:{name:"from_config",anchor:"transformers.models.auto.auto_factory._BaseAutoModelClass.from_config",parameters:[{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/pr_15839/src/transformers/models/auto/auto_factory.py#L390",parametersDescription:[{anchor:"transformers.models.auto.auto_factory._BaseAutoModelClass.from_config.config",description:`<strong>config</strong> (<a href="/docs/transformers/pr_15839/en/main_classes/configuration#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_15839/en/model_doc/bert#transformers.BertConfig">BertConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/bert#transformers.TFBertLMHeadModel">TFBertLMHeadModel</a> (BERT model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/ctrl#transformers.CTRLConfig">CTRLConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/ctrl#transformers.TFCTRLLMHeadModel">TFCTRLLMHeadModel</a> (CTRL model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/camembert#transformers.CamembertConfig">CamembertConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/camembert#transformers.TFCamembertForCausalLM">TFCamembertForCausalLM</a> (CamemBERT model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/gpt2#transformers.GPT2Config">GPT2Config</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/gpt2#transformers.TFGPT2LMHeadModel">TFGPT2LMHeadModel</a> (OpenAI GPT-2 model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/openai-gpt#transformers.OpenAIGPTConfig">OpenAIGPTConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/openai-gpt#transformers.TFOpenAIGPTLMHeadModel">TFOpenAIGPTLMHeadModel</a> (OpenAI GPT model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/rembert#transformers.RemBertConfig">RemBertConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/rembert#transformers.TFRemBertForCausalLM">TFRemBertForCausalLM</a> (RemBERT model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/roformer#transformers.RoFormerConfig">RoFormerConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/roformer#transformers.TFRoFormerForCausalLM">TFRoFormerForCausalLM</a> (RoFormer model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/roberta#transformers.RobertaConfig">RobertaConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/roberta#transformers.TFRobertaForCausalLM">TFRobertaForCausalLM</a> (RoBERTa model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/transfo-xl#transformers.TransfoXLConfig">TransfoXLConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/transfo-xl#transformers.TFTransfoXLLMHeadModel">TFTransfoXLLMHeadModel</a> (Transformer-XL model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/xlm#transformers.XLMConfig">XLMConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/xlm#transformers.TFXLMWithLMHeadModel">TFXLMWithLMHeadModel</a> (XLM model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/xlnet#transformers.XLNetConfig">XLNetConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/xlnet#transformers.TFXLNetLMHeadModel">TFXLNetLMHeadModel</a> (XLNet model)</li>
</ul>`,name:"config"}]}}),Ww=new w({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)`}}),Qw=new M({props:{name:"from_pretrained",anchor:"transformers.models.auto.auto_factory._BaseAutoModelClass.from_pretrained",parameters:[{name:"*model_args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/pr_15839/src/transformers/models/auto/auto_factory.py#L418",parametersDescription:[{anchor:"transformers.models.auto.auto_factory._BaseAutoModelClass.from_pretrained.pretrained_model_name_or_path",description:`<strong>pretrained_model_name_or_path</strong> (<code>str</code> or <code>os.PathLike</code>) &#x2014;
Can be either:</p>
<ul>
<li>A string, the <em>model id</em> of a pretrained model hosted inside a model repo on huggingface.co.
Valid model ids can be located at the root-level, like <code>bert-base-uncased</code>, or namespaced under a
user or organization name, like <code>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_15839/en/main_classes/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.models.auto.auto_factory._BaseAutoModelClass.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.models.auto.auto_factory._BaseAutoModelClass.from_pretrained.config",description:`<strong>config</strong> (<a href="/docs/transformers/pr_15839/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>, <em>optional</em>) &#x2014;
Configuration for the model to use instead of an automatically loaded configuration. Configuration can
be automatically loaded when:</p>
<ul>
<li>The model is a model provided by the 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_15839/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and is reloaded by supplying the
save directory.</li>
<li>The model is loaded by supplying a local directory 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.models.auto.auto_factory._BaseAutoModelClass.from_pretrained.cache_dir",description:`<strong>cache_dir</strong> (<code>str</code> or <code>os.PathLike</code>, <em>optional</em>) &#x2014;
Path to a directory in which a downloaded pretrained model configuration should be cached if the
standard cache should not be used.`,name:"cache_dir"},{anchor:"transformers.models.auto.auto_factory._BaseAutoModelClass.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.models.auto.auto_factory._BaseAutoModelClass.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.models.auto.auto_factory._BaseAutoModelClass.from_pretrained.resume_download",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.models.auto.auto_factory._BaseAutoModelClass.from_pretrained.proxies",description:`<strong>proxies</strong> (<code>Dict[str, str]</code>, <em>optional</em>) &#x2014;
A dictionary of proxy servers to use by protocol or endpoint, e.g., <code>{&apos;http&apos;: &apos;foo.bar:3128&apos;, &apos;http://hostname&apos;: &apos;foo.bar:4012&apos;}</code>. The proxies are used on each request.`,name:"proxies"},{anchor:"transformers.models.auto.auto_factory._BaseAutoModelClass.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.models.auto.auto_factory._BaseAutoModelClass.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.models.auto.auto_factory._BaseAutoModelClass.from_pretrained.revision(str,",description:`<strong>revision(<code>str</code>,</strong> <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a
git-based system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any
identifier allowed by git.`,name:"revision(str,"},{anchor:"transformers.models.auto.auto_factory._BaseAutoModelClass.from_pretrained.trust_remote_code",description:`<strong>trust_remote_code</strong> (<code>bool</code>, <em>optional</em>, defaults 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.models.auto.auto_factory._BaseAutoModelClass.from_pretrained.kwargs",description:`<strong>kwargs</strong> (additional keyword arguments, <em>optional</em>) &#x2014;
Can be used to update the configuration object (after it being loaded) and initiate the model (e.g.,
<code>output_attentions=True</code>). Behaves differently depending on whether a <code>config</code> is provided or
automatically loaded:</p>
<ul>
<li>If a configuration is provided with <code>config</code>, <code>**kwargs</code> will be directly passed to the
underlying model&#x2019;s <code>__init__</code> method (we assume all relevant updates to the configuration have
already been done)</li>
<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_15839/en/main_classes/configuration#transformers.PretrainedConfig.from_pretrained">from_pretrained()</a>). Each key of <code>kwargs</code> that
corresponds to a configuration attribute will be used to override said attribute with the
supplied <code>kwargs</code> value. 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"}]}}),Hw=new w({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>)`}}),Uw=new X({}),Jw=new M({props:{name:"class transformers.TFAutoModelForImageClassification",anchor:"transformers.TFAutoModelForImageClassification",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/pr_15839/src/transformers/models/auto/modeling_tf_auto.py#L403"}}),Kw=new M({props:{name:"from_config",anchor:"transformers.models.auto.auto_factory._BaseAutoModelClass.from_config",parameters:[{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/pr_15839/src/transformers/models/auto/auto_factory.py#L390",parametersDescription:[{anchor:"transformers.models.auto.auto_factory._BaseAutoModelClass.from_config.config",description:`<strong>config</strong> (<a href="/docs/transformers/pr_15839/en/main_classes/configuration#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_15839/en/model_doc/convnext#transformers.ConvNextConfig">ConvNextConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/convnext#transformers.TFConvNextForImageClassification">TFConvNextForImageClassification</a> (ConvNext model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/vit#transformers.ViTConfig">ViTConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/vit#transformers.TFViTForImageClassification">TFViTForImageClassification</a> (ViT model)</li>
</ul>`,name:"config"}]}}),Zw=new w({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)`}}),e6=new M({props:{name:"from_pretrained",anchor:"transformers.models.auto.auto_factory._BaseAutoModelClass.from_pretrained",parameters:[{name:"*model_args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/pr_15839/src/transformers/models/auto/auto_factory.py#L418",parametersDescription:[{anchor:"transformers.models.auto.auto_factory._BaseAutoModelClass.from_pretrained.pretrained_model_name_or_path",description:`<strong>pretrained_model_name_or_path</strong> (<code>str</code> or <code>os.PathLike</code>) &#x2014;
Can be either:</p>
<ul>
<li>A string, the <em>model id</em> of a pretrained model hosted inside a model repo on huggingface.co.
Valid model ids can be located at the root-level, like <code>bert-base-uncased</code>, or namespaced under a
user or organization name, like <code>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_15839/en/main_classes/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.models.auto.auto_factory._BaseAutoModelClass.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.models.auto.auto_factory._BaseAutoModelClass.from_pretrained.config",description:`<strong>config</strong> (<a href="/docs/transformers/pr_15839/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>, <em>optional</em>) &#x2014;
Configuration for the model to use instead of an automatically loaded configuration. Configuration can
be automatically loaded when:</p>
<ul>
<li>The model is a model provided by the 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_15839/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and is reloaded by supplying the
save directory.</li>
<li>The model is loaded by supplying a local directory 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.models.auto.auto_factory._BaseAutoModelClass.from_pretrained.cache_dir",description:`<strong>cache_dir</strong> (<code>str</code> or <code>os.PathLike</code>, <em>optional</em>) &#x2014;
Path to a directory in which a downloaded pretrained model configuration should be cached if the
standard cache should not be used.`,name:"cache_dir"},{anchor:"transformers.models.auto.auto_factory._BaseAutoModelClass.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.models.auto.auto_factory._BaseAutoModelClass.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.models.auto.auto_factory._BaseAutoModelClass.from_pretrained.resume_download",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.models.auto.auto_factory._BaseAutoModelClass.from_pretrained.proxies",description:`<strong>proxies</strong> (<code>Dict[str, str]</code>, <em>optional</em>) &#x2014;
A dictionary of proxy servers to use by protocol or endpoint, e.g., <code>{&apos;http&apos;: &apos;foo.bar:3128&apos;, &apos;http://hostname&apos;: &apos;foo.bar:4012&apos;}</code>. The proxies are used on each request.`,name:"proxies"},{anchor:"transformers.models.auto.auto_factory._BaseAutoModelClass.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.models.auto.auto_factory._BaseAutoModelClass.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.models.auto.auto_factory._BaseAutoModelClass.from_pretrained.revision(str,",description:`<strong>revision(<code>str</code>,</strong> <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a
git-based system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any
identifier allowed by git.`,name:"revision(str,"},{anchor:"transformers.models.auto.auto_factory._BaseAutoModelClass.from_pretrained.trust_remote_code",description:`<strong>trust_remote_code</strong> (<code>bool</code>, <em>optional</em>, defaults 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.models.auto.auto_factory._BaseAutoModelClass.from_pretrained.kwargs",description:`<strong>kwargs</strong> (additional keyword arguments, <em>optional</em>) &#x2014;
Can be used to update the configuration object (after it being loaded) and initiate the model (e.g.,
<code>output_attentions=True</code>). Behaves differently depending on whether a <code>config</code> is provided or
automatically loaded:</p>
<ul>
<li>If a configuration is provided with <code>config</code>, <code>**kwargs</code> will be directly passed to the
underlying model&#x2019;s <code>__init__</code> method (we assume all relevant updates to the configuration have
already been done)</li>
<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_15839/en/main_classes/configuration#transformers.PretrainedConfig.from_pretrained">from_pretrained()</a>). Each key of <code>kwargs</code> that
corresponds to a configuration attribute will be used to override said attribute with the
supplied <code>kwargs</code> value. 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"}]}}),r6=new w({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>)`}}),t6=new X({}),a6=new M({props:{name:"class transformers.TFAutoModelForMaskedLM",anchor:"transformers.TFAutoModelForMaskedLM",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/pr_15839/src/transformers/models/auto/modeling_tf_auto.py#L417"}}),s6=new M({props:{name:"from_config",anchor:"transformers.models.auto.auto_factory._BaseAutoModelClass.from_config",parameters:[{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/pr_15839/src/transformers/models/auto/auto_factory.py#L390",parametersDescription:[{anchor:"transformers.models.auto.auto_factory._BaseAutoModelClass.from_config.config",description:`<strong>config</strong> (<a href="/docs/transformers/pr_15839/en/main_classes/configuration#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_15839/en/model_doc/albert#transformers.AlbertConfig">AlbertConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/albert#transformers.TFAlbertForMaskedLM">TFAlbertForMaskedLM</a> (ALBERT model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/bert#transformers.BertConfig">BertConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/bert#transformers.TFBertForMaskedLM">TFBertForMaskedLM</a> (BERT model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/camembert#transformers.CamembertConfig">CamembertConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/camembert#transformers.TFCamembertForMaskedLM">TFCamembertForMaskedLM</a> (CamemBERT model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/convbert#transformers.ConvBertConfig">ConvBertConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/convbert#transformers.TFConvBertForMaskedLM">TFConvBertForMaskedLM</a> (ConvBERT model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/deberta#transformers.DebertaConfig">DebertaConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/deberta#transformers.TFDebertaForMaskedLM">TFDebertaForMaskedLM</a> (DeBERTa model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/deberta-v2#transformers.DebertaV2Config">DebertaV2Config</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/deberta-v2#transformers.TFDebertaV2ForMaskedLM">TFDebertaV2ForMaskedLM</a> (DeBERTa-v2 model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/distilbert#transformers.DistilBertConfig">DistilBertConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/distilbert#transformers.TFDistilBertForMaskedLM">TFDistilBertForMaskedLM</a> (DistilBERT model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/electra#transformers.ElectraConfig">ElectraConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/electra#transformers.TFElectraForMaskedLM">TFElectraForMaskedLM</a> (ELECTRA model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/flaubert#transformers.FlaubertConfig">FlaubertConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/flaubert#transformers.TFFlaubertWithLMHeadModel">TFFlaubertWithLMHeadModel</a> (FlauBERT model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/funnel#transformers.FunnelConfig">FunnelConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/funnel#transformers.TFFunnelForMaskedLM">TFFunnelForMaskedLM</a> (Funnel Transformer model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/layoutlm#transformers.LayoutLMConfig">LayoutLMConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/layoutlm#transformers.TFLayoutLMForMaskedLM">TFLayoutLMForMaskedLM</a> (LayoutLM model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/longformer#transformers.LongformerConfig">LongformerConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/longformer#transformers.TFLongformerForMaskedLM">TFLongformerForMaskedLM</a> (Longformer model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/mpnet#transformers.MPNetConfig">MPNetConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/mpnet#transformers.TFMPNetForMaskedLM">TFMPNetForMaskedLM</a> (MPNet model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/mobilebert#transformers.MobileBertConfig">MobileBertConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/mobilebert#transformers.TFMobileBertForMaskedLM">TFMobileBertForMaskedLM</a> (MobileBERT model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/rembert#transformers.RemBertConfig">RemBertConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/rembert#transformers.TFRemBertForMaskedLM">TFRemBertForMaskedLM</a> (RemBERT model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/roformer#transformers.RoFormerConfig">RoFormerConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/roformer#transformers.TFRoFormerForMaskedLM">TFRoFormerForMaskedLM</a> (RoFormer model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/roberta#transformers.RobertaConfig">RobertaConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/roberta#transformers.TFRobertaForMaskedLM">TFRobertaForMaskedLM</a> (RoBERTa model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/tapas#transformers.TapasConfig">TapasConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/tapas#transformers.TFTapasForMaskedLM">TFTapasForMaskedLM</a> (TAPAS model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/xlm#transformers.XLMConfig">XLMConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/xlm#transformers.TFXLMWithLMHeadModel">TFXLMWithLMHeadModel</a> (XLM model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/xlm-roberta#transformers.XLMRobertaConfig">XLMRobertaConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/xlm-roberta#transformers.TFXLMRobertaForMaskedLM">TFXLMRobertaForMaskedLM</a> (XLM-RoBERTa model)</li>
</ul>`,name:"config"}]}}),l6=new w({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)`}}),i6=new M({props:{name:"from_pretrained",anchor:"transformers.models.auto.auto_factory._BaseAutoModelClass.from_pretrained",parameters:[{name:"*model_args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/pr_15839/src/transformers/models/auto/auto_factory.py#L418",parametersDescription:[{anchor:"transformers.models.auto.auto_factory._BaseAutoModelClass.from_pretrained.pretrained_model_name_or_path",description:`<strong>pretrained_model_name_or_path</strong> (<code>str</code> or <code>os.PathLike</code>) &#x2014;
Can be either:</p>
<ul>
<li>A string, the <em>model id</em> of a pretrained model hosted inside a model repo on huggingface.co.
Valid model ids can be located at the root-level, like <code>bert-base-uncased</code>, or namespaced under a
user or organization name, like <code>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_15839/en/main_classes/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.models.auto.auto_factory._BaseAutoModelClass.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.models.auto.auto_factory._BaseAutoModelClass.from_pretrained.config",description:`<strong>config</strong> (<a href="/docs/transformers/pr_15839/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>, <em>optional</em>) &#x2014;
Configuration for the model to use instead of an automatically loaded configuration. Configuration can
be automatically loaded when:</p>
<ul>
<li>The model is a model provided by the 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_15839/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and is reloaded by supplying the
save directory.</li>
<li>The model is loaded by supplying a local directory 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.models.auto.auto_factory._BaseAutoModelClass.from_pretrained.cache_dir",description:`<strong>cache_dir</strong> (<code>str</code> or <code>os.PathLike</code>, <em>optional</em>) &#x2014;
Path to a directory in which a downloaded pretrained model configuration should be cached if the
standard cache should not be used.`,name:"cache_dir"},{anchor:"transformers.models.auto.auto_factory._BaseAutoModelClass.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.models.auto.auto_factory._BaseAutoModelClass.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.models.auto.auto_factory._BaseAutoModelClass.from_pretrained.resume_download",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.models.auto.auto_factory._BaseAutoModelClass.from_pretrained.proxies",description:`<strong>proxies</strong> (<code>Dict[str, str]</code>, <em>optional</em>) &#x2014;
A dictionary of proxy servers to use by protocol or endpoint, e.g., <code>{&apos;http&apos;: &apos;foo.bar:3128&apos;, &apos;http://hostname&apos;: &apos;foo.bar:4012&apos;}</code>. The proxies are used on each request.`,name:"proxies"},{anchor:"transformers.models.auto.auto_factory._BaseAutoModelClass.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.models.auto.auto_factory._BaseAutoModelClass.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.models.auto.auto_factory._BaseAutoModelClass.from_pretrained.revision(str,",description:`<strong>revision(<code>str</code>,</strong> <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a
git-based system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any
identifier allowed by git.`,name:"revision(str,"},{anchor:"transformers.models.auto.auto_factory._BaseAutoModelClass.from_pretrained.trust_remote_code",description:`<strong>trust_remote_code</strong> (<code>bool</code>, <em>optional</em>, defaults 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.models.auto.auto_factory._BaseAutoModelClass.from_pretrained.kwargs",description:`<strong>kwargs</strong> (additional keyword arguments, <em>optional</em>) &#x2014;
Can be used to update the configuration object (after it being loaded) and initiate the model (e.g.,
<code>output_attentions=True</code>). Behaves differently depending on whether a <code>config</code> is provided or
automatically loaded:</p>
<ul>
<li>If a configuration is provided with <code>config</code>, <code>**kwargs</code> will be directly passed to the
underlying model&#x2019;s <code>__init__</code> method (we assume all relevant updates to the configuration have
already been done)</li>
<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_15839/en/main_classes/configuration#transformers.PretrainedConfig.from_pretrained">from_pretrained()</a>). Each key of <code>kwargs</code> that
corresponds to a configuration attribute will be used to override said attribute with the
supplied <code>kwargs</code> value. 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"}]}}),d6=new w({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>)`}}),c6=new X({}),f6=new M({props:{name:"class transformers.TFAutoModelForSeq2SeqLM",anchor:"transformers.TFAutoModelForSeq2SeqLM",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/pr_15839/src/transformers/models/auto/modeling_tf_auto.py#L424"}}),g6=new M({props:{name:"from_config",anchor:"transformers.models.auto.auto_factory._BaseAutoModelClass.from_config",parameters:[{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/pr_15839/src/transformers/models/auto/auto_factory.py#L390",parametersDescription:[{anchor:"transformers.models.auto.auto_factory._BaseAutoModelClass.from_config.config",description:`<strong>config</strong> (<a href="/docs/transformers/pr_15839/en/main_classes/configuration#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_15839/en/model_doc/bart#transformers.BartConfig">BartConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/bart#transformers.TFBartForConditionalGeneration">TFBartForConditionalGeneration</a> (BART model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/blenderbot#transformers.BlenderbotConfig">BlenderbotConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/blenderbot#transformers.TFBlenderbotForConditionalGeneration">TFBlenderbotForConditionalGeneration</a> (Blenderbot model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/blenderbot-small#transformers.BlenderbotSmallConfig">BlenderbotSmallConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/blenderbot-small#transformers.TFBlenderbotSmallForConditionalGeneration">TFBlenderbotSmallForConditionalGeneration</a> (BlenderbotSmall model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/encoder-decoder#transformers.EncoderDecoderConfig">EncoderDecoderConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/encoder-decoder#transformers.TFEncoderDecoderModel">TFEncoderDecoderModel</a> (Encoder decoder model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/led#transformers.LEDConfig">LEDConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/led#transformers.TFLEDForConditionalGeneration">TFLEDForConditionalGeneration</a> (LED model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/mbart#transformers.MBartConfig">MBartConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/mbart#transformers.TFMBartForConditionalGeneration">TFMBartForConditionalGeneration</a> (mBART model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/mt5#transformers.MT5Config">MT5Config</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/mt5#transformers.TFMT5ForConditionalGeneration">TFMT5ForConditionalGeneration</a> (mT5 model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/marian#transformers.MarianConfig">MarianConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/marian#transformers.TFMarianMTModel">TFMarianMTModel</a> (Marian model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/pegasus#transformers.PegasusConfig">PegasusConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/pegasus#transformers.TFPegasusForConditionalGeneration">TFPegasusForConditionalGeneration</a> (Pegasus model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/t5#transformers.T5Config">T5Config</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/t5#transformers.TFT5ForConditionalGeneration">TFT5ForConditionalGeneration</a> (T5 model)</li>
</ul>`,name:"config"}]}}),h6=new w({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)`}}),p6=new M({props:{name:"from_pretrained",anchor:"transformers.models.auto.auto_factory._BaseAutoModelClass.from_pretrained",parameters:[{name:"*model_args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/pr_15839/src/transformers/models/auto/auto_factory.py#L418",parametersDescription:[{anchor:"transformers.models.auto.auto_factory._BaseAutoModelClass.from_pretrained.pretrained_model_name_or_path",description:`<strong>pretrained_model_name_or_path</strong> (<code>str</code> or <code>os.PathLike</code>) &#x2014;
Can be either:</p>
<ul>
<li>A string, the <em>model id</em> of a pretrained model hosted inside a model repo on huggingface.co.
Valid model ids can be located at the root-level, like <code>bert-base-uncased</code>, or namespaced under a
user or organization name, like <code>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_15839/en/main_classes/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.models.auto.auto_factory._BaseAutoModelClass.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.models.auto.auto_factory._BaseAutoModelClass.from_pretrained.config",description:`<strong>config</strong> (<a href="/docs/transformers/pr_15839/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>, <em>optional</em>) &#x2014;
Configuration for the model to use instead of an automatically loaded configuration. Configuration can
be automatically loaded when:</p>
<ul>
<li>The model is a model provided by the 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_15839/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and is reloaded by supplying the
save directory.</li>
<li>The model is loaded by supplying a local directory 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.models.auto.auto_factory._BaseAutoModelClass.from_pretrained.cache_dir",description:`<strong>cache_dir</strong> (<code>str</code> or <code>os.PathLike</code>, <em>optional</em>) &#x2014;
Path to a directory in which a downloaded pretrained model configuration should be cached if the
standard cache should not be used.`,name:"cache_dir"},{anchor:"transformers.models.auto.auto_factory._BaseAutoModelClass.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.models.auto.auto_factory._BaseAutoModelClass.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.models.auto.auto_factory._BaseAutoModelClass.from_pretrained.resume_download",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.models.auto.auto_factory._BaseAutoModelClass.from_pretrained.proxies",description:`<strong>proxies</strong> (<code>Dict[str, str]</code>, <em>optional</em>) &#x2014;
A dictionary of proxy servers to use by protocol or endpoint, e.g., <code>{&apos;http&apos;: &apos;foo.bar:3128&apos;, &apos;http://hostname&apos;: &apos;foo.bar:4012&apos;}</code>. The proxies are used on each request.`,name:"proxies"},{anchor:"transformers.models.auto.auto_factory._BaseAutoModelClass.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.models.auto.auto_factory._BaseAutoModelClass.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.models.auto.auto_factory._BaseAutoModelClass.from_pretrained.revision(str,",description:`<strong>revision(<code>str</code>,</strong> <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a
git-based system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any
identifier allowed by git.`,name:"revision(str,"},{anchor:"transformers.models.auto.auto_factory._BaseAutoModelClass.from_pretrained.trust_remote_code",description:`<strong>trust_remote_code</strong> (<code>bool</code>, <em>optional</em>, defaults 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.models.auto.auto_factory._BaseAutoModelClass.from_pretrained.kwargs",description:`<strong>kwargs</strong> (additional keyword arguments, <em>optional</em>) &#x2014;
Can be used to update the configuration object (after it being loaded) and initiate the model (e.g.,
<code>output_attentions=True</code>). Behaves differently depending on whether a <code>config</code> is provided or
automatically loaded:</p>
<ul>
<li>If a configuration is provided with <code>config</code>, <code>**kwargs</code> will be directly passed to the
underlying model&#x2019;s <code>__init__</code> method (we assume all relevant updates to the configuration have
already been done)</li>
<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_15839/en/main_classes/configuration#transformers.PretrainedConfig.from_pretrained">from_pretrained()</a>). Each key of <code>kwargs</code> that
corresponds to a configuration attribute will be used to override said attribute with the
supplied <code>kwargs</code> value. 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"}]}}),_6=new w({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>)`}}),u6=new X({}),b6=new M({props:{name:"class transformers.TFAutoModelForSequenceClassification",anchor:"transformers.TFAutoModelForSequenceClassification",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/pr_15839/src/transformers/models/auto/modeling_tf_auto.py#L433"}}),T6=new M({props:{name:"from_config",anchor:"transformers.models.auto.auto_factory._BaseAutoModelClass.from_config",parameters:[{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/pr_15839/src/transformers/models/auto/auto_factory.py#L390",parametersDescription:[{anchor:"transformers.models.auto.auto_factory._BaseAutoModelClass.from_config.config",description:`<strong>config</strong> (<a href="/docs/transformers/pr_15839/en/main_classes/configuration#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_15839/en/model_doc/albert#transformers.AlbertConfig">AlbertConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/albert#transformers.TFAlbertForSequenceClassification">TFAlbertForSequenceClassification</a> (ALBERT model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/bert#transformers.BertConfig">BertConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/bert#transformers.TFBertForSequenceClassification">TFBertForSequenceClassification</a> (BERT model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/ctrl#transformers.CTRLConfig">CTRLConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/ctrl#transformers.TFCTRLForSequenceClassification">TFCTRLForSequenceClassification</a> (CTRL model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/camembert#transformers.CamembertConfig">CamembertConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/camembert#transformers.TFCamembertForSequenceClassification">TFCamembertForSequenceClassification</a> (CamemBERT model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/convbert#transformers.ConvBertConfig">ConvBertConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/convbert#transformers.TFConvBertForSequenceClassification">TFConvBertForSequenceClassification</a> (ConvBERT model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/deberta#transformers.DebertaConfig">DebertaConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/deberta#transformers.TFDebertaForSequenceClassification">TFDebertaForSequenceClassification</a> (DeBERTa model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/deberta-v2#transformers.DebertaV2Config">DebertaV2Config</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/deberta-v2#transformers.TFDebertaV2ForSequenceClassification">TFDebertaV2ForSequenceClassification</a> (DeBERTa-v2 model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/distilbert#transformers.DistilBertConfig">DistilBertConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/distilbert#transformers.TFDistilBertForSequenceClassification">TFDistilBertForSequenceClassification</a> (DistilBERT model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/electra#transformers.ElectraConfig">ElectraConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/electra#transformers.TFElectraForSequenceClassification">TFElectraForSequenceClassification</a> (ELECTRA model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/flaubert#transformers.FlaubertConfig">FlaubertConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/flaubert#transformers.TFFlaubertForSequenceClassification">TFFlaubertForSequenceClassification</a> (FlauBERT model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/funnel#transformers.FunnelConfig">FunnelConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/funnel#transformers.TFFunnelForSequenceClassification">TFFunnelForSequenceClassification</a> (Funnel Transformer model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/gpt2#transformers.GPT2Config">GPT2Config</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/gpt2#transformers.TFGPT2ForSequenceClassification">TFGPT2ForSequenceClassification</a> (OpenAI GPT-2 model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/layoutlm#transformers.LayoutLMConfig">LayoutLMConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/layoutlm#transformers.TFLayoutLMForSequenceClassification">TFLayoutLMForSequenceClassification</a> (LayoutLM model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/longformer#transformers.LongformerConfig">LongformerConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/longformer#transformers.TFLongformerForSequenceClassification">TFLongformerForSequenceClassification</a> (Longformer model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/mpnet#transformers.MPNetConfig">MPNetConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/mpnet#transformers.TFMPNetForSequenceClassification">TFMPNetForSequenceClassification</a> (MPNet model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/mobilebert#transformers.MobileBertConfig">MobileBertConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/mobilebert#transformers.TFMobileBertForSequenceClassification">TFMobileBertForSequenceClassification</a> (MobileBERT model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/openai-gpt#transformers.OpenAIGPTConfig">OpenAIGPTConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/openai-gpt#transformers.TFOpenAIGPTForSequenceClassification">TFOpenAIGPTForSequenceClassification</a> (OpenAI GPT model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/rembert#transformers.RemBertConfig">RemBertConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/rembert#transformers.TFRemBertForSequenceClassification">TFRemBertForSequenceClassification</a> (RemBERT model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/roformer#transformers.RoFormerConfig">RoFormerConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/roformer#transformers.TFRoFormerForSequenceClassification">TFRoFormerForSequenceClassification</a> (RoFormer model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/roberta#transformers.RobertaConfig">RobertaConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/roberta#transformers.TFRobertaForSequenceClassification">TFRobertaForSequenceClassification</a> (RoBERTa model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/tapas#transformers.TapasConfig">TapasConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/tapas#transformers.TFTapasForSequenceClassification">TFTapasForSequenceClassification</a> (TAPAS model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/transfo-xl#transformers.TransfoXLConfig">TransfoXLConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/transfo-xl#transformers.TFTransfoXLForSequenceClassification">TFTransfoXLForSequenceClassification</a> (Transformer-XL model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/xlm#transformers.XLMConfig">XLMConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/xlm#transformers.TFXLMForSequenceClassification">TFXLMForSequenceClassification</a> (XLM model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/xlm-roberta#transformers.XLMRobertaConfig">XLMRobertaConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/xlm-roberta#transformers.TFXLMRobertaForSequenceClassification">TFXLMRobertaForSequenceClassification</a> (XLM-RoBERTa model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/xlnet#transformers.XLNetConfig">XLNetConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/xlnet#transformers.TFXLNetForSequenceClassification">TFXLNetForSequenceClassification</a> (XLNet model)</li>
</ul>`,name:"config"}]}}),F6=new w({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)`}}),C6=new M({props:{name:"from_pretrained",anchor:"transformers.models.auto.auto_factory._BaseAutoModelClass.from_pretrained",parameters:[{name:"*model_args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/pr_15839/src/transformers/models/auto/auto_factory.py#L418",parametersDescription:[{anchor:"transformers.models.auto.auto_factory._BaseAutoModelClass.from_pretrained.pretrained_model_name_or_path",description:`<strong>pretrained_model_name_or_path</strong> (<code>str</code> or <code>os.PathLike</code>) &#x2014;
Can be either:</p>
<ul>
<li>A string, the <em>model id</em> of a pretrained model hosted inside a model repo on huggingface.co.
Valid model ids can be located at the root-level, like <code>bert-base-uncased</code>, or namespaced under a
user or organization name, like <code>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_15839/en/main_classes/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.models.auto.auto_factory._BaseAutoModelClass.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.models.auto.auto_factory._BaseAutoModelClass.from_pretrained.config",description:`<strong>config</strong> (<a href="/docs/transformers/pr_15839/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>, <em>optional</em>) &#x2014;
Configuration for the model to use instead of an automatically loaded configuration. Configuration can
be automatically loaded when:</p>
<ul>
<li>The model is a model provided by the 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_15839/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and is reloaded by supplying the
save directory.</li>
<li>The model is loaded by supplying a local directory 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.models.auto.auto_factory._BaseAutoModelClass.from_pretrained.cache_dir",description:`<strong>cache_dir</strong> (<code>str</code> or <code>os.PathLike</code>, <em>optional</em>) &#x2014;
Path to a directory in which a downloaded pretrained model configuration should be cached if the
standard cache should not be used.`,name:"cache_dir"},{anchor:"transformers.models.auto.auto_factory._BaseAutoModelClass.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.models.auto.auto_factory._BaseAutoModelClass.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.models.auto.auto_factory._BaseAutoModelClass.from_pretrained.resume_download",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.models.auto.auto_factory._BaseAutoModelClass.from_pretrained.proxies",description:`<strong>proxies</strong> (<code>Dict[str, str]</code>, <em>optional</em>) &#x2014;
A dictionary of proxy servers to use by protocol or endpoint, e.g., <code>{&apos;http&apos;: &apos;foo.bar:3128&apos;, &apos;http://hostname&apos;: &apos;foo.bar:4012&apos;}</code>. The proxies are used on each request.`,name:"proxies"},{anchor:"transformers.models.auto.auto_factory._BaseAutoModelClass.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.models.auto.auto_factory._BaseAutoModelClass.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.models.auto.auto_factory._BaseAutoModelClass.from_pretrained.revision(str,",description:`<strong>revision(<code>str</code>,</strong> <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a
git-based system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any
identifier allowed by git.`,name:"revision(str,"},{anchor:"transformers.models.auto.auto_factory._BaseAutoModelClass.from_pretrained.trust_remote_code",description:`<strong>trust_remote_code</strong> (<code>bool</code>, <em>optional</em>, defaults 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.models.auto.auto_factory._BaseAutoModelClass.from_pretrained.kwargs",description:`<strong>kwargs</strong> (additional keyword arguments, <em>optional</em>) &#x2014;
Can be used to update the configuration object (after it being loaded) and initiate the model (e.g.,
<code>output_attentions=True</code>). Behaves differently depending on whether a <code>config</code> is provided or
automatically loaded:</p>
<ul>
<li>If a configuration is provided with <code>config</code>, <code>**kwargs</code> will be directly passed to the
underlying model&#x2019;s <code>__init__</code> method (we assume all relevant updates to the configuration have
already been done)</li>
<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_15839/en/main_classes/configuration#transformers.PretrainedConfig.from_pretrained">from_pretrained()</a>). Each key of <code>kwargs</code> that
corresponds to a configuration attribute will be used to override said attribute with the
supplied <code>kwargs</code> value. 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"}]}}),M6=new w({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>)`}}),E6=new X({}),y6=new M({props:{name:"class transformers.TFAutoModelForMultipleChoice",anchor:"transformers.TFAutoModelForMultipleChoice",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/pr_15839/src/transformers/models/auto/modeling_tf_auto.py#L469"}}),A6=new M({props:{name:"from_config",anchor:"transformers.models.auto.auto_factory._BaseAutoModelClass.from_config",parameters:[{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/pr_15839/src/transformers/models/auto/auto_factory.py#L390",parametersDescription:[{anchor:"transformers.models.auto.auto_factory._BaseAutoModelClass.from_config.config",description:`<strong>config</strong> (<a href="/docs/transformers/pr_15839/en/main_classes/configuration#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_15839/en/model_doc/albert#transformers.AlbertConfig">AlbertConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/albert#transformers.TFAlbertForMultipleChoice">TFAlbertForMultipleChoice</a> (ALBERT model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/bert#transformers.BertConfig">BertConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/bert#transformers.TFBertForMultipleChoice">TFBertForMultipleChoice</a> (BERT model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/camembert#transformers.CamembertConfig">CamembertConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/camembert#transformers.TFCamembertForMultipleChoice">TFCamembertForMultipleChoice</a> (CamemBERT model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/convbert#transformers.ConvBertConfig">ConvBertConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/convbert#transformers.TFConvBertForMultipleChoice">TFConvBertForMultipleChoice</a> (ConvBERT model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/distilbert#transformers.DistilBertConfig">DistilBertConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/distilbert#transformers.TFDistilBertForMultipleChoice">TFDistilBertForMultipleChoice</a> (DistilBERT model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/electra#transformers.ElectraConfig">ElectraConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/electra#transformers.TFElectraForMultipleChoice">TFElectraForMultipleChoice</a> (ELECTRA model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/flaubert#transformers.FlaubertConfig">FlaubertConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/flaubert#transformers.TFFlaubertForMultipleChoice">TFFlaubertForMultipleChoice</a> (FlauBERT model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/funnel#transformers.FunnelConfig">FunnelConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/funnel#transformers.TFFunnelForMultipleChoice">TFFunnelForMultipleChoice</a> (Funnel Transformer model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/longformer#transformers.LongformerConfig">LongformerConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/longformer#transformers.TFLongformerForMultipleChoice">TFLongformerForMultipleChoice</a> (Longformer model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/mpnet#transformers.MPNetConfig">MPNetConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/mpnet#transformers.TFMPNetForMultipleChoice">TFMPNetForMultipleChoice</a> (MPNet model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/mobilebert#transformers.MobileBertConfig">MobileBertConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/mobilebert#transformers.TFMobileBertForMultipleChoice">TFMobileBertForMultipleChoice</a> (MobileBERT model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/rembert#transformers.RemBertConfig">RemBertConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/rembert#transformers.TFRemBertForMultipleChoice">TFRemBertForMultipleChoice</a> (RemBERT model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/roformer#transformers.RoFormerConfig">RoFormerConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/roformer#transformers.TFRoFormerForMultipleChoice">TFRoFormerForMultipleChoice</a> (RoFormer model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/roberta#transformers.RobertaConfig">RobertaConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/roberta#transformers.TFRobertaForMultipleChoice">TFRobertaForMultipleChoice</a> (RoBERTa model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/xlm#transformers.XLMConfig">XLMConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/xlm#transformers.TFXLMForMultipleChoice">TFXLMForMultipleChoice</a> (XLM model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/xlm-roberta#transformers.XLMRobertaConfig">XLMRobertaConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/xlm-roberta#transformers.TFXLMRobertaForMultipleChoice">TFXLMRobertaForMultipleChoice</a> (XLM-RoBERTa model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/xlnet#transformers.XLNetConfig">XLNetConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/xlnet#transformers.TFXLNetForMultipleChoice">TFXLNetForMultipleChoice</a> (XLNet model)</li>
</ul>`,name:"config"}]}}),L6=new w({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)`}}),B6=new M({props:{name:"from_pretrained",anchor:"transformers.models.auto.auto_factory._BaseAutoModelClass.from_pretrained",parameters:[{name:"*model_args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/pr_15839/src/transformers/models/auto/auto_factory.py#L418",parametersDescription:[{anchor:"transformers.models.auto.auto_factory._BaseAutoModelClass.from_pretrained.pretrained_model_name_or_path",description:`<strong>pretrained_model_name_or_path</strong> (<code>str</code> or <code>os.PathLike</code>) &#x2014;
Can be either:</p>
<ul>
<li>A string, the <em>model id</em> of a pretrained model hosted inside a model repo on huggingface.co.
Valid model ids can be located at the root-level, like <code>bert-base-uncased</code>, or namespaced under a
user or organization name, like <code>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_15839/en/main_classes/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.models.auto.auto_factory._BaseAutoModelClass.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.models.auto.auto_factory._BaseAutoModelClass.from_pretrained.config",description:`<strong>config</strong> (<a href="/docs/transformers/pr_15839/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>, <em>optional</em>) &#x2014;
Configuration for the model to use instead of an automatically loaded configuration. Configuration can
be automatically loaded when:</p>
<ul>
<li>The model is a model provided by the 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_15839/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and is reloaded by supplying the
save directory.</li>
<li>The model is loaded by supplying a local directory 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.models.auto.auto_factory._BaseAutoModelClass.from_pretrained.cache_dir",description:`<strong>cache_dir</strong> (<code>str</code> or <code>os.PathLike</code>, <em>optional</em>) &#x2014;
Path to a directory in which a downloaded pretrained model configuration should be cached if the
standard cache should not be used.`,name:"cache_dir"},{anchor:"transformers.models.auto.auto_factory._BaseAutoModelClass.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.models.auto.auto_factory._BaseAutoModelClass.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.models.auto.auto_factory._BaseAutoModelClass.from_pretrained.resume_download",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.models.auto.auto_factory._BaseAutoModelClass.from_pretrained.proxies",description:`<strong>proxies</strong> (<code>Dict[str, str]</code>, <em>optional</em>) &#x2014;
A dictionary of proxy servers to use by protocol or endpoint, e.g., <code>{&apos;http&apos;: &apos;foo.bar:3128&apos;, &apos;http://hostname&apos;: &apos;foo.bar:4012&apos;}</code>. The proxies are used on each request.`,name:"proxies"},{anchor:"transformers.models.auto.auto_factory._BaseAutoModelClass.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.models.auto.auto_factory._BaseAutoModelClass.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.models.auto.auto_factory._BaseAutoModelClass.from_pretrained.revision(str,",description:`<strong>revision(<code>str</code>,</strong> <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a
git-based system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any
identifier allowed by git.`,name:"revision(str,"},{anchor:"transformers.models.auto.auto_factory._BaseAutoModelClass.from_pretrained.trust_remote_code",description:`<strong>trust_remote_code</strong> (<code>bool</code>, <em>optional</em>, defaults 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.models.auto.auto_factory._BaseAutoModelClass.from_pretrained.kwargs",description:`<strong>kwargs</strong> (additional keyword arguments, <em>optional</em>) &#x2014;
Can be used to update the configuration object (after it being loaded) and initiate the model (e.g.,
<code>output_attentions=True</code>). Behaves differently depending on whether a <code>config</code> is provided or
automatically loaded:</p>
<ul>
<li>If a configuration is provided with <code>config</code>, <code>**kwargs</code> will be directly passed to the
underlying model&#x2019;s <code>__init__</code> method (we assume all relevant updates to the configuration have
already been done)</li>
<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_15839/en/main_classes/configuration#transformers.PretrainedConfig.from_pretrained">from_pretrained()</a>). Each key of <code>kwargs</code> that
corresponds to a configuration attribute will be used to override said attribute with the
supplied <code>kwargs</code> value. 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"}]}}),x6=new w({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>)`}}),k6=new X({}),R6=new M({props:{name:"class transformers.TFAutoModelForTableQuestionAnswering",anchor:"transformers.TFAutoModelForTableQuestionAnswering",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/pr_15839/src/transformers/models/auto/modeling_tf_auto.py#L449"}}),P6=new M({props:{name:"from_config",anchor:"transformers.models.auto.auto_factory._BaseAutoModelClass.from_config",parameters:[{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/pr_15839/src/transformers/models/auto/auto_factory.py#L390",parametersDescription:[{anchor:"transformers.models.auto.auto_factory._BaseAutoModelClass.from_config.config",description:`<strong>config</strong> (<a href="/docs/transformers/pr_15839/en/main_classes/configuration#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_15839/en/model_doc/tapas#transformers.TapasConfig">TapasConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/tapas#transformers.TFTapasForQuestionAnswering">TFTapasForQuestionAnswering</a> (TAPAS model)</li>
</ul>`,name:"config"}]}}),$6=new w({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)`}}),I6=new M({props:{name:"from_pretrained",anchor:"transformers.models.auto.auto_factory._BaseAutoModelClass.from_pretrained",parameters:[{name:"*model_args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/pr_15839/src/transformers/models/auto/auto_factory.py#L418",parametersDescription:[{anchor:"transformers.models.auto.auto_factory._BaseAutoModelClass.from_pretrained.pretrained_model_name_or_path",description:`<strong>pretrained_model_name_or_path</strong> (<code>str</code> or <code>os.PathLike</code>) &#x2014;
Can be either:</p>
<ul>
<li>A string, the <em>model id</em> of a pretrained model hosted inside a model repo on huggingface.co.
Valid model ids can be located at the root-level, like <code>bert-base-uncased</code>, or namespaced under a
user or organization name, like <code>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_15839/en/main_classes/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.models.auto.auto_factory._BaseAutoModelClass.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.models.auto.auto_factory._BaseAutoModelClass.from_pretrained.config",description:`<strong>config</strong> (<a href="/docs/transformers/pr_15839/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>, <em>optional</em>) &#x2014;
Configuration for the model to use instead of an automatically loaded configuration. Configuration can
be automatically loaded when:</p>
<ul>
<li>The model is a model provided by the 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_15839/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and is reloaded by supplying the
save directory.</li>
<li>The model is loaded by supplying a local directory 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.models.auto.auto_factory._BaseAutoModelClass.from_pretrained.cache_dir",description:`<strong>cache_dir</strong> (<code>str</code> or <code>os.PathLike</code>, <em>optional</em>) &#x2014;
Path to a directory in which a downloaded pretrained model configuration should be cached if the
standard cache should not be used.`,name:"cache_dir"},{anchor:"transformers.models.auto.auto_factory._BaseAutoModelClass.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.models.auto.auto_factory._BaseAutoModelClass.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.models.auto.auto_factory._BaseAutoModelClass.from_pretrained.resume_download",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.models.auto.auto_factory._BaseAutoModelClass.from_pretrained.proxies",description:`<strong>proxies</strong> (<code>Dict[str, str]</code>, <em>optional</em>) &#x2014;
A dictionary of proxy servers to use by protocol or endpoint, e.g., <code>{&apos;http&apos;: &apos;foo.bar:3128&apos;, &apos;http://hostname&apos;: &apos;foo.bar:4012&apos;}</code>. The proxies are used on each request.`,name:"proxies"},{anchor:"transformers.models.auto.auto_factory._BaseAutoModelClass.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.models.auto.auto_factory._BaseAutoModelClass.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.models.auto.auto_factory._BaseAutoModelClass.from_pretrained.revision(str,",description:`<strong>revision(<code>str</code>,</strong> <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a
git-based system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any
identifier allowed by git.`,name:"revision(str,"},{anchor:"transformers.models.auto.auto_factory._BaseAutoModelClass.from_pretrained.trust_remote_code",description:`<strong>trust_remote_code</strong> (<code>bool</code>, <em>optional</em>, defaults 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.models.auto.auto_factory._BaseAutoModelClass.from_pretrained.kwargs",description:`<strong>kwargs</strong> (additional keyword arguments, <em>optional</em>) &#x2014;
Can be used to update the configuration object (after it being loaded) and initiate the model (e.g.,
<code>output_attentions=True</code>). Behaves differently depending on whether a <code>config</code> is provided or
automatically loaded:</p>
<ul>
<li>If a configuration is provided with <code>config</code>, <code>**kwargs</code> will be directly passed to the
underlying model&#x2019;s <code>__init__</code> method (we assume all relevant updates to the configuration have
already been done)</li>
<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_15839/en/main_classes/configuration#transformers.PretrainedConfig.from_pretrained">from_pretrained()</a>). Each key of <code>kwargs</code> that
corresponds to a configuration attribute will be used to override said attribute with the
supplied <code>kwargs</code> value. 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"}]}}),j6=new w({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>)`}}),D6=new X({}),N6=new M({props:{name:"class transformers.TFAutoModelForTokenClassification",anchor:"transformers.TFAutoModelForTokenClassification",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/pr_15839/src/transformers/models/auto/modeling_tf_auto.py#L460"}}),O6=new M({props:{name:"from_config",anchor:"transformers.models.auto.auto_factory._BaseAutoModelClass.from_config",parameters:[{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/pr_15839/src/transformers/models/auto/auto_factory.py#L390",parametersDescription:[{anchor:"transformers.models.auto.auto_factory._BaseAutoModelClass.from_config.config",description:`<strong>config</strong> (<a href="/docs/transformers/pr_15839/en/main_classes/configuration#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_15839/en/model_doc/albert#transformers.AlbertConfig">AlbertConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/albert#transformers.TFAlbertForTokenClassification">TFAlbertForTokenClassification</a> (ALBERT model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/bert#transformers.BertConfig">BertConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/bert#transformers.TFBertForTokenClassification">TFBertForTokenClassification</a> (BERT model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/camembert#transformers.CamembertConfig">CamembertConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/camembert#transformers.TFCamembertForTokenClassification">TFCamembertForTokenClassification</a> (CamemBERT model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/convbert#transformers.ConvBertConfig">ConvBertConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/convbert#transformers.TFConvBertForTokenClassification">TFConvBertForTokenClassification</a> (ConvBERT model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/deberta#transformers.DebertaConfig">DebertaConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/deberta#transformers.TFDebertaForTokenClassification">TFDebertaForTokenClassification</a> (DeBERTa model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/deberta-v2#transformers.DebertaV2Config">DebertaV2Config</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/deberta-v2#transformers.TFDebertaV2ForTokenClassification">TFDebertaV2ForTokenClassification</a> (DeBERTa-v2 model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/distilbert#transformers.DistilBertConfig">DistilBertConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/distilbert#transformers.TFDistilBertForTokenClassification">TFDistilBertForTokenClassification</a> (DistilBERT model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/electra#transformers.ElectraConfig">ElectraConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/electra#transformers.TFElectraForTokenClassification">TFElectraForTokenClassification</a> (ELECTRA model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/flaubert#transformers.FlaubertConfig">FlaubertConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/flaubert#transformers.TFFlaubertForTokenClassification">TFFlaubertForTokenClassification</a> (FlauBERT model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/funnel#transformers.FunnelConfig">FunnelConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/funnel#transformers.TFFunnelForTokenClassification">TFFunnelForTokenClassification</a> (Funnel Transformer model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/layoutlm#transformers.LayoutLMConfig">LayoutLMConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/layoutlm#transformers.TFLayoutLMForTokenClassification">TFLayoutLMForTokenClassification</a> (LayoutLM model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/longformer#transformers.LongformerConfig">LongformerConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/longformer#transformers.TFLongformerForTokenClassification">TFLongformerForTokenClassification</a> (Longformer model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/mpnet#transformers.MPNetConfig">MPNetConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/mpnet#transformers.TFMPNetForTokenClassification">TFMPNetForTokenClassification</a> (MPNet model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/mobilebert#transformers.MobileBertConfig">MobileBertConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/mobilebert#transformers.TFMobileBertForTokenClassification">TFMobileBertForTokenClassification</a> (MobileBERT model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/rembert#transformers.RemBertConfig">RemBertConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/rembert#transformers.TFRemBertForTokenClassification">TFRemBertForTokenClassification</a> (RemBERT model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/roformer#transformers.RoFormerConfig">RoFormerConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/roformer#transformers.TFRoFormerForTokenClassification">TFRoFormerForTokenClassification</a> (RoFormer model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/roberta#transformers.RobertaConfig">RobertaConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/roberta#transformers.TFRobertaForTokenClassification">TFRobertaForTokenClassification</a> (RoBERTa model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/xlm#transformers.XLMConfig">XLMConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/xlm#transformers.TFXLMForTokenClassification">TFXLMForTokenClassification</a> (XLM model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/xlm-roberta#transformers.XLMRobertaConfig">XLMRobertaConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/xlm-roberta#transformers.TFXLMRobertaForTokenClassification">TFXLMRobertaForTokenClassification</a> (XLM-RoBERTa model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/xlnet#transformers.XLNetConfig">XLNetConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/xlnet#transformers.TFXLNetForTokenClassification">TFXLNetForTokenClassification</a> (XLNet model)</li>
</ul>`,name:"config"}]}}),G6=new w({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)`}}),X6=new M({props:{name:"from_pretrained",anchor:"transformers.models.auto.auto_factory._BaseAutoModelClass.from_pretrained",parameters:[{name:"*model_args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/pr_15839/src/transformers/models/auto/auto_factory.py#L418",parametersDescription:[{anchor:"transformers.models.auto.auto_factory._BaseAutoModelClass.from_pretrained.pretrained_model_name_or_path",description:`<strong>pretrained_model_name_or_path</strong> (<code>str</code> or <code>os.PathLike</code>) &#x2014;
Can be either:</p>
<ul>
<li>A string, the <em>model id</em> of a pretrained model hosted inside a model repo on huggingface.co.
Valid model ids can be located at the root-level, like <code>bert-base-uncased</code>, or namespaced under a
user or organization name, like <code>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_15839/en/main_classes/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.models.auto.auto_factory._BaseAutoModelClass.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.models.auto.auto_factory._BaseAutoModelClass.from_pretrained.config",description:`<strong>config</strong> (<a href="/docs/transformers/pr_15839/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>, <em>optional</em>) &#x2014;
Configuration for the model to use instead of an automatically loaded configuration. Configuration can
be automatically loaded when:</p>
<ul>
<li>The model is a model provided by the 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_15839/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and is reloaded by supplying the
save directory.</li>
<li>The model is loaded by supplying a local directory 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.models.auto.auto_factory._BaseAutoModelClass.from_pretrained.cache_dir",description:`<strong>cache_dir</strong> (<code>str</code> or <code>os.PathLike</code>, <em>optional</em>) &#x2014;
Path to a directory in which a downloaded pretrained model configuration should be cached if the
standard cache should not be used.`,name:"cache_dir"},{anchor:"transformers.models.auto.auto_factory._BaseAutoModelClass.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.models.auto.auto_factory._BaseAutoModelClass.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.models.auto.auto_factory._BaseAutoModelClass.from_pretrained.resume_download",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.models.auto.auto_factory._BaseAutoModelClass.from_pretrained.proxies",description:`<strong>proxies</strong> (<code>Dict[str, str]</code>, <em>optional</em>) &#x2014;
A dictionary of proxy servers to use by protocol or endpoint, e.g., <code>{&apos;http&apos;: &apos;foo.bar:3128&apos;, &apos;http://hostname&apos;: &apos;foo.bar:4012&apos;}</code>. The proxies are used on each request.`,name:"proxies"},{anchor:"transformers.models.auto.auto_factory._BaseAutoModelClass.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.models.auto.auto_factory._BaseAutoModelClass.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.models.auto.auto_factory._BaseAutoModelClass.from_pretrained.revision(str,",description:`<strong>revision(<code>str</code>,</strong> <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a
git-based system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any
identifier allowed by git.`,name:"revision(str,"},{anchor:"transformers.models.auto.auto_factory._BaseAutoModelClass.from_pretrained.trust_remote_code",description:`<strong>trust_remote_code</strong> (<code>bool</code>, <em>optional</em>, defaults 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.models.auto.auto_factory._BaseAutoModelClass.from_pretrained.kwargs",description:`<strong>kwargs</strong> (additional keyword arguments, <em>optional</em>) &#x2014;
Can be used to update the configuration object (after it being loaded) and initiate the model (e.g.,
<code>output_attentions=True</code>). Behaves differently depending on whether a <code>config</code> is provided or
automatically loaded:</p>
<ul>
<li>If a configuration is provided with <code>config</code>, <code>**kwargs</code> will be directly passed to the
underlying model&#x2019;s <code>__init__</code> method (we assume all relevant updates to the configuration have
already been done)</li>
<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_15839/en/main_classes/configuration#transformers.PretrainedConfig.from_pretrained">from_pretrained()</a>). Each key of <code>kwargs</code> that
corresponds to a configuration attribute will be used to override said attribute with the
supplied <code>kwargs</code> value. 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"}]}}),V6=new w({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>)`}}),z6=new X({}),W6=new M({props:{name:"class transformers.TFAutoModelForQuestionAnswering",anchor:"transformers.TFAutoModelForQuestionAnswering",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/pr_15839/src/transformers/models/auto/modeling_tf_auto.py#L442"}}),H6=new M({props:{name:"from_config",anchor:"transformers.models.auto.auto_factory._BaseAutoModelClass.from_config",parameters:[{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/pr_15839/src/transformers/models/auto/auto_factory.py#L390",parametersDescription:[{anchor:"transformers.models.auto.auto_factory._BaseAutoModelClass.from_config.config",description:`<strong>config</strong> (<a href="/docs/transformers/pr_15839/en/main_classes/configuration#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_15839/en/model_doc/albert#transformers.AlbertConfig">AlbertConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/albert#transformers.TFAlbertForQuestionAnswering">TFAlbertForQuestionAnswering</a> (ALBERT model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/bert#transformers.BertConfig">BertConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/bert#transformers.TFBertForQuestionAnswering">TFBertForQuestionAnswering</a> (BERT model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/camembert#transformers.CamembertConfig">CamembertConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/camembert#transformers.TFCamembertForQuestionAnswering">TFCamembertForQuestionAnswering</a> (CamemBERT model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/convbert#transformers.ConvBertConfig">ConvBertConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/convbert#transformers.TFConvBertForQuestionAnswering">TFConvBertForQuestionAnswering</a> (ConvBERT model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/deberta#transformers.DebertaConfig">DebertaConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/deberta#transformers.TFDebertaForQuestionAnswering">TFDebertaForQuestionAnswering</a> (DeBERTa model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/deberta-v2#transformers.DebertaV2Config">DebertaV2Config</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/deberta-v2#transformers.TFDebertaV2ForQuestionAnswering">TFDebertaV2ForQuestionAnswering</a> (DeBERTa-v2 model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/distilbert#transformers.DistilBertConfig">DistilBertConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/distilbert#transformers.TFDistilBertForQuestionAnswering">TFDistilBertForQuestionAnswering</a> (DistilBERT model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/electra#transformers.ElectraConfig">ElectraConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/electra#transformers.TFElectraForQuestionAnswering">TFElectraForQuestionAnswering</a> (ELECTRA model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/flaubert#transformers.FlaubertConfig">FlaubertConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/flaubert#transformers.TFFlaubertForQuestionAnsweringSimple">TFFlaubertForQuestionAnsweringSimple</a> (FlauBERT model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/funnel#transformers.FunnelConfig">FunnelConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/funnel#transformers.TFFunnelForQuestionAnswering">TFFunnelForQuestionAnswering</a> (Funnel Transformer model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/longformer#transformers.LongformerConfig">LongformerConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/longformer#transformers.TFLongformerForQuestionAnswering">TFLongformerForQuestionAnswering</a> (Longformer model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/mpnet#transformers.MPNetConfig">MPNetConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/mpnet#transformers.TFMPNetForQuestionAnswering">TFMPNetForQuestionAnswering</a> (MPNet model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/mobilebert#transformers.MobileBertConfig">MobileBertConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/mobilebert#transformers.TFMobileBertForQuestionAnswering">TFMobileBertForQuestionAnswering</a> (MobileBERT model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/rembert#transformers.RemBertConfig">RemBertConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/rembert#transformers.TFRemBertForQuestionAnswering">TFRemBertForQuestionAnswering</a> (RemBERT model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/roformer#transformers.RoFormerConfig">RoFormerConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/roformer#transformers.TFRoFormerForQuestionAnswering">TFRoFormerForQuestionAnswering</a> (RoFormer model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/roberta#transformers.RobertaConfig">RobertaConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/roberta#transformers.TFRobertaForQuestionAnswering">TFRobertaForQuestionAnswering</a> (RoBERTa model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/xlm#transformers.XLMConfig">XLMConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/xlm#transformers.TFXLMForQuestionAnsweringSimple">TFXLMForQuestionAnsweringSimple</a> (XLM model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/xlm-roberta#transformers.XLMRobertaConfig">XLMRobertaConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/xlm-roberta#transformers.TFXLMRobertaForQuestionAnswering">TFXLMRobertaForQuestionAnswering</a> (XLM-RoBERTa model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/xlnet#transformers.XLNetConfig">XLNetConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/xlnet#transformers.TFXLNetForQuestionAnsweringSimple">TFXLNetForQuestionAnsweringSimple</a> (XLNet model)</li>
</ul>`,name:"config"}]}}),U6=new w({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)`}}),J6=new M({props:{name:"from_pretrained",anchor:"transformers.models.auto.auto_factory._BaseAutoModelClass.from_pretrained",parameters:[{name:"*model_args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/pr_15839/src/transformers/models/auto/auto_factory.py#L418",parametersDescription:[{anchor:"transformers.models.auto.auto_factory._BaseAutoModelClass.from_pretrained.pretrained_model_name_or_path",description:`<strong>pretrained_model_name_or_path</strong> (<code>str</code> or <code>os.PathLike</code>) &#x2014;
Can be either:</p>
<ul>
<li>A string, the <em>model id</em> of a pretrained model hosted inside a model repo on huggingface.co.
Valid model ids can be located at the root-level, like <code>bert-base-uncased</code>, or namespaced under a
user or organization name, like <code>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_15839/en/main_classes/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.models.auto.auto_factory._BaseAutoModelClass.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.models.auto.auto_factory._BaseAutoModelClass.from_pretrained.config",description:`<strong>config</strong> (<a href="/docs/transformers/pr_15839/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>, <em>optional</em>) &#x2014;
Configuration for the model to use instead of an automatically loaded configuration. Configuration can
be automatically loaded when:</p>
<ul>
<li>The model is a model provided by the 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_15839/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and is reloaded by supplying the
save directory.</li>
<li>The model is loaded by supplying a local directory 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.models.auto.auto_factory._BaseAutoModelClass.from_pretrained.cache_dir",description:`<strong>cache_dir</strong> (<code>str</code> or <code>os.PathLike</code>, <em>optional</em>) &#x2014;
Path to a directory in which a downloaded pretrained model configuration should be cached if the
standard cache should not be used.`,name:"cache_dir"},{anchor:"transformers.models.auto.auto_factory._BaseAutoModelClass.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.models.auto.auto_factory._BaseAutoModelClass.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.models.auto.auto_factory._BaseAutoModelClass.from_pretrained.resume_download",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.models.auto.auto_factory._BaseAutoModelClass.from_pretrained.proxies",description:`<strong>proxies</strong> (<code>Dict[str, str]</code>, <em>optional</em>) &#x2014;
A dictionary of proxy servers to use by protocol or endpoint, e.g., <code>{&apos;http&apos;: &apos;foo.bar:3128&apos;, &apos;http://hostname&apos;: &apos;foo.bar:4012&apos;}</code>. The proxies are used on each request.`,name:"proxies"},{anchor:"transformers.models.auto.auto_factory._BaseAutoModelClass.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.models.auto.auto_factory._BaseAutoModelClass.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.models.auto.auto_factory._BaseAutoModelClass.from_pretrained.revision(str,",description:`<strong>revision(<code>str</code>,</strong> <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a
git-based system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any
identifier allowed by git.`,name:"revision(str,"},{anchor:"transformers.models.auto.auto_factory._BaseAutoModelClass.from_pretrained.trust_remote_code",description:`<strong>trust_remote_code</strong> (<code>bool</code>, <em>optional</em>, defaults 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.models.auto.auto_factory._BaseAutoModelClass.from_pretrained.kwargs",description:`<strong>kwargs</strong> (additional keyword arguments, <em>optional</em>) &#x2014;
Can be used to update the configuration object (after it being loaded) and initiate the model (e.g.,
<code>output_attentions=True</code>). Behaves differently depending on whether a <code>config</code> is provided or
automatically loaded:</p>
<ul>
<li>If a configuration is provided with <code>config</code>, <code>**kwargs</code> will be directly passed to the
underlying model&#x2019;s <code>__init__</code> method (we assume all relevant updates to the configuration have
already been done)</li>
<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_15839/en/main_classes/configuration#transformers.PretrainedConfig.from_pretrained">from_pretrained()</a>). Each key of <code>kwargs</code> that
corresponds to a configuration attribute will be used to override said attribute with the
supplied <code>kwargs</code> value. 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"}]}}),Y6=new w({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>)`}}),K6=new X({}),Z6=new M({props:{name:"class transformers.TFAutoModelForVision2Seq",anchor:"transformers.TFAutoModelForVision2Seq",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/pr_15839/src/transformers/models/auto/modeling_tf_auto.py#L410"}}),oA=new M({props:{name:"from_config",anchor:"transformers.models.auto.auto_factory._BaseAutoModelClass.from_config",parameters:[{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/pr_15839/src/transformers/models/auto/auto_factory.py#L390",parametersDescription:[{anchor:"transformers.models.auto.auto_factory._BaseAutoModelClass.from_config.config",description:`<strong>config</strong> (<a href="/docs/transformers/pr_15839/en/main_classes/configuration#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_15839/en/model_doc/vision-encoder-decoder#transformers.VisionEncoderDecoderConfig">VisionEncoderDecoderConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/vision-encoder-decoder#transformers.TFVisionEncoderDecoderModel">TFVisionEncoderDecoderModel</a> (Vision Encoder decoder model)</li>
</ul>`,name:"config"}]}}),rA=new w({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)`}}),tA=new M({props:{name:"from_pretrained",anchor:"transformers.models.auto.auto_factory._BaseAutoModelClass.from_pretrained",parameters:[{name:"*model_args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/pr_15839/src/transformers/models/auto/auto_factory.py#L418",parametersDescription:[{anchor:"transformers.models.auto.auto_factory._BaseAutoModelClass.from_pretrained.pretrained_model_name_or_path",description:`<strong>pretrained_model_name_or_path</strong> (<code>str</code> or <code>os.PathLike</code>) &#x2014;
Can be either:</p>
<ul>
<li>A string, the <em>model id</em> of a pretrained model hosted inside a model repo on huggingface.co.
Valid model ids can be located at the root-level, like <code>bert-base-uncased</code>, or namespaced under a
user or organization name, like <code>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_15839/en/main_classes/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.models.auto.auto_factory._BaseAutoModelClass.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.models.auto.auto_factory._BaseAutoModelClass.from_pretrained.config",description:`<strong>config</strong> (<a href="/docs/transformers/pr_15839/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>, <em>optional</em>) &#x2014;
Configuration for the model to use instead of an automatically loaded configuration. Configuration can
be automatically loaded when:</p>
<ul>
<li>The model is a model provided by the 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_15839/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and is reloaded by supplying the
save directory.</li>
<li>The model is loaded by supplying a local directory 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.models.auto.auto_factory._BaseAutoModelClass.from_pretrained.cache_dir",description:`<strong>cache_dir</strong> (<code>str</code> or <code>os.PathLike</code>, <em>optional</em>) &#x2014;
Path to a directory in which a downloaded pretrained model configuration should be cached if the
standard cache should not be used.`,name:"cache_dir"},{anchor:"transformers.models.auto.auto_factory._BaseAutoModelClass.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.models.auto.auto_factory._BaseAutoModelClass.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.models.auto.auto_factory._BaseAutoModelClass.from_pretrained.resume_download",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.models.auto.auto_factory._BaseAutoModelClass.from_pretrained.proxies",description:`<strong>proxies</strong> (<code>Dict[str, str]</code>, <em>optional</em>) &#x2014;
A dictionary of proxy servers to use by protocol or endpoint, e.g., <code>{&apos;http&apos;: &apos;foo.bar:3128&apos;, &apos;http://hostname&apos;: &apos;foo.bar:4012&apos;}</code>. The proxies are used on each request.`,name:"proxies"},{anchor:"transformers.models.auto.auto_factory._BaseAutoModelClass.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.models.auto.auto_factory._BaseAutoModelClass.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.models.auto.auto_factory._BaseAutoModelClass.from_pretrained.revision(str,",description:`<strong>revision(<code>str</code>,</strong> <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a
git-based system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any
identifier allowed by git.`,name:"revision(str,"},{anchor:"transformers.models.auto.auto_factory._BaseAutoModelClass.from_pretrained.trust_remote_code",description:`<strong>trust_remote_code</strong> (<code>bool</code>, <em>optional</em>, defaults 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.models.auto.auto_factory._BaseAutoModelClass.from_pretrained.kwargs",description:`<strong>kwargs</strong> (additional keyword arguments, <em>optional</em>) &#x2014;
Can be used to update the configuration object (after it being loaded) and initiate the model (e.g.,
<code>output_attentions=True</code>). Behaves differently depending on whether a <code>config</code> is provided or
automatically loaded:</p>
<ul>
<li>If a configuration is provided with <code>config</code>, <code>**kwargs</code> will be directly passed to the
underlying model&#x2019;s <code>__init__</code> method (we assume all relevant updates to the configuration have
already been done)</li>
<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_15839/en/main_classes/configuration#transformers.PretrainedConfig.from_pretrained">from_pretrained()</a>). Each key of <code>kwargs</code> that
corresponds to a configuration attribute will be used to override said attribute with the
supplied <code>kwargs</code> value. 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"}]}}),aA=new w({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>)`}}),nA=new X({}),sA=new M({props:{name:"class transformers.TFAutoModelForSpeechSeq2Seq",anchor:"transformers.TFAutoModelForSpeechSeq2Seq",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/pr_15839/src/transformers/models/auto/modeling_tf_auto.py#L485"}}),iA=new M({props:{name:"from_config",anchor:"transformers.models.auto.auto_factory._BaseAutoModelClass.from_config",parameters:[{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/pr_15839/src/transformers/models/auto/auto_factory.py#L390",parametersDescription:[{anchor:"transformers.models.auto.auto_factory._BaseAutoModelClass.from_config.config",description:`<strong>config</strong> (<a href="/docs/transformers/pr_15839/en/main_classes/configuration#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_15839/en/model_doc/speech_to_text#transformers.Speech2TextConfig">Speech2TextConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/speech_to_text#transformers.TFSpeech2TextForConditionalGeneration">TFSpeech2TextForConditionalGeneration</a> (Speech2Text model)</li>
</ul>`,name:"config"}]}}),dA=new w({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)`}}),cA=new M({props:{name:"from_pretrained",anchor:"transformers.models.auto.auto_factory._BaseAutoModelClass.from_pretrained",parameters:[{name:"*model_args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/pr_15839/src/transformers/models/auto/auto_factory.py#L418",parametersDescription:[{anchor:"transformers.models.auto.auto_factory._BaseAutoModelClass.from_pretrained.pretrained_model_name_or_path",description:`<strong>pretrained_model_name_or_path</strong> (<code>str</code> or <code>os.PathLike</code>) &#x2014;
Can be either:</p>
<ul>
<li>A string, the <em>model id</em> of a pretrained model hosted inside a model repo on huggingface.co.
Valid model ids can be located at the root-level, like <code>bert-base-uncased</code>, or namespaced under a
user or organization name, like <code>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_15839/en/main_classes/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.models.auto.auto_factory._BaseAutoModelClass.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.models.auto.auto_factory._BaseAutoModelClass.from_pretrained.config",description:`<strong>config</strong> (<a href="/docs/transformers/pr_15839/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>, <em>optional</em>) &#x2014;
Configuration for the model to use instead of an automatically loaded configuration. Configuration can
be automatically loaded when:</p>
<ul>
<li>The model is a model provided by the 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_15839/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and is reloaded by supplying the
save directory.</li>
<li>The model is loaded by supplying a local directory 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.models.auto.auto_factory._BaseAutoModelClass.from_pretrained.cache_dir",description:`<strong>cache_dir</strong> (<code>str</code> or <code>os.PathLike</code>, <em>optional</em>) &#x2014;
Path to a directory in which a downloaded pretrained model configuration should be cached if the
standard cache should not be used.`,name:"cache_dir"},{anchor:"transformers.models.auto.auto_factory._BaseAutoModelClass.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.models.auto.auto_factory._BaseAutoModelClass.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.models.auto.auto_factory._BaseAutoModelClass.from_pretrained.resume_download",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.models.auto.auto_factory._BaseAutoModelClass.from_pretrained.proxies",description:`<strong>proxies</strong> (<code>Dict[str, str]</code>, <em>optional</em>) &#x2014;
A dictionary of proxy servers to use by protocol or endpoint, e.g., <code>{&apos;http&apos;: &apos;foo.bar:3128&apos;, &apos;http://hostname&apos;: &apos;foo.bar:4012&apos;}</code>. The proxies are used on each request.`,name:"proxies"},{anchor:"transformers.models.auto.auto_factory._BaseAutoModelClass.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.models.auto.auto_factory._BaseAutoModelClass.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.models.auto.auto_factory._BaseAutoModelClass.from_pretrained.revision(str,",description:`<strong>revision(<code>str</code>,</strong> <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a
git-based system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any
identifier allowed by git.`,name:"revision(str,"},{anchor:"transformers.models.auto.auto_factory._BaseAutoModelClass.from_pretrained.trust_remote_code",description:`<strong>trust_remote_code</strong> (<code>bool</code>, <em>optional</em>, defaults 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.models.auto.auto_factory._BaseAutoModelClass.from_pretrained.kwargs",description:`<strong>kwargs</strong> (additional keyword arguments, <em>optional</em>) &#x2014;
Can be used to update the configuration object (after it being loaded) and initiate the model (e.g.,
<code>output_attentions=True</code>). Behaves differently depending on whether a <code>config</code> is provided or
automatically loaded:</p>
<ul>
<li>If a configuration is provided with <code>config</code>, <code>**kwargs</code> will be directly passed to the
underlying model&#x2019;s <code>__init__</code> method (we assume all relevant updates to the configuration have
already been done)</li>
<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_15839/en/main_classes/configuration#transformers.PretrainedConfig.from_pretrained">from_pretrained()</a>). Each key of <code>kwargs</code> that
corresponds to a configuration attribute will be used to override said attribute with the
supplied <code>kwargs</code> value. 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"}]}}),fA=new w({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>)`}}),mA=new X({}),gA=new M({props:{name:"class transformers.FlaxAutoModel",anchor:"transformers.FlaxAutoModel",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/pr_15839/src/transformers/models/auto/modeling_flax_auto.py#L237"}}),pA=new M({props:{name:"from_config",anchor:"transformers.models.auto.auto_factory._BaseAutoModelClass.from_config",parameters:[{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/pr_15839/src/transformers/models/auto/auto_factory.py#L390",parametersDescription:[{anchor:"transformers.models.auto.auto_factory._BaseAutoModelClass.from_config.config",description:`<strong>config</strong> (<a href="/docs/transformers/pr_15839/en/main_classes/configuration#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_15839/en/model_doc/albert#transformers.AlbertConfig">AlbertConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/albert#transformers.FlaxAlbertModel">FlaxAlbertModel</a> (ALBERT model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/bart#transformers.BartConfig">BartConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/bart#transformers.FlaxBartModel">FlaxBartModel</a> (BART model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/beit#transformers.BeitConfig">BeitConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/beit#transformers.FlaxBeitModel">FlaxBeitModel</a> (BEiT model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/bert#transformers.BertConfig">BertConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/bert#transformers.FlaxBertModel">FlaxBertModel</a> (BERT model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/big_bird#transformers.BigBirdConfig">BigBirdConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/big_bird#transformers.FlaxBigBirdModel">FlaxBigBirdModel</a> (BigBird model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/blenderbot#transformers.BlenderbotConfig">BlenderbotConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/blenderbot#transformers.FlaxBlenderbotModel">FlaxBlenderbotModel</a> (Blenderbot model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/blenderbot-small#transformers.BlenderbotSmallConfig">BlenderbotSmallConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/blenderbot-small#transformers.FlaxBlenderbotSmallModel">FlaxBlenderbotSmallModel</a> (BlenderbotSmall model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/clip#transformers.CLIPConfig">CLIPConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/clip#transformers.FlaxCLIPModel">FlaxCLIPModel</a> (CLIP model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/distilbert#transformers.DistilBertConfig">DistilBertConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/distilbert#transformers.FlaxDistilBertModel">FlaxDistilBertModel</a> (DistilBERT model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/electra#transformers.ElectraConfig">ElectraConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/electra#transformers.FlaxElectraModel">FlaxElectraModel</a> (ELECTRA model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/gpt2#transformers.GPT2Config">GPT2Config</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/gpt2#transformers.FlaxGPT2Model">FlaxGPT2Model</a> (OpenAI GPT-2 model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/gptj#transformers.GPTJConfig">GPTJConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/gptj#transformers.FlaxGPTJModel">FlaxGPTJModel</a> (GPT-J model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/gpt_neo#transformers.GPTNeoConfig">GPTNeoConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/gpt_neo#transformers.FlaxGPTNeoModel">FlaxGPTNeoModel</a> (GPT Neo model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/mbart#transformers.MBartConfig">MBartConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/mbart#transformers.FlaxMBartModel">FlaxMBartModel</a> (mBART model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/mt5#transformers.MT5Config">MT5Config</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/mt5#transformers.FlaxMT5Model">FlaxMT5Model</a> (mT5 model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/marian#transformers.MarianConfig">MarianConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/marian#transformers.FlaxMarianModel">FlaxMarianModel</a> (Marian model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/pegasus#transformers.PegasusConfig">PegasusConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/pegasus#transformers.FlaxPegasusModel">FlaxPegasusModel</a> (Pegasus model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/roformer#transformers.RoFormerConfig">RoFormerConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/roformer#transformers.FlaxRoFormerModel">FlaxRoFormerModel</a> (RoFormer model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/roberta#transformers.RobertaConfig">RobertaConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/roberta#transformers.FlaxRobertaModel">FlaxRobertaModel</a> (RoBERTa model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/t5#transformers.T5Config">T5Config</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/t5#transformers.FlaxT5Model">FlaxT5Model</a> (T5 model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/vit#transformers.ViTConfig">ViTConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/vit#transformers.FlaxViTModel">FlaxViTModel</a> (ViT model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/vision-text-dual-encoder#transformers.VisionTextDualEncoderConfig">VisionTextDualEncoderConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/vision-text-dual-encoder#transformers.FlaxVisionTextDualEncoderModel">FlaxVisionTextDualEncoderModel</a> (VisionTextDualEncoder model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/wav2vec2#transformers.Wav2Vec2Config">Wav2Vec2Config</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/wav2vec2#transformers.FlaxWav2Vec2Model">FlaxWav2Vec2Model</a> (Wav2Vec2 model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/xglm#transformers.XGLMConfig">XGLMConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/xglm#transformers.FlaxXGLMModel">FlaxXGLMModel</a> (XGLM model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/xlm-roberta#transformers.XLMRobertaConfig">XLMRobertaConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/xlm-roberta#transformers.FlaxXLMRobertaModel">FlaxXLMRobertaModel</a> (XLM-RoBERTa model)</li>
</ul>`,name:"config"}]}}),_A=new w({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)`}}),uA=new M({props:{name:"from_pretrained",anchor:"transformers.models.auto.auto_factory._BaseAutoModelClass.from_pretrained",parameters:[{name:"*model_args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/pr_15839/src/transformers/models/auto/auto_factory.py#L418",parametersDescription:[{anchor:"transformers.models.auto.auto_factory._BaseAutoModelClass.from_pretrained.pretrained_model_name_or_path",description:`<strong>pretrained_model_name_or_path</strong> (<code>str</code> or <code>os.PathLike</code>) &#x2014;
Can be either:</p>
<ul>
<li>A string, the <em>model id</em> of a pretrained model hosted inside a model repo on huggingface.co.
Valid model ids can be located at the root-level, like <code>bert-base-uncased</code>, or namespaced under a
user or organization name, like <code>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_15839/en/main_classes/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.models.auto.auto_factory._BaseAutoModelClass.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.models.auto.auto_factory._BaseAutoModelClass.from_pretrained.config",description:`<strong>config</strong> (<a href="/docs/transformers/pr_15839/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>, <em>optional</em>) &#x2014;
Configuration for the model to use instead of an automatically loaded configuration. Configuration can
be automatically loaded when:</p>
<ul>
<li>The model is a model provided by the 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_15839/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and is reloaded by supplying the
save directory.</li>
<li>The model is loaded by supplying a local directory 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.models.auto.auto_factory._BaseAutoModelClass.from_pretrained.cache_dir",description:`<strong>cache_dir</strong> (<code>str</code> or <code>os.PathLike</code>, <em>optional</em>) &#x2014;
Path to a directory in which a downloaded pretrained model configuration should be cached if the
standard cache should not be used.`,name:"cache_dir"},{anchor:"transformers.models.auto.auto_factory._BaseAutoModelClass.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.models.auto.auto_factory._BaseAutoModelClass.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.models.auto.auto_factory._BaseAutoModelClass.from_pretrained.resume_download",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.models.auto.auto_factory._BaseAutoModelClass.from_pretrained.proxies",description:`<strong>proxies</strong> (<code>Dict[str, str]</code>, <em>optional</em>) &#x2014;
A dictionary of proxy servers to use by protocol or endpoint, e.g., <code>{&apos;http&apos;: &apos;foo.bar:3128&apos;, &apos;http://hostname&apos;: &apos;foo.bar:4012&apos;}</code>. The proxies are used on each request.`,name:"proxies"},{anchor:"transformers.models.auto.auto_factory._BaseAutoModelClass.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.models.auto.auto_factory._BaseAutoModelClass.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.models.auto.auto_factory._BaseAutoModelClass.from_pretrained.revision(str,",description:`<strong>revision(<code>str</code>,</strong> <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a
git-based system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any
identifier allowed by git.`,name:"revision(str,"},{anchor:"transformers.models.auto.auto_factory._BaseAutoModelClass.from_pretrained.trust_remote_code",description:`<strong>trust_remote_code</strong> (<code>bool</code>, <em>optional</em>, defaults 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.models.auto.auto_factory._BaseAutoModelClass.from_pretrained.kwargs",description:`<strong>kwargs</strong> (additional keyword arguments, <em>optional</em>) &#x2014;
Can be used to update the configuration object (after it being loaded) and initiate the model (e.g.,
<code>output_attentions=True</code>). Behaves differently depending on whether a <code>config</code> is provided or
automatically loaded:</p>
<ul>
<li>If a configuration is provided with <code>config</code>, <code>**kwargs</code> will be directly passed to the
underlying model&#x2019;s <code>__init__</code> method (we assume all relevant updates to the configuration have
already been done)</li>
<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_15839/en/main_classes/configuration#transformers.PretrainedConfig.from_pretrained">from_pretrained()</a>). Each key of <code>kwargs</code> that
corresponds to a configuration attribute will be used to override said attribute with the
supplied <code>kwargs</code> value. 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"}]}}),bA=new w({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>)`}}),vA=new X({}),TA=new M({props:{name:"class transformers.FlaxAutoModelForCausalLM",anchor:"transformers.FlaxAutoModelForCausalLM",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/pr_15839/src/transformers/models/auto/modeling_flax_auto.py#L251"}}),CA=new M({props:{name:"from_config",anchor:"transformers.models.auto.auto_factory._BaseAutoModelClass.from_config",parameters:[{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/pr_15839/src/transformers/models/auto/auto_factory.py#L390",parametersDescription:[{anchor:"transformers.models.auto.auto_factory._BaseAutoModelClass.from_config.config",description:`<strong>config</strong> (<a href="/docs/transformers/pr_15839/en/main_classes/configuration#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_15839/en/model_doc/bart#transformers.BartConfig">BartConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/bart#transformers.FlaxBartForCausalLM">FlaxBartForCausalLM</a> (BART model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/gpt2#transformers.GPT2Config">GPT2Config</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/gpt2#transformers.FlaxGPT2LMHeadModel">FlaxGPT2LMHeadModel</a> (OpenAI GPT-2 model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/gptj#transformers.GPTJConfig">GPTJConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/gptj#transformers.FlaxGPTJForCausalLM">FlaxGPTJForCausalLM</a> (GPT-J model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/gpt_neo#transformers.GPTNeoConfig">GPTNeoConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/gpt_neo#transformers.FlaxGPTNeoForCausalLM">FlaxGPTNeoForCausalLM</a> (GPT Neo model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/xglm#transformers.XGLMConfig">XGLMConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/xglm#transformers.FlaxXGLMForCausalLM">FlaxXGLMForCausalLM</a> (XGLM model)</li>
</ul>`,name:"config"}]}}),MA=new w({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)`}}),EA=new M({props:{name:"from_pretrained",anchor:"transformers.models.auto.auto_factory._BaseAutoModelClass.from_pretrained",parameters:[{name:"*model_args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/pr_15839/src/transformers/models/auto/auto_factory.py#L418",parametersDescription:[{anchor:"transformers.models.auto.auto_factory._BaseAutoModelClass.from_pretrained.pretrained_model_name_or_path",description:`<strong>pretrained_model_name_or_path</strong> (<code>str</code> or <code>os.PathLike</code>) &#x2014;
Can be either:</p>
<ul>
<li>A string, the <em>model id</em> of a pretrained model hosted inside a model repo on huggingface.co.
Valid model ids can be located at the root-level, like <code>bert-base-uncased</code>, or namespaced under a
user or organization name, like <code>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_15839/en/main_classes/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.models.auto.auto_factory._BaseAutoModelClass.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.models.auto.auto_factory._BaseAutoModelClass.from_pretrained.config",description:`<strong>config</strong> (<a href="/docs/transformers/pr_15839/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>, <em>optional</em>) &#x2014;
Configuration for the model to use instead of an automatically loaded configuration. Configuration can
be automatically loaded when:</p>
<ul>
<li>The model is a model provided by the 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_15839/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and is reloaded by supplying the
save directory.</li>
<li>The model is loaded by supplying a local directory 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.models.auto.auto_factory._BaseAutoModelClass.from_pretrained.cache_dir",description:`<strong>cache_dir</strong> (<code>str</code> or <code>os.PathLike</code>, <em>optional</em>) &#x2014;
Path to a directory in which a downloaded pretrained model configuration should be cached if the
standard cache should not be used.`,name:"cache_dir"},{anchor:"transformers.models.auto.auto_factory._BaseAutoModelClass.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.models.auto.auto_factory._BaseAutoModelClass.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.models.auto.auto_factory._BaseAutoModelClass.from_pretrained.resume_download",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.models.auto.auto_factory._BaseAutoModelClass.from_pretrained.proxies",description:`<strong>proxies</strong> (<code>Dict[str, str]</code>, <em>optional</em>) &#x2014;
A dictionary of proxy servers to use by protocol or endpoint, e.g., <code>{&apos;http&apos;: &apos;foo.bar:3128&apos;, &apos;http://hostname&apos;: &apos;foo.bar:4012&apos;}</code>. The proxies are used on each request.`,name:"proxies"},{anchor:"transformers.models.auto.auto_factory._BaseAutoModelClass.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.models.auto.auto_factory._BaseAutoModelClass.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.models.auto.auto_factory._BaseAutoModelClass.from_pretrained.revision(str,",description:`<strong>revision(<code>str</code>,</strong> <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a
git-based system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any
identifier allowed by git.`,name:"revision(str,"},{anchor:"transformers.models.auto.auto_factory._BaseAutoModelClass.from_pretrained.trust_remote_code",description:`<strong>trust_remote_code</strong> (<code>bool</code>, <em>optional</em>, defaults 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.models.auto.auto_factory._BaseAutoModelClass.from_pretrained.kwargs",description:`<strong>kwargs</strong> (additional keyword arguments, <em>optional</em>) &#x2014;
Can be used to update the configuration object (after it being loaded) and initiate the model (e.g.,
<code>output_attentions=True</code>). Behaves differently depending on whether a <code>config</code> is provided or
automatically loaded:</p>
<ul>
<li>If a configuration is provided with <code>config</code>, <code>**kwargs</code> will be directly passed to the
underlying model&#x2019;s <code>__init__</code> method (we assume all relevant updates to the configuration have
already been done)</li>
<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_15839/en/main_classes/configuration#transformers.PretrainedConfig.from_pretrained">from_pretrained()</a>). Each key of <code>kwargs</code> that
corresponds to a configuration attribute will be used to override said attribute with the
supplied <code>kwargs</code> value. 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"}]}}),yA=new w({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>)`}}),wA=new X({}),AA=new M({props:{name:"class transformers.FlaxAutoModelForPreTraining",anchor:"transformers.FlaxAutoModelForPreTraining",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/pr_15839/src/transformers/models/auto/modeling_flax_auto.py#L244"}}),BA=new M({props:{name:"from_config",anchor:"transformers.models.auto.auto_factory._BaseAutoModelClass.from_config",parameters:[{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/pr_15839/src/transformers/models/auto/auto_factory.py#L390",parametersDescription:[{anchor:"transformers.models.auto.auto_factory._BaseAutoModelClass.from_config.config",description:`<strong>config</strong> (<a href="/docs/transformers/pr_15839/en/main_classes/configuration#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_15839/en/model_doc/albert#transformers.AlbertConfig">AlbertConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/albert#transformers.FlaxAlbertForPreTraining">FlaxAlbertForPreTraining</a> (ALBERT model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/bart#transformers.BartConfig">BartConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/bart#transformers.FlaxBartForConditionalGeneration">FlaxBartForConditionalGeneration</a> (BART model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/bert#transformers.BertConfig">BertConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/bert#transformers.FlaxBertForPreTraining">FlaxBertForPreTraining</a> (BERT model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/big_bird#transformers.BigBirdConfig">BigBirdConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/big_bird#transformers.FlaxBigBirdForPreTraining">FlaxBigBirdForPreTraining</a> (BigBird model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/electra#transformers.ElectraConfig">ElectraConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/electra#transformers.FlaxElectraForPreTraining">FlaxElectraForPreTraining</a> (ELECTRA model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/mbart#transformers.MBartConfig">MBartConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/mbart#transformers.FlaxMBartForConditionalGeneration">FlaxMBartForConditionalGeneration</a> (mBART model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/mt5#transformers.MT5Config">MT5Config</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/mt5#transformers.FlaxMT5ForConditionalGeneration">FlaxMT5ForConditionalGeneration</a> (mT5 model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/roformer#transformers.RoFormerConfig">RoFormerConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/roformer#transformers.FlaxRoFormerForMaskedLM">FlaxRoFormerForMaskedLM</a> (RoFormer model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/roberta#transformers.RobertaConfig">RobertaConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/roberta#transformers.FlaxRobertaForMaskedLM">FlaxRobertaForMaskedLM</a> (RoBERTa model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/t5#transformers.T5Config">T5Config</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/t5#transformers.FlaxT5ForConditionalGeneration">FlaxT5ForConditionalGeneration</a> (T5 model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/wav2vec2#transformers.Wav2Vec2Config">Wav2Vec2Config</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/wav2vec2#transformers.FlaxWav2Vec2ForPreTraining">FlaxWav2Vec2ForPreTraining</a> (Wav2Vec2 model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/xlm-roberta#transformers.XLMRobertaConfig">XLMRobertaConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/xlm-roberta#transformers.FlaxXLMRobertaForMaskedLM">FlaxXLMRobertaForMaskedLM</a> (XLM-RoBERTa model)</li>
</ul>`,name:"config"}]}}),xA=new w({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)`}}),kA=new M({props:{name:"from_pretrained",anchor:"transformers.models.auto.auto_factory._BaseAutoModelClass.from_pretrained",parameters:[{name:"*model_args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/pr_15839/src/transformers/models/auto/auto_factory.py#L418",parametersDescription:[{anchor:"transformers.models.auto.auto_factory._BaseAutoModelClass.from_pretrained.pretrained_model_name_or_path",description:`<strong>pretrained_model_name_or_path</strong> (<code>str</code> or <code>os.PathLike</code>) &#x2014;
Can be either:</p>
<ul>
<li>A string, the <em>model id</em> of a pretrained model hosted inside a model repo on huggingface.co.
Valid model ids can be located at the root-level, like <code>bert-base-uncased</code>, or namespaced under a
user or organization name, like <code>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_15839/en/main_classes/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.models.auto.auto_factory._BaseAutoModelClass.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.models.auto.auto_factory._BaseAutoModelClass.from_pretrained.config",description:`<strong>config</strong> (<a href="/docs/transformers/pr_15839/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>, <em>optional</em>) &#x2014;
Configuration for the model to use instead of an automatically loaded configuration. Configuration can
be automatically loaded when:</p>
<ul>
<li>The model is a model provided by the 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_15839/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and is reloaded by supplying the
save directory.</li>
<li>The model is loaded by supplying a local directory 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.models.auto.auto_factory._BaseAutoModelClass.from_pretrained.cache_dir",description:`<strong>cache_dir</strong> (<code>str</code> or <code>os.PathLike</code>, <em>optional</em>) &#x2014;
Path to a directory in which a downloaded pretrained model configuration should be cached if the
standard cache should not be used.`,name:"cache_dir"},{anchor:"transformers.models.auto.auto_factory._BaseAutoModelClass.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.models.auto.auto_factory._BaseAutoModelClass.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.models.auto.auto_factory._BaseAutoModelClass.from_pretrained.resume_download",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.models.auto.auto_factory._BaseAutoModelClass.from_pretrained.proxies",description:`<strong>proxies</strong> (<code>Dict[str, str]</code>, <em>optional</em>) &#x2014;
A dictionary of proxy servers to use by protocol or endpoint, e.g., <code>{&apos;http&apos;: &apos;foo.bar:3128&apos;, &apos;http://hostname&apos;: &apos;foo.bar:4012&apos;}</code>. The proxies are used on each request.`,name:"proxies"},{anchor:"transformers.models.auto.auto_factory._BaseAutoModelClass.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.models.auto.auto_factory._BaseAutoModelClass.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.models.auto.auto_factory._BaseAutoModelClass.from_pretrained.revision(str,",description:`<strong>revision(<code>str</code>,</strong> <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a
git-based system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any
identifier allowed by git.`,name:"revision(str,"},{anchor:"transformers.models.auto.auto_factory._BaseAutoModelClass.from_pretrained.trust_remote_code",description:`<strong>trust_remote_code</strong> (<code>bool</code>, <em>optional</em>, defaults 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.models.auto.auto_factory._BaseAutoModelClass.from_pretrained.kwargs",description:`<strong>kwargs</strong> (additional keyword arguments, <em>optional</em>) &#x2014;
Can be used to update the configuration object (after it being loaded) and initiate the model (e.g.,
<code>output_attentions=True</code>). Behaves differently depending on whether a <code>config</code> is provided or
automatically loaded:</p>
<ul>
<li>If a configuration is provided with <code>config</code>, <code>**kwargs</code> will be directly passed to the
underlying model&#x2019;s <code>__init__</code> method (we assume all relevant updates to the configuration have
already been done)</li>
<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_15839/en/main_classes/configuration#transformers.PretrainedConfig.from_pretrained">from_pretrained()</a>). Each key of <code>kwargs</code> that
corresponds to a configuration attribute will be used to override said attribute with the
supplied <code>kwargs</code> value. 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"}]}}),RA=new w({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>)`}}),SA=new X({}),PA=new M({props:{name:"class transformers.FlaxAutoModelForMaskedLM",anchor:"transformers.FlaxAutoModelForMaskedLM",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/pr_15839/src/transformers/models/auto/modeling_flax_auto.py#L258"}}),IA=new M({props:{name:"from_config",anchor:"transformers.models.auto.auto_factory._BaseAutoModelClass.from_config",parameters:[{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/pr_15839/src/transformers/models/auto/auto_factory.py#L390",parametersDescription:[{anchor:"transformers.models.auto.auto_factory._BaseAutoModelClass.from_config.config",description:`<strong>config</strong> (<a href="/docs/transformers/pr_15839/en/main_classes/configuration#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_15839/en/model_doc/albert#transformers.AlbertConfig">AlbertConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/albert#transformers.FlaxAlbertForMaskedLM">FlaxAlbertForMaskedLM</a> (ALBERT model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/bart#transformers.BartConfig">BartConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/bart#transformers.FlaxBartForConditionalGeneration">FlaxBartForConditionalGeneration</a> (BART model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/bert#transformers.BertConfig">BertConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/bert#transformers.FlaxBertForMaskedLM">FlaxBertForMaskedLM</a> (BERT model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/big_bird#transformers.BigBirdConfig">BigBirdConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/big_bird#transformers.FlaxBigBirdForMaskedLM">FlaxBigBirdForMaskedLM</a> (BigBird model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/distilbert#transformers.DistilBertConfig">DistilBertConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/distilbert#transformers.FlaxDistilBertForMaskedLM">FlaxDistilBertForMaskedLM</a> (DistilBERT model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/electra#transformers.ElectraConfig">ElectraConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/electra#transformers.FlaxElectraForMaskedLM">FlaxElectraForMaskedLM</a> (ELECTRA model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/mbart#transformers.MBartConfig">MBartConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/mbart#transformers.FlaxMBartForConditionalGeneration">FlaxMBartForConditionalGeneration</a> (mBART model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/roformer#transformers.RoFormerConfig">RoFormerConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/roformer#transformers.FlaxRoFormerForMaskedLM">FlaxRoFormerForMaskedLM</a> (RoFormer model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/roberta#transformers.RobertaConfig">RobertaConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/roberta#transformers.FlaxRobertaForMaskedLM">FlaxRobertaForMaskedLM</a> (RoBERTa model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/xlm-roberta#transformers.XLMRobertaConfig">XLMRobertaConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/xlm-roberta#transformers.FlaxXLMRobertaForMaskedLM">FlaxXLMRobertaForMaskedLM</a> (XLM-RoBERTa model)</li>
</ul>`,name:"config"}]}}),jA=new w({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)`}}),DA=new M({props:{name:"from_pretrained",anchor:"transformers.models.auto.auto_factory._BaseAutoModelClass.from_pretrained",parameters:[{name:"*model_args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/pr_15839/src/transformers/models/auto/auto_factory.py#L418",parametersDescription:[{anchor:"transformers.models.auto.auto_factory._BaseAutoModelClass.from_pretrained.pretrained_model_name_or_path",description:`<strong>pretrained_model_name_or_path</strong> (<code>str</code> or <code>os.PathLike</code>) &#x2014;
Can be either:</p>
<ul>
<li>A string, the <em>model id</em> of a pretrained model hosted inside a model repo on huggingface.co.
Valid model ids can be located at the root-level, like <code>bert-base-uncased</code>, or namespaced under a
user or organization name, like <code>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_15839/en/main_classes/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.models.auto.auto_factory._BaseAutoModelClass.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.models.auto.auto_factory._BaseAutoModelClass.from_pretrained.config",description:`<strong>config</strong> (<a href="/docs/transformers/pr_15839/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>, <em>optional</em>) &#x2014;
Configuration for the model to use instead of an automatically loaded configuration. Configuration can
be automatically loaded when:</p>
<ul>
<li>The model is a model provided by the 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_15839/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and is reloaded by supplying the
save directory.</li>
<li>The model is loaded by supplying a local directory 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.models.auto.auto_factory._BaseAutoModelClass.from_pretrained.cache_dir",description:`<strong>cache_dir</strong> (<code>str</code> or <code>os.PathLike</code>, <em>optional</em>) &#x2014;
Path to a directory in which a downloaded pretrained model configuration should be cached if the
standard cache should not be used.`,name:"cache_dir"},{anchor:"transformers.models.auto.auto_factory._BaseAutoModelClass.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.models.auto.auto_factory._BaseAutoModelClass.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.models.auto.auto_factory._BaseAutoModelClass.from_pretrained.resume_download",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.models.auto.auto_factory._BaseAutoModelClass.from_pretrained.proxies",description:`<strong>proxies</strong> (<code>Dict[str, str]</code>, <em>optional</em>) &#x2014;
A dictionary of proxy servers to use by protocol or endpoint, e.g., <code>{&apos;http&apos;: &apos;foo.bar:3128&apos;, &apos;http://hostname&apos;: &apos;foo.bar:4012&apos;}</code>. The proxies are used on each request.`,name:"proxies"},{anchor:"transformers.models.auto.auto_factory._BaseAutoModelClass.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.models.auto.auto_factory._BaseAutoModelClass.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.models.auto.auto_factory._BaseAutoModelClass.from_pretrained.revision(str,",description:`<strong>revision(<code>str</code>,</strong> <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a
git-based system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any
identifier allowed by git.`,name:"revision(str,"},{anchor:"transformers.models.auto.auto_factory._BaseAutoModelClass.from_pretrained.trust_remote_code",description:`<strong>trust_remote_code</strong> (<code>bool</code>, <em>optional</em>, defaults 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.models.auto.auto_factory._BaseAutoModelClass.from_pretrained.kwargs",description:`<strong>kwargs</strong> (additional keyword arguments, <em>optional</em>) &#x2014;
Can be used to update the configuration object (after it being loaded) and initiate the model (e.g.,
<code>output_attentions=True</code>). Behaves differently depending on whether a <code>config</code> is provided or
automatically loaded:</p>
<ul>
<li>If a configuration is provided with <code>config</code>, <code>**kwargs</code> will be directly passed to the
underlying model&#x2019;s <code>__init__</code> method (we assume all relevant updates to the configuration have
already been done)</li>
<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_15839/en/main_classes/configuration#transformers.PretrainedConfig.from_pretrained">from_pretrained()</a>). Each key of <code>kwargs</code> that
corresponds to a configuration attribute will be used to override said attribute with the
supplied <code>kwargs</code> value. 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"}]}}),NA=new w({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>)`}}),qA=new X({}),OA=new M({props:{name:"class transformers.FlaxAutoModelForSeq2SeqLM",anchor:"transformers.FlaxAutoModelForSeq2SeqLM",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/pr_15839/src/transformers/models/auto/modeling_flax_auto.py#L265"}}),XA=new M({props:{name:"from_config",anchor:"transformers.models.auto.auto_factory._BaseAutoModelClass.from_config",parameters:[{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/pr_15839/src/transformers/models/auto/auto_factory.py#L390",parametersDescription:[{anchor:"transformers.models.auto.auto_factory._BaseAutoModelClass.from_config.config",description:`<strong>config</strong> (<a href="/docs/transformers/pr_15839/en/main_classes/configuration#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_15839/en/model_doc/bart#transformers.BartConfig">BartConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/bart#transformers.FlaxBartForConditionalGeneration">FlaxBartForConditionalGeneration</a> (BART model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/blenderbot#transformers.BlenderbotConfig">BlenderbotConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/blenderbot#transformers.FlaxBlenderbotForConditionalGeneration">FlaxBlenderbotForConditionalGeneration</a> (Blenderbot model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/blenderbot-small#transformers.BlenderbotSmallConfig">BlenderbotSmallConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/blenderbot-small#transformers.FlaxBlenderbotSmallForConditionalGeneration">FlaxBlenderbotSmallForConditionalGeneration</a> (BlenderbotSmall model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/encoder-decoder#transformers.EncoderDecoderConfig">EncoderDecoderConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/encoder-decoder#transformers.FlaxEncoderDecoderModel">FlaxEncoderDecoderModel</a> (Encoder decoder model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/mbart#transformers.MBartConfig">MBartConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/mbart#transformers.FlaxMBartForConditionalGeneration">FlaxMBartForConditionalGeneration</a> (mBART model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/mt5#transformers.MT5Config">MT5Config</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/mt5#transformers.FlaxMT5ForConditionalGeneration">FlaxMT5ForConditionalGeneration</a> (mT5 model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/marian#transformers.MarianConfig">MarianConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/marian#transformers.FlaxMarianMTModel">FlaxMarianMTModel</a> (Marian model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/pegasus#transformers.PegasusConfig">PegasusConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/pegasus#transformers.FlaxPegasusForConditionalGeneration">FlaxPegasusForConditionalGeneration</a> (Pegasus model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/t5#transformers.T5Config">T5Config</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/t5#transformers.FlaxT5ForConditionalGeneration">FlaxT5ForConditionalGeneration</a> (T5 model)</li>
</ul>`,name:"config"}]}}),VA=new w({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)`}}),zA=new M({props:{name:"from_pretrained",anchor:"transformers.models.auto.auto_factory._BaseAutoModelClass.from_pretrained",parameters:[{name:"*model_args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/pr_15839/src/transformers/models/auto/auto_factory.py#L418",parametersDescription:[{anchor:"transformers.models.auto.auto_factory._BaseAutoModelClass.from_pretrained.pretrained_model_name_or_path",description:`<strong>pretrained_model_name_or_path</strong> (<code>str</code> or <code>os.PathLike</code>) &#x2014;
Can be either:</p>
<ul>
<li>A string, the <em>model id</em> of a pretrained model hosted inside a model repo on huggingface.co.
Valid model ids can be located at the root-level, like <code>bert-base-uncased</code>, or namespaced under a
user or organization name, like <code>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_15839/en/main_classes/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.models.auto.auto_factory._BaseAutoModelClass.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.models.auto.auto_factory._BaseAutoModelClass.from_pretrained.config",description:`<strong>config</strong> (<a href="/docs/transformers/pr_15839/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>, <em>optional</em>) &#x2014;
Configuration for the model to use instead of an automatically loaded configuration. Configuration can
be automatically loaded when:</p>
<ul>
<li>The model is a model provided by the 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_15839/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and is reloaded by supplying the
save directory.</li>
<li>The model is loaded by supplying a local directory 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.models.auto.auto_factory._BaseAutoModelClass.from_pretrained.cache_dir",description:`<strong>cache_dir</strong> (<code>str</code> or <code>os.PathLike</code>, <em>optional</em>) &#x2014;
Path to a directory in which a downloaded pretrained model configuration should be cached if the
standard cache should not be used.`,name:"cache_dir"},{anchor:"transformers.models.auto.auto_factory._BaseAutoModelClass.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.models.auto.auto_factory._BaseAutoModelClass.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.models.auto.auto_factory._BaseAutoModelClass.from_pretrained.resume_download",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.models.auto.auto_factory._BaseAutoModelClass.from_pretrained.proxies",description:`<strong>proxies</strong> (<code>Dict[str, str]</code>, <em>optional</em>) &#x2014;
A dictionary of proxy servers to use by protocol or endpoint, e.g., <code>{&apos;http&apos;: &apos;foo.bar:3128&apos;, &apos;http://hostname&apos;: &apos;foo.bar:4012&apos;}</code>. The proxies are used on each request.`,name:"proxies"},{anchor:"transformers.models.auto.auto_factory._BaseAutoModelClass.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.models.auto.auto_factory._BaseAutoModelClass.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.models.auto.auto_factory._BaseAutoModelClass.from_pretrained.revision(str,",description:`<strong>revision(<code>str</code>,</strong> <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a
git-based system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any
identifier allowed by git.`,name:"revision(str,"},{anchor:"transformers.models.auto.auto_factory._BaseAutoModelClass.from_pretrained.trust_remote_code",description:`<strong>trust_remote_code</strong> (<code>bool</code>, <em>optional</em>, defaults 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.models.auto.auto_factory._BaseAutoModelClass.from_pretrained.kwargs",description:`<strong>kwargs</strong> (additional keyword arguments, <em>optional</em>) &#x2014;
Can be used to update the configuration object (after it being loaded) and initiate the model (e.g.,
<code>output_attentions=True</code>). Behaves differently depending on whether a <code>config</code> is provided or
automatically loaded:</p>
<ul>
<li>If a configuration is provided with <code>config</code>, <code>**kwargs</code> will be directly passed to the
underlying model&#x2019;s <code>__init__</code> method (we assume all relevant updates to the configuration have
already been done)</li>
<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_15839/en/main_classes/configuration#transformers.PretrainedConfig.from_pretrained">from_pretrained()</a>). Each key of <code>kwargs</code> that
corresponds to a configuration attribute will be used to override said attribute with the
supplied <code>kwargs</code> value. 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"}]}}),WA=new w({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>)`}}),QA=new X({}),HA=new M({props:{name:"class transformers.FlaxAutoModelForSequenceClassification",anchor:"transformers.FlaxAutoModelForSequenceClassification",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/pr_15839/src/transformers/models/auto/modeling_flax_auto.py#L274"}}),JA=new M({props:{name:"from_config",anchor:"transformers.models.auto.auto_factory._BaseAutoModelClass.from_config",parameters:[{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/pr_15839/src/transformers/models/auto/auto_factory.py#L390",parametersDescription:[{anchor:"transformers.models.auto.auto_factory._BaseAutoModelClass.from_config.config",description:`<strong>config</strong> (<a href="/docs/transformers/pr_15839/en/main_classes/configuration#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_15839/en/model_doc/albert#transformers.AlbertConfig">AlbertConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/albert#transformers.FlaxAlbertForSequenceClassification">FlaxAlbertForSequenceClassification</a> (ALBERT model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/bart#transformers.BartConfig">BartConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/bart#transformers.FlaxBartForSequenceClassification">FlaxBartForSequenceClassification</a> (BART model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/bert#transformers.BertConfig">BertConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/bert#transformers.FlaxBertForSequenceClassification">FlaxBertForSequenceClassification</a> (BERT model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/big_bird#transformers.BigBirdConfig">BigBirdConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/big_bird#transformers.FlaxBigBirdForSequenceClassification">FlaxBigBirdForSequenceClassification</a> (BigBird model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/distilbert#transformers.DistilBertConfig">DistilBertConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/distilbert#transformers.FlaxDistilBertForSequenceClassification">FlaxDistilBertForSequenceClassification</a> (DistilBERT model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/electra#transformers.ElectraConfig">ElectraConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/electra#transformers.FlaxElectraForSequenceClassification">FlaxElectraForSequenceClassification</a> (ELECTRA model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/mbart#transformers.MBartConfig">MBartConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/mbart#transformers.FlaxMBartForSequenceClassification">FlaxMBartForSequenceClassification</a> (mBART model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/roformer#transformers.RoFormerConfig">RoFormerConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/roformer#transformers.FlaxRoFormerForSequenceClassification">FlaxRoFormerForSequenceClassification</a> (RoFormer model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/roberta#transformers.RobertaConfig">RobertaConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/roberta#transformers.FlaxRobertaForSequenceClassification">FlaxRobertaForSequenceClassification</a> (RoBERTa model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/xlm-roberta#transformers.XLMRobertaConfig">XLMRobertaConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/xlm-roberta#transformers.FlaxXLMRobertaForSequenceClassification">FlaxXLMRobertaForSequenceClassification</a> (XLM-RoBERTa model)</li>
</ul>`,name:"config"}]}}),YA=new w({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)`}}),KA=new M({props:{name:"from_pretrained",anchor:"transformers.models.auto.auto_factory._BaseAutoModelClass.from_pretrained",parameters:[{name:"*model_args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/pr_15839/src/transformers/models/auto/auto_factory.py#L418",parametersDescription:[{anchor:"transformers.models.auto.auto_factory._BaseAutoModelClass.from_pretrained.pretrained_model_name_or_path",description:`<strong>pretrained_model_name_or_path</strong> (<code>str</code> or <code>os.PathLike</code>) &#x2014;
Can be either:</p>
<ul>
<li>A string, the <em>model id</em> of a pretrained model hosted inside a model repo on huggingface.co.
Valid model ids can be located at the root-level, like <code>bert-base-uncased</code>, or namespaced under a
user or organization name, like <code>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_15839/en/main_classes/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.models.auto.auto_factory._BaseAutoModelClass.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.models.auto.auto_factory._BaseAutoModelClass.from_pretrained.config",description:`<strong>config</strong> (<a href="/docs/transformers/pr_15839/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>, <em>optional</em>) &#x2014;
Configuration for the model to use instead of an automatically loaded configuration. Configuration can
be automatically loaded when:</p>
<ul>
<li>The model is a model provided by the 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_15839/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and is reloaded by supplying the
save directory.</li>
<li>The model is loaded by supplying a local directory 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.models.auto.auto_factory._BaseAutoModelClass.from_pretrained.cache_dir",description:`<strong>cache_dir</strong> (<code>str</code> or <code>os.PathLike</code>, <em>optional</em>) &#x2014;
Path to a directory in which a downloaded pretrained model configuration should be cached if the
standard cache should not be used.`,name:"cache_dir"},{anchor:"transformers.models.auto.auto_factory._BaseAutoModelClass.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.models.auto.auto_factory._BaseAutoModelClass.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.models.auto.auto_factory._BaseAutoModelClass.from_pretrained.resume_download",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.models.auto.auto_factory._BaseAutoModelClass.from_pretrained.proxies",description:`<strong>proxies</strong> (<code>Dict[str, str]</code>, <em>optional</em>) &#x2014;
A dictionary of proxy servers to use by protocol or endpoint, e.g., <code>{&apos;http&apos;: &apos;foo.bar:3128&apos;, &apos;http://hostname&apos;: &apos;foo.bar:4012&apos;}</code>. The proxies are used on each request.`,name:"proxies"},{anchor:"transformers.models.auto.auto_factory._BaseAutoModelClass.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.models.auto.auto_factory._BaseAutoModelClass.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.models.auto.auto_factory._BaseAutoModelClass.from_pretrained.revision(str,",description:`<strong>revision(<code>str</code>,</strong> <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a
git-based system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any
identifier allowed by git.`,name:"revision(str,"},{anchor:"transformers.models.auto.auto_factory._BaseAutoModelClass.from_pretrained.trust_remote_code",description:`<strong>trust_remote_code</strong> (<code>bool</code>, <em>optional</em>, defaults 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.models.auto.auto_factory._BaseAutoModelClass.from_pretrained.kwargs",description:`<strong>kwargs</strong> (additional keyword arguments, <em>optional</em>) &#x2014;
Can be used to update the configuration object (after it being loaded) and initiate the model (e.g.,
<code>output_attentions=True</code>). Behaves differently depending on whether a <code>config</code> is provided or
automatically loaded:</p>
<ul>
<li>If a configuration is provided with <code>config</code>, <code>**kwargs</code> will be directly passed to the
underlying model&#x2019;s <code>__init__</code> method (we assume all relevant updates to the configuration have
already been done)</li>
<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_15839/en/main_classes/configuration#transformers.PretrainedConfig.from_pretrained">from_pretrained()</a>). Each key of <code>kwargs</code> that
corresponds to a configuration attribute will be used to override said attribute with the
supplied <code>kwargs</code> value. 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"}]}}),ZA=new w({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>)`}}),e0=new X({}),o0=new M({props:{name:"class transformers.FlaxAutoModelForQuestionAnswering",anchor:"transformers.FlaxAutoModelForQuestionAnswering",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/pr_15839/src/transformers/models/auto/modeling_flax_auto.py#L283"}}),t0=new M({props:{name:"from_config",anchor:"transformers.models.auto.auto_factory._BaseAutoModelClass.from_config",parameters:[{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/pr_15839/src/transformers/models/auto/auto_factory.py#L390",parametersDescription:[{anchor:"transformers.models.auto.auto_factory._BaseAutoModelClass.from_config.config",description:`<strong>config</strong> (<a href="/docs/transformers/pr_15839/en/main_classes/configuration#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_15839/en/model_doc/albert#transformers.AlbertConfig">AlbertConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/albert#transformers.FlaxAlbertForQuestionAnswering">FlaxAlbertForQuestionAnswering</a> (ALBERT model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/bart#transformers.BartConfig">BartConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/bart#transformers.FlaxBartForQuestionAnswering">FlaxBartForQuestionAnswering</a> (BART model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/bert#transformers.BertConfig">BertConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/bert#transformers.FlaxBertForQuestionAnswering">FlaxBertForQuestionAnswering</a> (BERT model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/big_bird#transformers.BigBirdConfig">BigBirdConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/big_bird#transformers.FlaxBigBirdForQuestionAnswering">FlaxBigBirdForQuestionAnswering</a> (BigBird model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/distilbert#transformers.DistilBertConfig">DistilBertConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/distilbert#transformers.FlaxDistilBertForQuestionAnswering">FlaxDistilBertForQuestionAnswering</a> (DistilBERT model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/electra#transformers.ElectraConfig">ElectraConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/electra#transformers.FlaxElectraForQuestionAnswering">FlaxElectraForQuestionAnswering</a> (ELECTRA model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/mbart#transformers.MBartConfig">MBartConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/mbart#transformers.FlaxMBartForQuestionAnswering">FlaxMBartForQuestionAnswering</a> (mBART model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/roformer#transformers.RoFormerConfig">RoFormerConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/roformer#transformers.FlaxRoFormerForQuestionAnswering">FlaxRoFormerForQuestionAnswering</a> (RoFormer model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/roberta#transformers.RobertaConfig">RobertaConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/roberta#transformers.FlaxRobertaForQuestionAnswering">FlaxRobertaForQuestionAnswering</a> (RoBERTa model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/xlm-roberta#transformers.XLMRobertaConfig">XLMRobertaConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/xlm-roberta#transformers.FlaxXLMRobertaForQuestionAnswering">FlaxXLMRobertaForQuestionAnswering</a> (XLM-RoBERTa model)</li>
</ul>`,name:"config"}]}}),a0=new w({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)`}}),n0=new M({props:{name:"from_pretrained",anchor:"transformers.models.auto.auto_factory._BaseAutoModelClass.from_pretrained",parameters:[{name:"*model_args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/pr_15839/src/transformers/models/auto/auto_factory.py#L418",parametersDescription:[{anchor:"transformers.models.auto.auto_factory._BaseAutoModelClass.from_pretrained.pretrained_model_name_or_path",description:`<strong>pretrained_model_name_or_path</strong> (<code>str</code> or <code>os.PathLike</code>) &#x2014;
Can be either:</p>
<ul>
<li>A string, the <em>model id</em> of a pretrained model hosted inside a model repo on huggingface.co.
Valid model ids can be located at the root-level, like <code>bert-base-uncased</code>, or namespaced under a
user or organization name, like <code>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_15839/en/main_classes/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.models.auto.auto_factory._BaseAutoModelClass.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.models.auto.auto_factory._BaseAutoModelClass.from_pretrained.config",description:`<strong>config</strong> (<a href="/docs/transformers/pr_15839/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>, <em>optional</em>) &#x2014;
Configuration for the model to use instead of an automatically loaded configuration. Configuration can
be automatically loaded when:</p>
<ul>
<li>The model is a model provided by the 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_15839/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and is reloaded by supplying the
save directory.</li>
<li>The model is loaded by supplying a local directory 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.models.auto.auto_factory._BaseAutoModelClass.from_pretrained.cache_dir",description:`<strong>cache_dir</strong> (<code>str</code> or <code>os.PathLike</code>, <em>optional</em>) &#x2014;
Path to a directory in which a downloaded pretrained model configuration should be cached if the
standard cache should not be used.`,name:"cache_dir"},{anchor:"transformers.models.auto.auto_factory._BaseAutoModelClass.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.models.auto.auto_factory._BaseAutoModelClass.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.models.auto.auto_factory._BaseAutoModelClass.from_pretrained.resume_download",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.models.auto.auto_factory._BaseAutoModelClass.from_pretrained.proxies",description:`<strong>proxies</strong> (<code>Dict[str, str]</code>, <em>optional</em>) &#x2014;
A dictionary of proxy servers to use by protocol or endpoint, e.g., <code>{&apos;http&apos;: &apos;foo.bar:3128&apos;, &apos;http://hostname&apos;: &apos;foo.bar:4012&apos;}</code>. The proxies are used on each request.`,name:"proxies"},{anchor:"transformers.models.auto.auto_factory._BaseAutoModelClass.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.models.auto.auto_factory._BaseAutoModelClass.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.models.auto.auto_factory._BaseAutoModelClass.from_pretrained.revision(str,",description:`<strong>revision(<code>str</code>,</strong> <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a
git-based system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any
identifier allowed by git.`,name:"revision(str,"},{anchor:"transformers.models.auto.auto_factory._BaseAutoModelClass.from_pretrained.trust_remote_code",description:`<strong>trust_remote_code</strong> (<code>bool</code>, <em>optional</em>, defaults 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.models.auto.auto_factory._BaseAutoModelClass.from_pretrained.kwargs",description:`<strong>kwargs</strong> (additional keyword arguments, <em>optional</em>) &#x2014;
Can be used to update the configuration object (after it being loaded) and initiate the model (e.g.,
<code>output_attentions=True</code>). Behaves differently depending on whether a <code>config</code> is provided or
automatically loaded:</p>
<ul>
<li>If a configuration is provided with <code>config</code>, <code>**kwargs</code> will be directly passed to the
underlying model&#x2019;s <code>__init__</code> method (we assume all relevant updates to the configuration have
already been done)</li>
<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_15839/en/main_classes/configuration#transformers.PretrainedConfig.from_pretrained">from_pretrained()</a>). Each key of <code>kwargs</code> that
corresponds to a configuration attribute will be used to override said attribute with the
supplied <code>kwargs</code> value. 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"}]}}),s0=new w({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>)`}}),l0=new X({}),i0=new M({props:{name:"class transformers.FlaxAutoModelForTokenClassification",anchor:"transformers.FlaxAutoModelForTokenClassification",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/pr_15839/src/transformers/models/auto/modeling_flax_auto.py#L290"}}),c0=new M({props:{name:"from_config",anchor:"transformers.models.auto.auto_factory._BaseAutoModelClass.from_config",parameters:[{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/pr_15839/src/transformers/models/auto/auto_factory.py#L390",parametersDescription:[{anchor:"transformers.models.auto.auto_factory._BaseAutoModelClass.from_config.config",description:`<strong>config</strong> (<a href="/docs/transformers/pr_15839/en/main_classes/configuration#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_15839/en/model_doc/albert#transformers.AlbertConfig">AlbertConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/albert#transformers.FlaxAlbertForTokenClassification">FlaxAlbertForTokenClassification</a> (ALBERT model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/bert#transformers.BertConfig">BertConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/bert#transformers.FlaxBertForTokenClassification">FlaxBertForTokenClassification</a> (BERT model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/big_bird#transformers.BigBirdConfig">BigBirdConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/big_bird#transformers.FlaxBigBirdForTokenClassification">FlaxBigBirdForTokenClassification</a> (BigBird model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/distilbert#transformers.DistilBertConfig">DistilBertConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/distilbert#transformers.FlaxDistilBertForTokenClassification">FlaxDistilBertForTokenClassification</a> (DistilBERT model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/electra#transformers.ElectraConfig">ElectraConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/electra#transformers.FlaxElectraForTokenClassification">FlaxElectraForTokenClassification</a> (ELECTRA model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/roformer#transformers.RoFormerConfig">RoFormerConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/roformer#transformers.FlaxRoFormerForTokenClassification">FlaxRoFormerForTokenClassification</a> (RoFormer model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/roberta#transformers.RobertaConfig">RobertaConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/roberta#transformers.FlaxRobertaForTokenClassification">FlaxRobertaForTokenClassification</a> (RoBERTa model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/xlm-roberta#transformers.XLMRobertaConfig">XLMRobertaConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/xlm-roberta#transformers.FlaxXLMRobertaForTokenClassification">FlaxXLMRobertaForTokenClassification</a> (XLM-RoBERTa model)</li>
</ul>`,name:"config"}]}}),f0=new w({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)`}}),m0=new M({props:{name:"from_pretrained",anchor:"transformers.models.auto.auto_factory._BaseAutoModelClass.from_pretrained",parameters:[{name:"*model_args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/pr_15839/src/transformers/models/auto/auto_factory.py#L418",parametersDescription:[{anchor:"transformers.models.auto.auto_factory._BaseAutoModelClass.from_pretrained.pretrained_model_name_or_path",description:`<strong>pretrained_model_name_or_path</strong> (<code>str</code> or <code>os.PathLike</code>) &#x2014;
Can be either:</p>
<ul>
<li>A string, the <em>model id</em> of a pretrained model hosted inside a model repo on huggingface.co.
Valid model ids can be located at the root-level, like <code>bert-base-uncased</code>, or namespaced under a
user or organization name, like <code>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_15839/en/main_classes/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.models.auto.auto_factory._BaseAutoModelClass.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.models.auto.auto_factory._BaseAutoModelClass.from_pretrained.config",description:`<strong>config</strong> (<a href="/docs/transformers/pr_15839/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>, <em>optional</em>) &#x2014;
Configuration for the model to use instead of an automatically loaded configuration. Configuration can
be automatically loaded when:</p>
<ul>
<li>The model is a model provided by the 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_15839/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and is reloaded by supplying the
save directory.</li>
<li>The model is loaded by supplying a local directory 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.models.auto.auto_factory._BaseAutoModelClass.from_pretrained.cache_dir",description:`<strong>cache_dir</strong> (<code>str</code> or <code>os.PathLike</code>, <em>optional</em>) &#x2014;
Path to a directory in which a downloaded pretrained model configuration should be cached if the
standard cache should not be used.`,name:"cache_dir"},{anchor:"transformers.models.auto.auto_factory._BaseAutoModelClass.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.models.auto.auto_factory._BaseAutoModelClass.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.models.auto.auto_factory._BaseAutoModelClass.from_pretrained.resume_download",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.models.auto.auto_factory._BaseAutoModelClass.from_pretrained.proxies",description:`<strong>proxies</strong> (<code>Dict[str, str]</code>, <em>optional</em>) &#x2014;
A dictionary of proxy servers to use by protocol or endpoint, e.g., <code>{&apos;http&apos;: &apos;foo.bar:3128&apos;, &apos;http://hostname&apos;: &apos;foo.bar:4012&apos;}</code>. The proxies are used on each request.`,name:"proxies"},{anchor:"transformers.models.auto.auto_factory._BaseAutoModelClass.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.models.auto.auto_factory._BaseAutoModelClass.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.models.auto.auto_factory._BaseAutoModelClass.from_pretrained.revision(str,",description:`<strong>revision(<code>str</code>,</strong> <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a
git-based system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any
identifier allowed by git.`,name:"revision(str,"},{anchor:"transformers.models.auto.auto_factory._BaseAutoModelClass.from_pretrained.trust_remote_code",description:`<strong>trust_remote_code</strong> (<code>bool</code>, <em>optional</em>, defaults 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.models.auto.auto_factory._BaseAutoModelClass.from_pretrained.kwargs",description:`<strong>kwargs</strong> (additional keyword arguments, <em>optional</em>) &#x2014;
Can be used to update the configuration object (after it being loaded) and initiate the model (e.g.,
<code>output_attentions=True</code>). Behaves differently depending on whether a <code>config</code> is provided or
automatically loaded:</p>
<ul>
<li>If a configuration is provided with <code>config</code>, <code>**kwargs</code> will be directly passed to the
underlying model&#x2019;s <code>__init__</code> method (we assume all relevant updates to the configuration have
already been done)</li>
<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_15839/en/main_classes/configuration#transformers.PretrainedConfig.from_pretrained">from_pretrained()</a>). Each key of <code>kwargs</code> that
corresponds to a configuration attribute will be used to override said attribute with the
supplied <code>kwargs</code> value. 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"}]}}),g0=new w({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>)`}}),h0=new X({}),p0=new M({props:{name:"class transformers.FlaxAutoModelForMultipleChoice",anchor:"transformers.FlaxAutoModelForMultipleChoice",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/pr_15839/src/transformers/models/auto/modeling_flax_auto.py#L299"}}),u0=new M({props:{name:"from_config",anchor:"transformers.models.auto.auto_factory._BaseAutoModelClass.from_config",parameters:[{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/pr_15839/src/transformers/models/auto/auto_factory.py#L390",parametersDescription:[{anchor:"transformers.models.auto.auto_factory._BaseAutoModelClass.from_config.config",description:`<strong>config</strong> (<a href="/docs/transformers/pr_15839/en/main_classes/configuration#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_15839/en/model_doc/albert#transformers.AlbertConfig">AlbertConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/albert#transformers.FlaxAlbertForMultipleChoice">FlaxAlbertForMultipleChoice</a> (ALBERT model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/bert#transformers.BertConfig">BertConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/bert#transformers.FlaxBertForMultipleChoice">FlaxBertForMultipleChoice</a> (BERT model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/big_bird#transformers.BigBirdConfig">BigBirdConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/big_bird#transformers.FlaxBigBirdForMultipleChoice">FlaxBigBirdForMultipleChoice</a> (BigBird model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/distilbert#transformers.DistilBertConfig">DistilBertConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/distilbert#transformers.FlaxDistilBertForMultipleChoice">FlaxDistilBertForMultipleChoice</a> (DistilBERT model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/electra#transformers.ElectraConfig">ElectraConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/electra#transformers.FlaxElectraForMultipleChoice">FlaxElectraForMultipleChoice</a> (ELECTRA model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/roformer#transformers.RoFormerConfig">RoFormerConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/roformer#transformers.FlaxRoFormerForMultipleChoice">FlaxRoFormerForMultipleChoice</a> (RoFormer model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/roberta#transformers.RobertaConfig">RobertaConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/roberta#transformers.FlaxRobertaForMultipleChoice">FlaxRobertaForMultipleChoice</a> (RoBERTa model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/xlm-roberta#transformers.XLMRobertaConfig">XLMRobertaConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/xlm-roberta#transformers.FlaxXLMRobertaForMultipleChoice">FlaxXLMRobertaForMultipleChoice</a> (XLM-RoBERTa model)</li>
</ul>`,name:"config"}]}}),b0=new w({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)`}}),v0=new M({props:{name:"from_pretrained",anchor:"transformers.models.auto.auto_factory._BaseAutoModelClass.from_pretrained",parameters:[{name:"*model_args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/pr_15839/src/transformers/models/auto/auto_factory.py#L418",parametersDescription:[{anchor:"transformers.models.auto.auto_factory._BaseAutoModelClass.from_pretrained.pretrained_model_name_or_path",description:`<strong>pretrained_model_name_or_path</strong> (<code>str</code> or <code>os.PathLike</code>) &#x2014;
Can be either:</p>
<ul>
<li>A string, the <em>model id</em> of a pretrained model hosted inside a model repo on huggingface.co.
Valid model ids can be located at the root-level, like <code>bert-base-uncased</code>, or namespaced under a
user or organization name, like <code>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_15839/en/main_classes/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.models.auto.auto_factory._BaseAutoModelClass.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_15839/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and is reloaded by supplying the
save directory.</li>
<li>The model is loaded by supplying a local directory 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.models.auto.auto_factory._BaseAutoModelClass.from_pretrained.cache_dir",description:`<strong>cache_dir</strong> (<code>str</code> or <code>os.PathLike</code>, <em>optional</em>) &#x2014;
Path to a directory in which a downloaded pretrained model configuration should be cached if the
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Load the model weights from a PyTorch checkpoint save file (see docstring of
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Whether or not to force the (re-)download of the model weights and configuration files, overriding the
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Whether or not to delete incompletely received files. Will attempt to resume the download if such a
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A dictionary of proxy servers to use by protocol or endpoint, e.g., <code>{&apos;http&apos;: &apos;foo.bar:3128&apos;, &apos;http://hostname&apos;: &apos;foo.bar:4012&apos;}</code>. The proxies are used on each request.`,name:"proxies"},{anchor:"transformers.models.auto.auto_factory._BaseAutoModelClass.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.models.auto.auto_factory._BaseAutoModelClass.from_pretrained.revision(str,",description:`<strong>revision(<code>str</code>,</strong> <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a
git-based system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any
identifier allowed by git.`,name:"revision(str,"},{anchor:"transformers.models.auto.auto_factory._BaseAutoModelClass.from_pretrained.trust_remote_code",description:`<strong>trust_remote_code</strong> (<code>bool</code>, <em>optional</em>, defaults 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.models.auto.auto_factory._BaseAutoModelClass.from_pretrained.kwargs",description:`<strong>kwargs</strong> (additional keyword arguments, <em>optional</em>) &#x2014;
Can be used to update the configuration object (after it being loaded) and initiate the model (e.g.,
<code>output_attentions=True</code>). Behaves differently depending on whether a <code>config</code> is provided or
automatically loaded:</p>
<ul>
<li>If a configuration is provided with <code>config</code>, <code>**kwargs</code> will be directly passed to the
underlying model&#x2019;s <code>__init__</code> method (we assume all relevant updates to the configuration have
already been done)</li>
<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_15839/en/main_classes/configuration#transformers.PretrainedConfig.from_pretrained">from_pretrained()</a>). Each key of <code>kwargs</code> that
corresponds to a configuration attribute will be used to override said attribute with the
supplied <code>kwargs</code> value. 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"}]}}),T0=new w({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(
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<span class="hljs-meta">&gt;&gt;&gt; </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>)`}}),F0=new X({}),C0=new M({props:{name:"class transformers.FlaxAutoModelForNextSentencePrediction",anchor:"transformers.FlaxAutoModelForNextSentencePrediction",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/pr_15839/src/transformers/models/auto/modeling_flax_auto.py#L306"}}),E0=new M({props:{name:"from_config",anchor:"transformers.models.auto.auto_factory._BaseAutoModelClass.from_config",parameters:[{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/pr_15839/src/transformers/models/auto/auto_factory.py#L390",parametersDescription:[{anchor:"transformers.models.auto.auto_factory._BaseAutoModelClass.from_config.config",description:`<strong>config</strong> (<a href="/docs/transformers/pr_15839/en/main_classes/configuration#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_15839/en/model_doc/bert#transformers.BertConfig">BertConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/bert#transformers.FlaxBertForNextSentencePrediction">FlaxBertForNextSentencePrediction</a> (BERT model)</li>
</ul>`,name:"config"}]}}),y0=new w({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)`}}),w0=new M({props:{name:"from_pretrained",anchor:"transformers.models.auto.auto_factory._BaseAutoModelClass.from_pretrained",parameters:[{name:"*model_args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/pr_15839/src/transformers/models/auto/auto_factory.py#L418",parametersDescription:[{anchor:"transformers.models.auto.auto_factory._BaseAutoModelClass.from_pretrained.pretrained_model_name_or_path",description:`<strong>pretrained_model_name_or_path</strong> (<code>str</code> or <code>os.PathLike</code>) &#x2014;
Can be either:</p>
<ul>
<li>A string, the <em>model id</em> of a pretrained model hosted inside a model repo on huggingface.co.
Valid model ids can be located at the root-level, like <code>bert-base-uncased</code>, or namespaced under a
user or organization name, like <code>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_15839/en/main_classes/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.models.auto.auto_factory._BaseAutoModelClass.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_15839/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and is reloaded by supplying the
save directory.</li>
<li>The model is loaded by supplying a local directory 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.models.auto.auto_factory._BaseAutoModelClass.from_pretrained.cache_dir",description:`<strong>cache_dir</strong> (<code>str</code> or <code>os.PathLike</code>, <em>optional</em>) &#x2014;
Path to a directory in which a downloaded pretrained model configuration should be cached if the
standard cache should not be used.`,name:"cache_dir"},{anchor:"transformers.models.auto.auto_factory._BaseAutoModelClass.from_pretrained.from_pt",description:`<strong>from_pt</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Load the model weights from a PyTorch checkpoint save file (see docstring of
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Whether or not to force the (re-)download of the model weights and configuration files, overriding the
cached versions if they exist.`,name:"force_download"},{anchor:"transformers.models.auto.auto_factory._BaseAutoModelClass.from_pretrained.resume_download",description:`<strong>resume_download</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to delete incompletely received files. Will attempt to resume the download if such a
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Whether or not to only look at local files (e.g., not try downloading the model).`,name:"local_files_only(bool,"},{anchor:"transformers.models.auto.auto_factory._BaseAutoModelClass.from_pretrained.revision(str,",description:`<strong>revision(<code>str</code>,</strong> <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a
git-based system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any
identifier allowed by git.`,name:"revision(str,"},{anchor:"transformers.models.auto.auto_factory._BaseAutoModelClass.from_pretrained.trust_remote_code",description:`<strong>trust_remote_code</strong> (<code>bool</code>, <em>optional</em>, defaults 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.models.auto.auto_factory._BaseAutoModelClass.from_pretrained.kwargs",description:`<strong>kwargs</strong> (additional keyword arguments, <em>optional</em>) &#x2014;
Can be used to update the configuration object (after it being loaded) and initiate the model (e.g.,
<code>output_attentions=True</code>). Behaves differently depending on whether a <code>config</code> is provided or
automatically loaded:</p>
<ul>
<li>If a configuration is provided with <code>config</code>, <code>**kwargs</code> will be directly passed to the
underlying model&#x2019;s <code>__init__</code> method (we assume all relevant updates to the configuration have
already been done)</li>
<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_15839/en/main_classes/configuration#transformers.PretrainedConfig.from_pretrained">from_pretrained()</a>). Each key of <code>kwargs</code> that
corresponds to a configuration attribute will be used to override said attribute with the
supplied <code>kwargs</code> value. 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"}]}}),A0=new w({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>)`}}),L0=new X({}),B0=new M({props:{name:"class transformers.FlaxAutoModelForImageClassification",anchor:"transformers.FlaxAutoModelForImageClassification",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/pr_15839/src/transformers/models/auto/modeling_flax_auto.py#L315"}}),k0=new M({props:{name:"from_config",anchor:"transformers.models.auto.auto_factory._BaseAutoModelClass.from_config",parameters:[{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/pr_15839/src/transformers/models/auto/auto_factory.py#L390",parametersDescription:[{anchor:"transformers.models.auto.auto_factory._BaseAutoModelClass.from_config.config",description:`<strong>config</strong> (<a href="/docs/transformers/pr_15839/en/main_classes/configuration#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_15839/en/model_doc/beit#transformers.BeitConfig">BeitConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/beit#transformers.FlaxBeitForImageClassification">FlaxBeitForImageClassification</a> (BEiT model)</li>
<li><a href="/docs/transformers/pr_15839/en/model_doc/vit#transformers.ViTConfig">ViTConfig</a> configuration class: <a href="/docs/transformers/pr_15839/en/model_doc/vit#transformers.FlaxViTForImageClassification">FlaxViTForImageClassification</a> (ViT model)</li>
</ul>`,name:"config"}]}}),R0=new w({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)`}}),S0=new M({props:{name:"from_pretrained",anchor:"transformers.models.auto.auto_factory._BaseAutoModelClass.from_pretrained",parameters:[{name:"*model_args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/pr_15839/src/transformers/models/auto/auto_factory.py#L418",parametersDescription:[{anchor:"transformers.models.auto.auto_factory._BaseAutoModelClass.from_pretrained.pretrained_model_name_or_path",description:`<strong>pretrained_model_name_or_path</strong> (<code>str</code> or <code>os.PathLike</code>) &#x2014;
Can be either:</p>
<ul>
<li>A string, the <em>model id</em> of a pretrained model hosted inside a model repo on huggingface.co.
Valid model ids can be located at the root-level, like <code>bert-base-uncased</code>, or namespaced under a
user or organization name, like <code>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_15839/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a>, e.g., <code>./my_model_directory/</code>.</li>
<li>A path or url to a <em>PyTorch state_dict save file</em> (e.g, <code>./pt_model/pytorch_model.bin</code>). In this
case, <code>from_pt</code> should be set to <code>True</code> and a configuration object should be provided as <code>config</code>
argument. This loading path is slower than converting the PyTorch model in a TensorFlow model
using the provided conversion scripts and loading the TensorFlow model afterwards.</li>
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# Update configuration during loading
model = FlaxAutoModelForImageClassification.from_pretrained("bert-base-cased", output_attentions=True)
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</ul>`,name:"config"}]}}),q0=new w({props:{code:`from transformers import AutoConfig, FlaxAutoModelForVision2Seq

# Download configuration from huggingface.co and cache.
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<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Download configuration from 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>)
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<li>A path to a <em>directory</em> containing model weights saved using
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# 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")
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<span class="hljs-meta">&gt;&gt;&gt; </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(
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library when created with the `),Hk=a("a"),tZe=o("AutoFeatureExtractor.from_pretrained()"),aZe=o(" class method."),nZe=l(),_4=a("p"),sZe=o("This class cannot be instantiated directly using "),WJ=a("code"),lZe=o("__init__()"),iZe=o(" (throws an error)."),dZe=l(),$e=a("div"),f(u4.$$.fragment),cZe=l(),QJ=a("p"),fZe=o("Instantiate one of the feature extractor classes of the library from a pretrained model vocabulary."),mZe=l(),Xa=a("p"),gZe=o("The feature extractor class to instantiate is selected based on the "),HJ=a("code"),hZe=o("model_type"),pZe=o(` property of the config object
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missing, by falling back to using pattern matching on `),JJ=a("code"),bZe=o("pretrained_model_name_or_path"),vZe=o(":"),TZe=l(),ae=a("ul"),ih=a("li"),YJ=a("strong"),FZe=o("beit"),CZe=o(" \u2014 "),Uk=a("a"),MZe=o("BeitFeatureExtractor"),EZe=o(" (BEiT model)"),yZe=l(),dh=a("li"),KJ=a("strong"),wZe=o("clip"),AZe=o(" \u2014 "),Jk=a("a"),LZe=o("CLIPFeatureExtractor"),BZe=o(" (CLIP model)"),xZe=l(),ch=a("li"),ZJ=a("strong"),kZe=o("convnext"),RZe=o(" \u2014 "),Yk=a("a"),SZe=o("ConvNextFeatureExtractor"),PZe=o(" (ConvNext model)"),$Ze=l(),fh=a("li"),eY=a("strong"),IZe=o("deit"),jZe=o(" \u2014 "),Kk=a("a"),DZe=o("DeiTFeatureExtractor"),NZe=o(" (DeiT model)"),qZe=l(),mh=a("li"),oY=a("strong"),OZe=o("detr"),GZe=o(" \u2014 "),Zk=a("a"),XZe=o("DetrFeatureExtractor"),VZe=o(" (DETR model)"),zZe=l(),gh=a("li"),rY=a("strong"),WZe=o("hubert"),QZe=o(" \u2014 "),eR=a("a"),HZe=o("Wav2Vec2FeatureExtractor"),UZe=o(" (Hubert model)"),JZe=l(),hh=a("li"),tY=a("strong"),YZe=o("layoutlmv2"),KZe=o(" \u2014 "),oR=a("a"),ZZe=o("LayoutLMv2FeatureExtractor"),eeo=o(" (LayoutLMv2 model)"),oeo=l(),ph=a("li"),aY=a("strong"),reo=o("maskformer"),teo=o(" \u2014 "),rR=a("a"),aeo=o("MaskFormerFeatureExtractor"),neo=o(" (MaskFormer model)"),seo=l(),_h=a("li"),nY=a("strong"),leo=o("perceiver"),ieo=o(" \u2014 "),tR=a("a"),deo=o("PerceiverFeatureExtractor"),ceo=o(" (Perceiver model)"),feo=l(),uh=a("li"),sY=a("strong"),meo=o("poolformer"),geo=o(" \u2014 "),aR=a("a"),heo=o("PoolFormerFeatureExtractor"),peo=o(" (PoolFormer model)"),_eo=l(),bh=a("li"),lY=a("strong"),ueo=o("segformer"),beo=o(" \u2014 "),nR=a("a"),veo=o("SegformerFeatureExtractor"),Teo=o(" (SegFormer model)"),Feo=l(),vh=a("li"),iY=a("strong"),Ceo=o("speech_to_text"),Meo=o(" \u2014 "),sR=a("a"),Eeo=o("Speech2TextFeatureExtractor"),yeo=o(" (Speech2Text model)"),weo=l(),Th=a("li"),dY=a("strong"),Aeo=o("swin"),Leo=o(" \u2014 "),lR=a("a"),Beo=o("ViTFeatureExtractor"),xeo=o(" (Swin model)"),keo=l(),Fh=a("li"),cY=a("strong"),Reo=o("vit"),Seo=o(" \u2014 "),iR=a("a"),Peo=o("ViTFeatureExtractor"),$eo=o(" (ViT model)"),Ieo=l(),Ch=a("li"),fY=a("strong"),jeo=o("vit_mae"),Deo=o(" \u2014 "),dR=a("a"),Neo=o("ViTFeatureExtractor"),qeo=o(" (ViTMAE model)"),Oeo=l(),Mh=a("li"),mY=a("strong"),Geo=o("wav2vec2"),Xeo=o(" \u2014 "),cR=a("a"),Veo=o("Wav2Vec2FeatureExtractor"),zeo=o(" (Wav2Vec2 model)"),Weo=l(),f(Eh.$$.fragment),Qeo=l(),gY=a("p"),Heo=o("Examples:"),Ueo=l(),f(b4.$$.fragment),Jeo=l(),yh=a("div"),f(v4.$$.fragment),Yeo=l(),hY=a("p"),Keo=o("Register a new feature extractor for this class."),uxe=l(),Ui=a("h2"),wh=a("a"),pY=a("span"),f(T4.$$.fragment),Zeo=l(),_Y=a("span"),eoo=o("AutoProcessor"),bxe=l(),Uo=a("div"),f(F4.$$.fragment),ooo=l(),C4=a("p"),roo=o(`This is a generic processor class that will be instantiated as one of the processor classes of the library when
created with the `),fR=a("a"),too=o("AutoProcessor.from_pretrained()"),aoo=o(" class method."),noo=l(),M4=a("p"),soo=o("This class cannot be instantiated directly using "),uY=a("code"),loo=o("__init__()"),ioo=o(" (throws an error)."),doo=l(),Ie=a("div"),f(E4.$$.fragment),coo=l(),bY=a("p"),foo=o("Instantiate one of the processor classes of the library from a pretrained model vocabulary."),moo=l(),Ji=a("p"),goo=o("The processor class to instantiate is selected based on the "),vY=a("code"),hoo=o("model_type"),poo=o(` property of the config object (either
passed as an argument or loaded from `),TY=a("code"),_oo=o("pretrained_model_name_or_path"),uoo=o(" if possible):"),boo=l(),Be=a("ul"),Ah=a("li"),FY=a("strong"),voo=o("clip"),Too=o(" \u2014 "),mR=a("a"),Foo=o("CLIPProcessor"),Coo=o(" (CLIP model)"),Moo=l(),Lh=a("li"),CY=a("strong"),Eoo=o("layoutlmv2"),yoo=o(" \u2014 "),gR=a("a"),woo=o("LayoutLMv2Processor"),Aoo=o(" (LayoutLMv2 model)"),Loo=l(),Bh=a("li"),MY=a("strong"),Boo=o("layoutxlm"),xoo=o(" \u2014 "),hR=a("a"),koo=o("LayoutXLMProcessor"),Roo=o(" (LayoutXLM model)"),Soo=l(),xh=a("li"),EY=a("strong"),Poo=o("speech_to_text"),$oo=o(" \u2014 "),pR=a("a"),Ioo=o("Speech2TextProcessor"),joo=o(" (Speech2Text model)"),Doo=l(),kh=a("li"),yY=a("strong"),Noo=o("speech_to_text_2"),qoo=o(" \u2014 "),_R=a("a"),Ooo=o("Speech2Text2Processor"),Goo=o(" (Speech2Text2 model)"),Xoo=l(),Rh=a("li"),wY=a("strong"),Voo=o("trocr"),zoo=o(" \u2014 "),uR=a("a"),Woo=o("TrOCRProcessor"),Qoo=o(" (TrOCR model)"),Hoo=l(),Sh=a("li"),AY=a("strong"),Uoo=o("vision-text-dual-encoder"),Joo=o(" \u2014 "),bR=a("a"),Yoo=o("VisionTextDualEncoderProcessor"),Koo=o(" (VisionTextDualEncoder model)"),Zoo=l(),Ph=a("li"),LY=a("strong"),ero=o("wav2vec2"),oro=o(" \u2014 "),vR=a("a"),rro=o("Wav2Vec2Processor"),tro=o(" (Wav2Vec2 model)"),aro=l(),f($h.$$.fragment),nro=l(),BY=a("p"),sro=o("Examples:"),lro=l(),f(y4.$$.fragment),iro=l(),Ih=a("div"),f(w4.$$.fragment),dro=l(),xY=a("p"),cro=o("Register a new processor for this class."),vxe=l(),Yi=a("h2"),jh=a("a"),kY=a("span"),f(A4.$$.fragment),fro=l(),RY=a("span"),mro=o("AutoModel"),Txe=l(),Jo=a("div"),f(L4.$$.fragment),gro=l(),Ki=a("p"),hro=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 `),SY=a("code"),pro=o("from_pretrained()"),_ro=o("class method or the "),PY=a("code"),uro=o("from_config()"),bro=o(`class
method.`),vro=l(),B4=a("p"),Tro=o("This class cannot be instantiated directly using "),$Y=a("code"),Fro=o("__init__()"),Cro=o(" (throws an error)."),Mro=l(),Vr=a("div"),f(x4.$$.fragment),Ero=l(),IY=a("p"),yro=o("Instantiates one of the base model classes of the library from a configuration."),wro=l(),Zi=a("p"),Aro=o(`Note:
Loading a model from its configuration file does `),jY=a("strong"),Lro=o("not"),Bro=o(` load the model weights. It only affects the
model\u2019s configuration. Use `),DY=a("code"),xro=o("from_pretrained()"),kro=o("to load the model weights."),Rro=l(),NY=a("p"),Sro=o("Examples:"),Pro=l(),f(k4.$$.fragment),$ro=l(),je=a("div"),f(R4.$$.fragment),Iro=l(),qY=a("p"),jro=o("Instantiate one of the base model classes of the library from a pretrained model."),Dro=l(),Va=a("p"),Nro=o("The model class to instantiate is selected based on the "),OY=a("code"),qro=o("model_type"),Oro=o(` property of the config object (either
passed as an argument or loaded from `),GY=a("code"),Gro=o("pretrained_model_name_or_path"),Xro=o(` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),XY=a("code"),Vro=o("pretrained_model_name_or_path"),zro=o(":"),Wro=l(),F=a("ul"),Dh=a("li"),VY=a("strong"),Qro=o("albert"),Hro=o(" \u2014 "),TR=a("a"),Uro=o("AlbertModel"),Jro=o(" (ALBERT model)"),Yro=l(),Nh=a("li"),zY=a("strong"),Kro=o("bart"),Zro=o(" \u2014 "),FR=a("a"),eto=o("BartModel"),oto=o(" (BART model)"),rto=l(),qh=a("li"),WY=a("strong"),tto=o("beit"),ato=o(" \u2014 "),CR=a("a"),nto=o("BeitModel"),sto=o(" (BEiT model)"),lto=l(),Oh=a("li"),QY=a("strong"),ito=o("bert"),dto=o(" \u2014 "),MR=a("a"),cto=o("BertModel"),fto=o(" (BERT model)"),mto=l(),Gh=a("li"),HY=a("strong"),gto=o("bert-generation"),hto=o(" \u2014 "),ER=a("a"),pto=o("BertGenerationEncoder"),_to=o(" (Bert Generation model)"),uto=l(),Xh=a("li"),UY=a("strong"),bto=o("big_bird"),vto=o(" \u2014 "),yR=a("a"),Tto=o("BigBirdModel"),Fto=o(" (BigBird model)"),Cto=l(),Vh=a("li"),JY=a("strong"),Mto=o("bigbird_pegasus"),Eto=o(" \u2014 "),wR=a("a"),yto=o("BigBirdPegasusModel"),wto=o(" (BigBirdPegasus model)"),Ato=l(),zh=a("li"),YY=a("strong"),Lto=o("blenderbot"),Bto=o(" \u2014 "),AR=a("a"),xto=o("BlenderbotModel"),kto=o(" (Blenderbot model)"),Rto=l(),Wh=a("li"),KY=a("strong"),Sto=o("blenderbot-small"),Pto=o(" \u2014 "),LR=a("a"),$to=o("BlenderbotSmallModel"),Ito=o(" (BlenderbotSmall model)"),jto=l(),Qh=a("li"),ZY=a("strong"),Dto=o("camembert"),Nto=o(" \u2014 "),BR=a("a"),qto=o("CamembertModel"),Oto=o(" (CamemBERT model)"),Gto=l(),Hh=a("li"),eK=a("strong"),Xto=o("canine"),Vto=o(" \u2014 "),xR=a("a"),zto=o("CanineModel"),Wto=o(" (Canine model)"),Qto=l(),Uh=a("li"),oK=a("strong"),Hto=o("clip"),Uto=o(" \u2014 "),kR=a("a"),Jto=o("CLIPModel"),Yto=o(" (CLIP model)"),Kto=l(),Jh=a("li"),rK=a("strong"),Zto=o("convbert"),eao=o(" \u2014 "),RR=a("a"),oao=o("ConvBertModel"),rao=o(" (ConvBERT model)"),tao=l(),Yh=a("li"),tK=a("strong"),aao=o("convnext"),nao=o(" \u2014 "),SR=a("a"),sao=o("ConvNextModel"),lao=o(" (ConvNext 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"),OR=a("a"),Wao=o("DistilBertModel"),Qao=o(" (DistilBERT model)"),Hao=l(),sp=a("li"),mK=a("strong"),Uao=o("dpr"),Jao=o(" \u2014 "),GR=a("a"),Yao=o("DPRQuestionEncoder"),Kao=o(" (DPR model)"),Zao=l(),lp=a("li"),gK=a("strong"),eno=o("electra"),ono=o(" \u2014 "),XR=a("a"),rno=o("ElectraModel"),tno=o(" (ELECTRA model)"),ano=l(),ip=a("li"),hK=a("strong"),nno=o("flaubert"),sno=o(" \u2014 "),VR=a("a"),lno=o("FlaubertModel"),ino=o(" (FlauBERT model)"),dno=l(),dp=a("li"),pK=a("strong"),cno=o("fnet"),fno=o(" \u2014 "),zR=a("a"),mno=o("FNetModel"),gno=o(" (FNet model)"),hno=l(),cp=a("li"),_K=a("strong"),pno=o("fsmt"),_no=o(" \u2014 "),WR=a("a"),uno=o("FSMTModel"),bno=o(" (FairSeq Machine-Translation model)"),vno=l(),Ns=a("li"),uK=a("strong"),Tno=o("funnel"),Fno=o(" \u2014 "),QR=a("a"),Cno=o("FunnelModel"),Mno=o(" or "),HR=a("a"),Eno=o("FunnelBaseModel"),yno=o(" (Funnel Transformer model)"),wno=l(),fp=a("li"),bK=a("strong"),Ano=o("gpt2"),Lno=o(" \u2014 "),UR=a("a"),Bno=o("GPT2Model"),xno=o(" (OpenAI GPT-2 model)"),kno=l(),mp=a("li"),vK=a("strong"),Rno=o("gpt_neo"),Sno=o(" \u2014 "),JR=a("a"),Pno=o("GPTNeoModel"),$no=o(" (GPT Neo model)"),Ino=l(),gp=a("li"),TK=a("strong"),jno=o("gptj"),Dno=o(" \u2014 "),YR=a("a"),Nno=o("GPTJModel"),qno=o(" (GPT-J model)"),Ono=l(),hp=a("li"),FK=a("strong"),Gno=o("hubert"),Xno=o(" \u2014 "),KR=a("a"),Vno=o("HubertModel"),zno=o(" (Hubert model)"),Wno=l(),pp=a("li"),CK=a("strong"),Qno=o("ibert"),Hno=o(" \u2014 "),ZR=a("a"),Uno=o("IBertModel"),Jno=o(" (I-BERT model)"),Yno=l(),_p=a("li"),MK=a("strong"),Kno=o("imagegpt"),Zno=o(" \u2014 "),eS=a("a"),eso=o("ImageGPTModel"),oso=o(" (ImageGPT model)"),rso=l(),up=a("li"),EK=a("strong"),tso=o("layoutlm"),aso=o(" \u2014 "),oS=a("a"),nso=o("LayoutLMModel"),sso=o(" (LayoutLM model)"),lso=l(),bp=a("li"),yK=a("strong"),iso=o("layoutlmv2"),dso=o(" \u2014 "),rS=a("a"),cso=o("LayoutLMv2Model"),fso=o(" (LayoutLMv2 model)"),mso=l(),vp=a("li"),wK=a("strong"),gso=o("led"),hso=o(" \u2014 "),tS=a("a"),pso=o("LEDModel"),_so=o(" (LED model)"),uso=l(),Tp=a("li"),AK=a("strong"),bso=o("longformer"),vso=o(" \u2014 "),aS=a("a"),Tso=o("LongformerModel"),Fso=o(" (Longformer model)"),Cso=l(),Fp=a("li"),LK=a("strong"),Mso=o("luke"),Eso=o(" \u2014 "),nS=a("a"),yso=o("LukeModel"),wso=o(" (LUKE model)"),Aso=l(),Cp=a("li"),BK=a("strong"),Lso=o("lxmert"),Bso=o(" \u2014 "),sS=a("a"),xso=o("LxmertModel"),kso=o(" (LXMERT model)"),Rso=l(),Mp=a("li"),xK=a("strong"),Sso=o("m2m_100"),Pso=o(" \u2014 "),lS=a("a"),$so=o("M2M100Model"),Iso=o(" (M2M100 model)"),jso=l(),Ep=a("li"),kK=a("strong"),Dso=o("marian"),Nso=o(" \u2014 "),iS=a("a"),qso=o("MarianModel"),Oso=o(" (Marian model)"),Gso=l(),yp=a("li"),RK=a("strong"),Xso=o("maskformer"),Vso=o(" \u2014 "),dS=a("a"),zso=o("MaskFormerModel"),Wso=o(" (MaskFormer model)"),Qso=l(),wp=a("li"),SK=a("strong"),Hso=o("mbart"),Uso=o(" \u2014 "),cS=a("a"),Jso=o("MBartModel"),Yso=o(" (mBART 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model)"),vio=l(),Gp=a("li"),UK=a("strong"),Tio=o("roberta"),Fio=o(" \u2014 "),wS=a("a"),Cio=o("RobertaModel"),Mio=o(" (RoBERTa model)"),Eio=l(),Xp=a("li"),JK=a("strong"),yio=o("roformer"),wio=o(" \u2014 "),AS=a("a"),Aio=o("RoFormerModel"),Lio=o(" (RoFormer model)"),Bio=l(),Vp=a("li"),YK=a("strong"),xio=o("segformer"),kio=o(" \u2014 "),LS=a("a"),Rio=o("SegformerModel"),Sio=o(" (SegFormer model)"),Pio=l(),zp=a("li"),KK=a("strong"),$io=o("sew"),Iio=o(" \u2014 "),BS=a("a"),jio=o("SEWModel"),Dio=o(" (SEW model)"),Nio=l(),Wp=a("li"),ZK=a("strong"),qio=o("sew-d"),Oio=o(" \u2014 "),xS=a("a"),Gio=o("SEWDModel"),Xio=o(" (SEW-D model)"),Vio=l(),Qp=a("li"),eZ=a("strong"),zio=o("speech_to_text"),Wio=o(" \u2014 "),kS=a("a"),Qio=o("Speech2TextModel"),Hio=o(" (Speech2Text model)"),Uio=l(),Hp=a("li"),oZ=a("strong"),Jio=o("splinter"),Yio=o(" \u2014 "),RS=a("a"),Kio=o("SplinterModel"),Zio=o(" (Splinter model)"),edo=l(),Up=a("li"),rZ=a("strong"),odo=o("squeezebert"),rdo=o(" \u2014 "),SS=a("a"),tdo=o("SqueezeBertModel"),ado=o(" (SqueezeBERT model)"),ndo=l(),Jp=a("li"),tZ=a("strong"),sdo=o("swin"),ldo=o(" \u2014 "),PS=a("a"),ido=o("SwinModel"),ddo=o(" (Swin model)"),cdo=l(),Yp=a("li"),aZ=a("strong"),fdo=o("t5"),mdo=o(" \u2014 "),$S=a("a"),gdo=o("T5Model"),hdo=o(" (T5 model)"),pdo=l(),Kp=a("li"),nZ=a("strong"),_do=o("tapas"),udo=o(" \u2014 "),IS=a("a"),bdo=o("TapasModel"),vdo=o(" (TAPAS model)"),Tdo=l(),Zp=a("li"),sZ=a("strong"),Fdo=o("transfo-xl"),Cdo=o(" \u2014 "),jS=a("a"),Mdo=o("TransfoXLModel"),Edo=o(" (Transformer-XL model)"),ydo=l(),e_=a("li"),lZ=a("strong"),wdo=o("unispeech"),Ado=o(" \u2014 "),DS=a("a"),Ldo=o("UniSpeechModel"),Bdo=o(" (UniSpeech model)"),xdo=l(),o_=a("li"),iZ=a("strong"),kdo=o("unispeech-sat"),Rdo=o(" \u2014 "),NS=a("a"),Sdo=o("UniSpeechSatModel"),Pdo=o(" (UniSpeechSat model)"),$do=l(),r_=a("li"),dZ=a("strong"),Ido=o("vilt"),jdo=o(" \u2014 "),qS=a("a"),Ddo=o("ViltModel"),Ndo=o(" (ViLT model)"),qdo=l(),t_=a("li"),cZ=a("strong"),Odo=o("vision-text-dual-encoder"),Gdo=o(" \u2014 "),OS=a("a"),Xdo=o("VisionTextDualEncoderModel"),Vdo=o(" (VisionTextDualEncoder model)"),zdo=l(),a_=a("li"),fZ=a("strong"),Wdo=o("visual_bert"),Qdo=o(" \u2014 "),GS=a("a"),Hdo=o("VisualBertModel"),Udo=o(" (VisualBert model)"),Jdo=l(),n_=a("li"),mZ=a("strong"),Ydo=o("vit"),Kdo=o(" \u2014 "),XS=a("a"),Zdo=o("ViTModel"),eco=o(" (ViT model)"),oco=l(),s_=a("li"),gZ=a("strong"),rco=o("vit_mae"),tco=o(" \u2014 "),VS=a("a"),aco=o("ViTMAEModel"),nco=o(" (ViTMAE model)"),sco=l(),l_=a("li"),hZ=a("strong"),lco=o("wav2vec2"),ico=o(" \u2014 "),zS=a("a"),dco=o("Wav2Vec2Model"),cco=o(" (Wav2Vec2 model)"),fco=l(),i_=a("li"),pZ=a("strong"),mco=o("wavlm"),gco=o(" \u2014 "),WS=a("a"),hco=o("WavLMModel"),pco=o(" (WavLM model)"),_co=l(),d_=a("li"),_Z=a("strong"),uco=o("xglm"),bco=o(" \u2014 "),QS=a("a"),vco=o("XGLMModel"),Tco=o(" (XGLM model)"),Fco=l(),c_=a("li"),uZ=a("strong"),Cco=o("xlm"),Mco=o(" \u2014 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deactivated). To train the model, you should first set it back in training mode with `),EZ=a("code"),ofo=o("model.train()"),rfo=l(),yZ=a("p"),tfo=o("Examples:"),afo=l(),f(S4.$$.fragment),Fxe=l(),ed=a("h2"),u_=a("a"),wZ=a("span"),f(P4.$$.fragment),nfo=l(),AZ=a("span"),sfo=o("AutoModelForPreTraining"),Cxe=l(),Yo=a("div"),f($4.$$.fragment),lfo=l(),od=a("p"),ifo=o(`This is a generic model class that will be instantiated as one of the model classes of the library (with a pretraining head) when created
with the `),LZ=a("code"),dfo=o("from_pretrained()"),cfo=o("class method or the "),BZ=a("code"),ffo=o("from_config()"),mfo=o(`class
method.`),gfo=l(),I4=a("p"),hfo=o("This class cannot be instantiated directly using "),xZ=a("code"),pfo=o("__init__()"),_fo=o(" (throws an error)."),ufo=l(),zr=a("div"),f(j4.$$.fragment),bfo=l(),kZ=a("p"),vfo=o("Instantiates one of the model classes of the library (with a pretraining head) from a configuration."),Tfo=l(),rd=a("p"),Ffo=o(`Note:
Loading a model from its configuration file does `),RZ=a("strong"),Cfo=o("not"),Mfo=o(` load the model weights. It only affects the
model\u2019s configuration. Use `),SZ=a("code"),Efo=o("from_pretrained()"),yfo=o("to load the model weights."),wfo=l(),PZ=a("p"),Afo=o("Examples:"),Lfo=l(),f(D4.$$.fragment),Bfo=l(),De=a("div"),f(N4.$$.fragment),xfo=l(),$Z=a("p"),kfo=o("Instantiate one of the model classes of the library (with a pretraining head) from a pretrained model."),Rfo=l(),za=a("p"),Sfo=o("The model class to instantiate is selected based on the "),IZ=a("code"),Pfo=o("model_type"),$fo=o(` property of the config object (either
passed as an argument or loaded from `),jZ=a("code"),Ifo=o("pretrained_model_name_or_path"),jfo=o(` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),DZ=a("code"),Dfo=o("pretrained_model_name_or_path"),Nfo=o(":"),qfo=l(),k=a("ul"),b_=a("li"),NZ=a("strong"),Ofo=o("albert"),Gfo=o(" \u2014 "),eP=a("a"),Xfo=o("AlbertForPreTraining"),Vfo=o(" (ALBERT model)"),zfo=l(),v_=a("li"),qZ=a("strong"),Wfo=o("bart"),Qfo=o(" \u2014 "),oP=a("a"),Hfo=o("BartForConditionalGeneration"),Ufo=o(" (BART model)"),Jfo=l(),T_=a("li"),OZ=a("strong"),Yfo=o("bert"),Kfo=o(" \u2014 "),rP=a("a"),Zfo=o("BertForPreTraining"),emo=o(" (BERT model)"),omo=l(),F_=a("li"),GZ=a("strong"),rmo=o("big_bird"),tmo=o(" \u2014 "),tP=a("a"),amo=o("BigBirdForPreTraining"),nmo=o(" (BigBird model)"),smo=l(),C_=a("li"),XZ=a("strong"),lmo=o("camembert"),imo=o(" \u2014 "),aP=a("a"),dmo=o("CamembertForMaskedLM"),cmo=o(" (CamemBERT model)"),fmo=l(),M_=a("li"),VZ=a("strong"),mmo=o("ctrl"),gmo=o(" \u2014 "),nP=a("a"),hmo=o("CTRLLMHeadModel"),pmo=o(" (CTRL model)"),_mo=l(),E_=a("li"),zZ=a("strong"),umo=o("data2vec-text"),bmo=o(" \u2014 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deactivated). To train the model, you should first set it back in training mode with `),yee=a("code"),Mpo=o("model.train()"),Epo=l(),wee=a("p"),ypo=o("Examples:"),wpo=l(),f(q4.$$.fragment),Mxe=l(),td=a("h2"),au=a("a"),Aee=a("span"),f(O4.$$.fragment),Apo=l(),Lee=a("span"),Lpo=o("AutoModelForCausalLM"),Exe=l(),Ko=a("div"),f(G4.$$.fragment),Bpo=l(),ad=a("p"),xpo=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 `),Bee=a("code"),kpo=o("from_pretrained()"),Rpo=o("class method or the "),xee=a("code"),Spo=o("from_config()"),Ppo=o(`class
method.`),$po=l(),X4=a("p"),Ipo=o("This class cannot be instantiated directly using "),kee=a("code"),jpo=o("__init__()"),Dpo=o(" (throws an error)."),Npo=l(),Wr=a("div"),f(V4.$$.fragment),qpo=l(),Ree=a("p"),Opo=o("Instantiates one of the model classes of the library (with a causal language modeling head) from a configuration."),Gpo=l(),nd=a("p"),Xpo=o(`Note:
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model\u2019s configuration. Use `),Pee=a("code"),Wpo=o("from_pretrained()"),Qpo=o("to load the model weights."),Hpo=l(),$ee=a("p"),Upo=o("Examples:"),Jpo=l(),f(z4.$$.fragment),Ypo=l(),Ne=a("div"),f(W4.$$.fragment),Kpo=l(),Iee=a("p"),Zpo=o("Instantiate one of the model classes of the library (with a causal language modeling head) from a pretrained model."),e_o=l(),Wa=a("p"),o_o=o("The model class to instantiate is selected based on the "),jee=a("code"),r_o=o("model_type"),t_o=o(` property of the config object (either
passed as an argument or loaded from `),Dee=a("code"),a_o=o("pretrained_model_name_or_path"),n_o=o(` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),Nee=a("code"),s_o=o("pretrained_model_name_or_path"),l_o=o(":"),i_o=l(),$=a("ul"),nu=a("li"),qee=a("strong"),d_o=o("bart"),c_o=o(" \u2014 "),NP=a("a"),f_o=o("BartForCausalLM"),m_o=o(" (BART model)"),g_o=l(),su=a("li"),Oee=a("strong"),h_o=o("bert"),p_o=o(" \u2014 "),qP=a("a"),__o=o("BertLMHeadModel"),u_o=o(" (BERT model)"),b_o=l(),lu=a("li"),Gee=a("strong"),v_o=o("bert-generation"),T_o=o(" \u2014 "),OP=a("a"),F_o=o("BertGenerationDecoder"),C_o=o(" (Bert Generation model)"),M_o=l(),iu=a("li"),Xee=a("strong"),E_o=o("big_bird"),y_o=o(" \u2014 "),GP=a("a"),w_o=o("BigBirdForCausalLM"),A_o=o(" (BigBird model)"),L_o=l(),du=a("li"),Vee=a("strong"),B_o=o("bigbird_pegasus"),x_o=o(" \u2014 "),XP=a("a"),k_o=o("BigBirdPegasusForCausalLM"),R_o=o(" (BigBirdPegasus model)"),S_o=l(),cu=a("li"),zee=a("strong"),P_o=o("blenderbot"),$_o=o(" \u2014 "),VP=a("a"),I_o=o("BlenderbotForCausalLM"),j_o=o(" (Blenderbot 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model)"),vuo=l(),bu=a("li"),Zee=a("strong"),Tuo=o("gptj"),Fuo=o(" \u2014 "),KP=a("a"),Cuo=o("GPTJForCausalLM"),Muo=o(" (GPT-J model)"),Euo=l(),vu=a("li"),eoe=a("strong"),yuo=o("marian"),wuo=o(" \u2014 "),ZP=a("a"),Auo=o("MarianForCausalLM"),Luo=o(" (Marian model)"),Buo=l(),Tu=a("li"),ooe=a("strong"),xuo=o("mbart"),kuo=o(" \u2014 "),e$=a("a"),Ruo=o("MBartForCausalLM"),Suo=o(" (mBART model)"),Puo=l(),Fu=a("li"),roe=a("strong"),$uo=o("megatron-bert"),Iuo=o(" \u2014 "),o$=a("a"),juo=o("MegatronBertForCausalLM"),Duo=o(" (MegatronBert model)"),Nuo=l(),Cu=a("li"),toe=a("strong"),quo=o("openai-gpt"),Ouo=o(" \u2014 "),r$=a("a"),Guo=o("OpenAIGPTLMHeadModel"),Xuo=o(" (OpenAI GPT model)"),Vuo=l(),Mu=a("li"),aoe=a("strong"),zuo=o("pegasus"),Wuo=o(" \u2014 "),t$=a("a"),Quo=o("PegasusForCausalLM"),Huo=o(" (Pegasus model)"),Uuo=l(),Eu=a("li"),noe=a("strong"),Juo=o("plbart"),Yuo=o(" \u2014 "),a$=a("a"),Kuo=o("PLBartForCausalLM"),Zuo=o(" (PLBart 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model)"),_bo=l(),Nu=a("li"),Toe=a("strong"),ubo=o("xlnet"),bbo=o(" \u2014 "),v$=a("a"),vbo=o("XLNetLMHeadModel"),Tbo=o(" (XLNet model)"),Fbo=l(),qu=a("p"),Cbo=o("The model is set in evaluation mode by default using "),Foe=a("code"),Mbo=o("model.eval()"),Ebo=o(` (so for instance, dropout modules are
deactivated). To train the model, you should first set it back in training mode with `),Coe=a("code"),ybo=o("model.train()"),wbo=l(),Moe=a("p"),Abo=o("Examples:"),Lbo=l(),f(Q4.$$.fragment),yxe=l(),sd=a("h2"),Ou=a("a"),Eoe=a("span"),f(H4.$$.fragment),Bbo=l(),yoe=a("span"),xbo=o("AutoModelForMaskedLM"),wxe=l(),Zo=a("div"),f(U4.$$.fragment),kbo=l(),ld=a("p"),Rbo=o(`This is a generic model class that will be instantiated as one of the model classes of the library (with a masked language modeling head) when created
with the `),woe=a("code"),Sbo=o("from_pretrained()"),Pbo=o("class method or the "),Aoe=a("code"),$bo=o("from_config()"),Ibo=o(`class
method.`),jbo=l(),J4=a("p"),Dbo=o("This class cannot be instantiated directly using "),Loe=a("code"),Nbo=o("__init__()"),qbo=o(" (throws an error)."),Obo=l(),Qr=a("div"),f(Y4.$$.fragment),Gbo=l(),Boe=a("p"),Xbo=o("Instantiates one of the model classes of the library (with a masked language modeling head) from a configuration."),Vbo=l(),id=a("p"),zbo=o(`Note:
Loading a model from its configuration file does `),xoe=a("strong"),Wbo=o("not"),Qbo=o(` load the model weights. It only affects the
model\u2019s configuration. Use `),koe=a("code"),Hbo=o("from_pretrained()"),Ubo=o("to load the model weights."),Jbo=l(),Roe=a("p"),Ybo=o("Examples:"),Kbo=l(),f(K4.$$.fragment),Zbo=l(),qe=a("div"),f(Z4.$$.fragment),e3o=l(),Soe=a("p"),o3o=o("Instantiate one of the model classes of the library (with a masked language modeling head) from a pretrained model."),r3o=l(),Qa=a("p"),t3o=o("The model class to instantiate is selected based on the "),Poe=a("code"),a3o=o("model_type"),n3o=o(` property of the config object (either
passed as an argument or loaded from `),$oe=a("code"),s3o=o("pretrained_model_name_or_path"),l3o=o(` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),Ioe=a("code"),i3o=o("pretrained_model_name_or_path"),d3o=o(":"),c3o=l(),I=a("ul"),Gu=a("li"),joe=a("strong"),f3o=o("albert"),m3o=o(" \u2014 "),T$=a("a"),g3o=o("AlbertForMaskedLM"),h3o=o(" (ALBERT model)"),p3o=l(),Xu=a("li"),Doe=a("strong"),_3o=o("bart"),u3o=o(" \u2014 "),F$=a("a"),b3o=o("BartForConditionalGeneration"),v3o=o(" (BART model)"),T3o=l(),Vu=a("li"),Noe=a("strong"),F3o=o("bert"),C3o=o(" \u2014 "),C$=a("a"),M3o=o("BertForMaskedLM"),E3o=o(" (BERT model)"),y3o=l(),zu=a("li"),qoe=a("strong"),w3o=o("big_bird"),A3o=o(" \u2014 "),M$=a("a"),L3o=o("BigBirdForMaskedLM"),B3o=o(" (BigBird model)"),x3o=l(),Wu=a("li"),Ooe=a("strong"),k3o=o("camembert"),R3o=o(" \u2014 "),E$=a("a"),S3o=o("CamembertForMaskedLM"),P3o=o(" (CamemBERT model)"),$3o=l(),Qu=a("li"),Goe=a("strong"),I3o=o("convbert"),j3o=o(" \u2014 "),y$=a("a"),D3o=o("ConvBertForMaskedLM"),N3o=o(" (ConvBERT model)"),q3o=l(),Hu=a("li"),Xoe=a("strong"),O3o=o("data2vec-text"),G3o=o(" \u2014 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"),Z$=a("a"),Fvo=o("YosoForMaskedLM"),Cvo=o(" (YOSO model)"),Mvo=l(),M1=a("p"),Evo=o("The model is set in evaluation mode by default using "),vre=a("code"),yvo=o("model.eval()"),wvo=o(` (so for instance, dropout modules are
deactivated). To train the model, you should first set it back in training mode with `),Tre=a("code"),Avo=o("model.train()"),Lvo=l(),Fre=a("p"),Bvo=o("Examples:"),xvo=l(),f(eE.$$.fragment),Axe=l(),dd=a("h2"),E1=a("a"),Cre=a("span"),f(oE.$$.fragment),kvo=l(),Mre=a("span"),Rvo=o("AutoModelForSeq2SeqLM"),Lxe=l(),er=a("div"),f(rE.$$.fragment),Svo=l(),cd=a("p"),Pvo=o(`This is a generic model class that will be instantiated as one of the model classes of the library (with a sequence-to-sequence language modeling head) when created
with the `),Ere=a("code"),$vo=o("from_pretrained()"),Ivo=o("class method or the "),yre=a("code"),jvo=o("from_config()"),Dvo=o(`class
method.`),Nvo=l(),tE=a("p"),qvo=o("This class cannot be instantiated directly using "),wre=a("code"),Ovo=o("__init__()"),Gvo=o(" (throws an error)."),Xvo=l(),Hr=a("div"),f(aE.$$.fragment),Vvo=l(),Are=a("p"),zvo=o("Instantiates one of the model classes of the library (with a sequence-to-sequence language modeling head) from a configuration."),Wvo=l(),fd=a("p"),Qvo=o(`Note:
Loading a model from its configuration file does `),Lre=a("strong"),Hvo=o("not"),Uvo=o(` load the model weights. It only affects the
model\u2019s configuration. Use `),Bre=a("code"),Jvo=o("from_pretrained()"),Yvo=o("to load the model weights."),Kvo=l(),xre=a("p"),Zvo=o("Examples:"),eTo=l(),f(nE.$$.fragment),oTo=l(),Oe=a("div"),f(sE.$$.fragment),rTo=l(),kre=a("p"),tTo=o("Instantiate one of the model classes of the library (with a sequence-to-sequence language modeling head) from a pretrained model."),aTo=l(),Ha=a("p"),nTo=o("The model class to instantiate is selected based on the "),Rre=a("code"),sTo=o("model_type"),lTo=o(` property of the config object (either
passed as an argument or loaded from `),Sre=a("code"),iTo=o("pretrained_model_name_or_path"),dTo=o(` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),Pre=a("code"),cTo=o("pretrained_model_name_or_path"),fTo=o(":"),mTo=l(),ne=a("ul"),y1=a("li"),$re=a("strong"),gTo=o("bart"),hTo=o(" \u2014 "),eI=a("a"),pTo=o("BartForConditionalGeneration"),_To=o(" (BART model)"),uTo=l(),w1=a("li"),Ire=a("strong"),bTo=o("bigbird_pegasus"),vTo=o(" \u2014 "),oI=a("a"),TTo=o("BigBirdPegasusForConditionalGeneration"),FTo=o(" (BigBirdPegasus model)"),CTo=l(),A1=a("li"),jre=a("strong"),MTo=o("blenderbot"),ETo=o(" \u2014 "),rI=a("a"),yTo=o("BlenderbotForConditionalGeneration"),wTo=o(" (Blenderbot model)"),ATo=l(),L1=a("li"),Dre=a("strong"),LTo=o("blenderbot-small"),BTo=o(" \u2014 "),tI=a("a"),xTo=o("BlenderbotSmallForConditionalGeneration"),kTo=o(" (BlenderbotSmall model)"),RTo=l(),B1=a("li"),Nre=a("strong"),STo=o("encoder-decoder"),PTo=o(" \u2014 "),aI=a("a"),$To=o("EncoderDecoderModel"),ITo=o(" (Encoder decoder model)"),jTo=l(),x1=a("li"),qre=a("strong"),DTo=o("fsmt"),NTo=o(" \u2014 "),nI=a("a"),qTo=o("FSMTForConditionalGeneration"),OTo=o(" (FairSeq Machine-Translation model)"),GTo=l(),k1=a("li"),Ore=a("strong"),XTo=o("led"),VTo=o(" \u2014 "),sI=a("a"),zTo=o("LEDForConditionalGeneration"),WTo=o(" (LED model)"),QTo=l(),R1=a("li"),Gre=a("strong"),HTo=o("m2m_100"),UTo=o(" \u2014 "),lI=a("a"),JTo=o("M2M100ForConditionalGeneration"),YTo=o(" (M2M100 model)"),KTo=l(),S1=a("li"),Xre=a("strong"),ZTo=o("marian"),e8o=o(" \u2014 "),iI=a("a"),o8o=o("MarianMTModel"),r8o=o(" (Marian model)"),t8o=l(),P1=a("li"),Vre=a("strong"),a8o=o("mbart"),n8o=o(" \u2014 "),dI=a("a"),s8o=o("MBartForConditionalGeneration"),l8o=o(" (mBART model)"),i8o=l(),$1=a("li"),zre=a("strong"),d8o=o("mt5"),c8o=o(" \u2014 "),cI=a("a"),f8o=o("MT5ForConditionalGeneration"),m8o=o(" (mT5 model)"),g8o=l(),I1=a("li"),Wre=a("strong"),h8o=o("pegasus"),p8o=o(" \u2014 "),fI=a("a"),_8o=o("PegasusForConditionalGeneration"),u8o=o(" (Pegasus model)"),b8o=l(),j1=a("li"),Qre=a("strong"),v8o=o("plbart"),T8o=o(" \u2014 "),mI=a("a"),F8o=o("PLBartForConditionalGeneration"),C8o=o(" (PLBart model)"),M8o=l(),D1=a("li"),Hre=a("strong"),E8o=o("prophetnet"),y8o=o(" \u2014 "),gI=a("a"),w8o=o("ProphetNetForConditionalGeneration"),A8o=o(" (ProphetNet model)"),L8o=l(),N1=a("li"),Ure=a("strong"),B8o=o("t5"),x8o=o(" \u2014 "),hI=a("a"),k8o=o("T5ForConditionalGeneration"),R8o=o(" (T5 model)"),S8o=l(),q1=a("li"),Jre=a("strong"),P8o=o("xlm-prophetnet"),$8o=o(" \u2014 "),pI=a("a"),I8o=o("XLMProphetNetForConditionalGeneration"),j8o=o(" (XLMProphetNet model)"),D8o=l(),O1=a("p"),N8o=o("The model is set in evaluation mode by default using "),Yre=a("code"),q8o=o("model.eval()"),O8o=o(` (so for instance, dropout modules are
deactivated). To train the model, you should first set it back in training mode with `),Kre=a("code"),G8o=o("model.train()"),X8o=l(),Zre=a("p"),V8o=o("Examples:"),z8o=l(),f(lE.$$.fragment),Bxe=l(),md=a("h2"),G1=a("a"),ete=a("span"),f(iE.$$.fragment),W8o=l(),ote=a("span"),Q8o=o("AutoModelForSequenceClassification"),xxe=l(),or=a("div"),f(dE.$$.fragment),H8o=l(),gd=a("p"),U8o=o(`This is a generic model class that will be instantiated as one of the model classes of the library (with a sequence classification head) when created
with the `),rte=a("code"),J8o=o("from_pretrained()"),Y8o=o("class method or the "),tte=a("code"),K8o=o("from_config()"),Z8o=o(`class
method.`),eFo=l(),cE=a("p"),oFo=o("This class cannot be instantiated directly using "),ate=a("code"),rFo=o("__init__()"),tFo=o(" (throws an error)."),aFo=l(),Ur=a("div"),f(fE.$$.fragment),nFo=l(),nte=a("p"),sFo=o("Instantiates one of the model classes of the library (with a sequence classification head) from a configuration."),lFo=l(),hd=a("p"),iFo=o(`Note:
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model\u2019s configuration. Use `),lte=a("code"),fFo=o("from_pretrained()"),mFo=o("to load the model weights."),gFo=l(),ite=a("p"),hFo=o("Examples:"),pFo=l(),f(mE.$$.fragment),_Fo=l(),Ge=a("div"),f(gE.$$.fragment),uFo=l(),dte=a("p"),bFo=o("Instantiate one of the model classes of the library (with a sequence classification head) from a pretrained model."),vFo=l(),Ua=a("p"),TFo=o("The model class to instantiate is selected based on the "),cte=a("code"),FFo=o("model_type"),CFo=o(` property of the config object (either
passed as an argument or loaded from `),fte=a("code"),MFo=o("pretrained_model_name_or_path"),EFo=o(` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),mte=a("code"),yFo=o("pretrained_model_name_or_path"),wFo=o(":"),AFo=l(),A=a("ul"),X1=a("li"),gte=a("strong"),LFo=o("albert"),BFo=o(" \u2014 "),_I=a("a"),xFo=o("AlbertForSequenceClassification"),kFo=o(" (ALBERT model)"),RFo=l(),V1=a("li"),hte=a("strong"),SFo=o("bart"),PFo=o(" \u2014 "),uI=a("a"),$Fo=o("BartForSequenceClassification"),IFo=o(" (BART model)"),jFo=l(),z1=a("li"),pte=a("strong"),DFo=o("bert"),NFo=o(" \u2014 "),bI=a("a"),qFo=o("BertForSequenceClassification"),OFo=o(" (BERT model)"),GFo=l(),W1=a("li"),_te=a("strong"),XFo=o("big_bird"),VFo=o(" \u2014 "),vI=a("a"),zFo=o("BigBirdForSequenceClassification"),WFo=o(" (BigBird model)"),QFo=l(),Q1=a("li"),ute=a("strong"),HFo=o("bigbird_pegasus"),UFo=o(" \u2014 "),TI=a("a"),JFo=o("BigBirdPegasusForSequenceClassification"),YFo=o(" (BigBirdPegasus model)"),KFo=l(),H1=a("li"),bte=a("strong"),ZFo=o("camembert"),e9o=o(" \u2014 "),FI=a("a"),o9o=o("CamembertForSequenceClassification"),r9o=o(" 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model)"),I4o=l(),Pb=a("li"),aae=a("strong"),j4o=o("yoso"),D4o=o(" \u2014 "),lj=a("a"),N4o=o("YosoForSequenceClassification"),q4o=o(" (YOSO model)"),O4o=l(),$b=a("p"),G4o=o("The model is set in evaluation mode by default using "),nae=a("code"),X4o=o("model.eval()"),V4o=o(` (so for instance, dropout modules are
deactivated). To train the model, you should first set it back in training mode with `),sae=a("code"),z4o=o("model.train()"),W4o=l(),lae=a("p"),Q4o=o("Examples:"),H4o=l(),f(hE.$$.fragment),kxe=l(),pd=a("h2"),Ib=a("a"),iae=a("span"),f(pE.$$.fragment),U4o=l(),dae=a("span"),J4o=o("AutoModelForMultipleChoice"),Rxe=l(),rr=a("div"),f(_E.$$.fragment),Y4o=l(),_d=a("p"),K4o=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 `),cae=a("code"),Z4o=o("from_pretrained()"),eEo=o("class method or the "),fae=a("code"),oEo=o("from_config()"),rEo=o(`class
method.`),tEo=l(),uE=a("p"),aEo=o("This class cannot be instantiated directly using "),mae=a("code"),nEo=o("__init__()"),sEo=o(" (throws an error)."),lEo=l(),Jr=a("div"),f(bE.$$.fragment),iEo=l(),gae=a("p"),dEo=o("Instantiates one of the model classes of the library (with a multiple choice head) from a configuration."),cEo=l(),ud=a("p"),fEo=o(`Note:
Loading a model from its configuration file does `),hae=a("strong"),mEo=o("not"),gEo=o(` load the model weights. It only affects the
model\u2019s configuration. Use `),pae=a("code"),hEo=o("from_pretrained()"),pEo=o("to load the model weights."),_Eo=l(),_ae=a("p"),uEo=o("Examples:"),bEo=l(),f(vE.$$.fragment),vEo=l(),Xe=a("div"),f(TE.$$.fragment),TEo=l(),uae=a("p"),FEo=o("Instantiate one of the model classes of the library (with a multiple choice head) from a pretrained model."),CEo=l(),Ja=a("p"),MEo=o("The model class to instantiate is selected based on the "),bae=a("code"),EEo=o("model_type"),yEo=o(` property of the config object (either
passed as an argument or loaded from `),vae=a("code"),wEo=o("pretrained_model_name_or_path"),AEo=o(` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),Tae=a("code"),LEo=o("pretrained_model_name_or_path"),BEo=o(":"),xEo=l(),O=a("ul"),jb=a("li"),Fae=a("strong"),kEo=o("albert"),REo=o(" \u2014 "),ij=a("a"),SEo=o("AlbertForMultipleChoice"),PEo=o(" (ALBERT model)"),$Eo=l(),Db=a("li"),Cae=a("strong"),IEo=o("bert"),jEo=o(" \u2014 "),dj=a("a"),DEo=o("BertForMultipleChoice"),NEo=o(" (BERT model)"),qEo=l(),Nb=a("li"),Mae=a("strong"),OEo=o("big_bird"),GEo=o(" \u2014 "),cj=a("a"),XEo=o("BigBirdForMultipleChoice"),VEo=o(" (BigBird model)"),zEo=l(),qb=a("li"),Eae=a("strong"),WEo=o("camembert"),QEo=o(" \u2014 "),fj=a("a"),HEo=o("CamembertForMultipleChoice"),UEo=o(" (CamemBERT model)"),JEo=l(),Ob=a("li"),yae=a("strong"),YEo=o("canine"),KEo=o(" \u2014 "),mj=a("a"),ZEo=o("CanineForMultipleChoice"),eyo=o(" (Canine model)"),oyo=l(),Gb=a("li"),wae=a("strong"),ryo=o("convbert"),tyo=o(" \u2014 "),gj=a("a"),ayo=o("ConvBertForMultipleChoice"),nyo=o(" (ConvBERT model)"),syo=l(),Xb=a("li"),Aae=a("strong"),lyo=o("data2vec-text"),iyo=o(" \u2014 "),hj=a("a"),dyo=o("Data2VecTextForMultipleChoice"),cyo=o(" (Data2VecText model)"),fyo=l(),Vb=a("li"),Lae=a("strong"),myo=o("distilbert"),gyo=o(" \u2014 "),pj=a("a"),hyo=o("DistilBertForMultipleChoice"),pyo=o(" (DistilBERT model)"),_yo=l(),zb=a("li"),Bae=a("strong"),uyo=o("electra"),byo=o(" \u2014 "),_j=a("a"),vyo=o("ElectraForMultipleChoice"),Tyo=o(" (ELECTRA model)"),Fyo=l(),Wb=a("li"),xae=a("strong"),Cyo=o("flaubert"),Myo=o(" \u2014 "),uj=a("a"),Eyo=o("FlaubertForMultipleChoice"),yyo=o(" (FlauBERT model)"),wyo=l(),Qb=a("li"),kae=a("strong"),Ayo=o("fnet"),Lyo=o(" \u2014 "),bj=a("a"),Byo=o("FNetForMultipleChoice"),xyo=o(" (FNet model)"),kyo=l(),Hb=a("li"),Rae=a("strong"),Ryo=o("funnel"),Syo=o(" \u2014 "),vj=a("a"),Pyo=o("FunnelForMultipleChoice"),$yo=o(" (Funnel Transformer model)"),Iyo=l(),Ub=a("li"),Sae=a("strong"),jyo=o("ibert"),Dyo=o(" \u2014 "),Tj=a("a"),Nyo=o("IBertForMultipleChoice"),qyo=o(" (I-BERT model)"),Oyo=l(),Jb=a("li"),Pae=a("strong"),Gyo=o("longformer"),Xyo=o(" \u2014 "),Fj=a("a"),Vyo=o("LongformerForMultipleChoice"),zyo=o(" (Longformer model)"),Wyo=l(),Yb=a("li"),$ae=a("strong"),Qyo=o("megatron-bert"),Hyo=o(" \u2014 "),Cj=a("a"),Uyo=o("MegatronBertForMultipleChoice"),Jyo=o(" (MegatronBert model)"),Yyo=l(),Kb=a("li"),Iae=a("strong"),Kyo=o("mobilebert"),Zyo=o(" \u2014 "),Mj=a("a"),ewo=o("MobileBertForMultipleChoice"),owo=o(" (MobileBERT model)"),rwo=l(),Zb=a("li"),jae=a("strong"),two=o("mpnet"),awo=o(" \u2014 "),Ej=a("a"),nwo=o("MPNetForMultipleChoice"),swo=o(" (MPNet model)"),lwo=l(),e3=a("li"),Dae=a("strong"),iwo=o("nystromformer"),dwo=o(" \u2014 "),yj=a("a"),cwo=o("NystromformerForMultipleChoice"),fwo=o(" (Nystromformer model)"),mwo=l(),o3=a("li"),Nae=a("strong"),gwo=o("qdqbert"),hwo=o(" \u2014 "),wj=a("a"),pwo=o("QDQBertForMultipleChoice"),_wo=o(" (QDQBert model)"),uwo=l(),r3=a("li"),qae=a("strong"),bwo=o("rembert"),vwo=o(" \u2014 "),Aj=a("a"),Two=o("RemBertForMultipleChoice"),Fwo=o(" (RemBERT model)"),Cwo=l(),t3=a("li"),Oae=a("strong"),Mwo=o("roberta"),Ewo=o(" \u2014 "),Lj=a("a"),ywo=o("RobertaForMultipleChoice"),wwo=o(" (RoBERTa model)"),Awo=l(),a3=a("li"),Gae=a("strong"),Lwo=o("roformer"),Bwo=o(" \u2014 "),Bj=a("a"),xwo=o("RoFormerForMultipleChoice"),kwo=o(" (RoFormer model)"),Rwo=l(),n3=a("li"),Xae=a("strong"),Swo=o("squeezebert"),Pwo=o(" \u2014 "),xj=a("a"),$wo=o("SqueezeBertForMultipleChoice"),Iwo=o(" (SqueezeBERT model)"),jwo=l(),s3=a("li"),Vae=a("strong"),Dwo=o("xlm"),Nwo=o(" \u2014 "),kj=a("a"),qwo=o("XLMForMultipleChoice"),Owo=o(" (XLM model)"),Gwo=l(),l3=a("li"),zae=a("strong"),Xwo=o("xlm-roberta"),Vwo=o(" \u2014 "),Rj=a("a"),zwo=o("XLMRobertaForMultipleChoice"),Wwo=o(" (XLM-RoBERTa model)"),Qwo=l(),i3=a("li"),Wae=a("strong"),Hwo=o("xlm-roberta-xl"),Uwo=o(" \u2014 "),Sj=a("a"),Jwo=o("XLMRobertaXLForMultipleChoice"),Ywo=o(" (XLM-RoBERTa-XL model)"),Kwo=l(),d3=a("li"),Qae=a("strong"),Zwo=o("xlnet"),e6o=o(" \u2014 "),Pj=a("a"),o6o=o("XLNetForMultipleChoice"),r6o=o(" (XLNet model)"),t6o=l(),c3=a("li"),Hae=a("strong"),a6o=o("yoso"),n6o=o(" \u2014 "),$j=a("a"),s6o=o("YosoForMultipleChoice"),l6o=o(" (YOSO model)"),i6o=l(),f3=a("p"),d6o=o("The model is set in evaluation mode by default using "),Uae=a("code"),c6o=o("model.eval()"),f6o=o(` (so for instance, dropout modules are
deactivated). To train the model, you should first set it back in training mode with `),Jae=a("code"),m6o=o("model.train()"),g6o=l(),Yae=a("p"),h6o=o("Examples:"),p6o=l(),f(FE.$$.fragment),Sxe=l(),bd=a("h2"),m3=a("a"),Kae=a("span"),f(CE.$$.fragment),_6o=l(),Zae=a("span"),u6o=o("AutoModelForNextSentencePrediction"),Pxe=l(),tr=a("div"),f(ME.$$.fragment),b6o=l(),vd=a("p"),v6o=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
with the `),ene=a("code"),T6o=o("from_pretrained()"),F6o=o("class method or the "),one=a("code"),C6o=o("from_config()"),M6o=o(`class
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passed as an argument or loaded from `),dne=a("code"),Q6o=o("pretrained_model_name_or_path"),H6o=o(` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),cne=a("code"),U6o=o("pretrained_model_name_or_path"),J6o=o(":"),Y6o=l(),da=a("ul"),g3=a("li"),fne=a("strong"),K6o=o("bert"),Z6o=o(" \u2014 "),Ij=a("a"),eAo=o("BertForNextSentencePrediction"),oAo=o(" (BERT model)"),rAo=l(),h3=a("li"),mne=a("strong"),tAo=o("fnet"),aAo=o(" \u2014 "),jj=a("a"),nAo=o("FNetForNextSentencePrediction"),sAo=o(" (FNet model)"),lAo=l(),p3=a("li"),gne=a("strong"),iAo=o("megatron-bert"),dAo=o(" \u2014 "),Dj=a("a"),cAo=o("MegatronBertForNextSentencePrediction"),fAo=o(" (MegatronBert model)"),mAo=l(),_3=a("li"),hne=a("strong"),gAo=o("mobilebert"),hAo=o(" \u2014 "),Nj=a("a"),pAo=o("MobileBertForNextSentencePrediction"),_Ao=o(" (MobileBERT model)"),uAo=l(),u3=a("li"),pne=a("strong"),bAo=o("qdqbert"),vAo=o(" \u2014 "),qj=a("a"),TAo=o("QDQBertForNextSentencePrediction"),FAo=o(" (QDQBert model)"),CAo=l(),b3=a("p"),MAo=o("The model is set in evaluation mode by default using "),_ne=a("code"),EAo=o("model.eval()"),yAo=o(` (so for instance, dropout modules are
deactivated). To train the model, you should first set it back in training mode with `),une=a("code"),wAo=o("model.train()"),AAo=l(),bne=a("p"),LAo=o("Examples:"),BAo=l(),f(LE.$$.fragment),$xe=l(),Fd=a("h2"),v3=a("a"),vne=a("span"),f(BE.$$.fragment),xAo=l(),Tne=a("span"),kAo=o("AutoModelForTokenClassification"),Ixe=l(),ar=a("div"),f(xE.$$.fragment),RAo=l(),Cd=a("p"),SAo=o(`This is a generic model class that will be instantiated as one of the model classes of the library (with a token classification head) when created
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method.`),DAo=l(),kE=a("p"),NAo=o("This class cannot be instantiated directly using "),Mne=a("code"),qAo=o("__init__()"),OAo=o(" (throws an error)."),GAo=l(),Kr=a("div"),f(RE.$$.fragment),XAo=l(),Ene=a("p"),VAo=o("Instantiates one of the model classes of the library (with a token classification head) from a configuration."),zAo=l(),Md=a("p"),WAo=o(`Note:
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model\u2019s configuration. Use `),wne=a("code"),UAo=o("from_pretrained()"),JAo=o("to load the model weights."),YAo=l(),Ane=a("p"),KAo=o("Examples:"),ZAo=l(),f(SE.$$.fragment),e0o=l(),ze=a("div"),f(PE.$$.fragment),o0o=l(),Lne=a("p"),r0o=o("Instantiate one of the model classes of the library (with a token classification head) from a pretrained model."),t0o=l(),Ka=a("p"),a0o=o("The model class to instantiate is selected based on the "),Bne=a("code"),n0o=o("model_type"),s0o=o(` property of the config object (either
passed as an argument or loaded from `),xne=a("code"),l0o=o("pretrained_model_name_or_path"),i0o=o(` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),kne=a("code"),d0o=o("pretrained_model_name_or_path"),c0o=o(":"),f0o=l(),N=a("ul"),T3=a("li"),Rne=a("strong"),m0o=o("albert"),g0o=o(" \u2014 "),Oj=a("a"),h0o=o("AlbertForTokenClassification"),p0o=o(" (ALBERT model)"),_0o=l(),F3=a("li"),Sne=a("strong"),u0o=o("bert"),b0o=o(" \u2014 "),Gj=a("a"),v0o=o("BertForTokenClassification"),T0o=o(" (BERT model)"),F0o=l(),C3=a("li"),Pne=a("strong"),C0o=o("big_bird"),M0o=o(" \u2014 "),Xj=a("a"),E0o=o("BigBirdForTokenClassification"),y0o=o(" (BigBird model)"),w0o=l(),M3=a("li"),$ne=a("strong"),A0o=o("camembert"),L0o=o(" \u2014 "),Vj=a("a"),B0o=o("CamembertForTokenClassification"),x0o=o(" (CamemBERT model)"),k0o=l(),E3=a("li"),Ine=a("strong"),R0o=o("canine"),S0o=o(" \u2014 "),zj=a("a"),P0o=o("CanineForTokenClassification"),$0o=o(" (Canine model)"),I0o=l(),y3=a("li"),jne=a("strong"),j0o=o("convbert"),D0o=o(" \u2014 "),Wj=a("a"),N0o=o("ConvBertForTokenClassification"),q0o=o(" (ConvBERT model)"),O0o=l(),w3=a("li"),Dne=a("strong"),G0o=o("data2vec-text"),X0o=o(" \u2014 "),Qj=a("a"),V0o=o("Data2VecTextForTokenClassification"),z0o=o(" (Data2VecText model)"),W0o=l(),A3=a("li"),Nne=a("strong"),Q0o=o("deberta"),H0o=o(" \u2014 "),Hj=a("a"),U0o=o("DebertaForTokenClassification"),J0o=o(" (DeBERTa model)"),Y0o=l(),L3=a("li"),qne=a("strong"),K0o=o("deberta-v2"),Z0o=o(" \u2014 "),Uj=a("a"),eLo=o("DebertaV2ForTokenClassification"),oLo=o(" (DeBERTa-v2 model)"),rLo=l(),B3=a("li"),One=a("strong"),tLo=o("distilbert"),aLo=o(" \u2014 "),Jj=a("a"),nLo=o("DistilBertForTokenClassification"),sLo=o(" (DistilBERT model)"),lLo=l(),x3=a("li"),Gne=a("strong"),iLo=o("electra"),dLo=o(" \u2014 "),Yj=a("a"),cLo=o("ElectraForTokenClassification"),fLo=o(" (ELECTRA model)"),mLo=l(),k3=a("li"),Xne=a("strong"),gLo=o("flaubert"),hLo=o(" \u2014 "),Kj=a("a"),pLo=o("FlaubertForTokenClassification"),_Lo=o(" (FlauBERT model)"),uLo=l(),R3=a("li"),Vne=a("strong"),bLo=o("fnet"),vLo=o(" \u2014 "),Zj=a("a"),TLo=o("FNetForTokenClassification"),FLo=o(" (FNet model)"),CLo=l(),S3=a("li"),zne=a("strong"),MLo=o("funnel"),ELo=o(" \u2014 "),eD=a("a"),yLo=o("FunnelForTokenClassification"),wLo=o(" (Funnel Transformer model)"),ALo=l(),P3=a("li"),Wne=a("strong"),LLo=o("gpt2"),BLo=o(" \u2014 "),oD=a("a"),xLo=o("GPT2ForTokenClassification"),kLo=o(" (OpenAI GPT-2 model)"),RLo=l(),$3=a("li"),Qne=a("strong"),SLo=o("ibert"),PLo=o(" \u2014 "),rD=a("a"),$Lo=o("IBertForTokenClassification"),ILo=o(" (I-BERT model)"),jLo=l(),I3=a("li"),Hne=a("strong"),DLo=o("layoutlm"),NLo=o(" \u2014 "),tD=a("a"),qLo=o("LayoutLMForTokenClassification"),OLo=o(" (LayoutLM model)"),GLo=l(),j3=a("li"),Une=a("strong"),XLo=o("layoutlmv2"),VLo=o(" \u2014 "),aD=a("a"),zLo=o("LayoutLMv2ForTokenClassification"),WLo=o(" (LayoutLMv2 model)"),QLo=l(),D3=a("li"),Jne=a("strong"),HLo=o("longformer"),ULo=o(" \u2014 "),nD=a("a"),JLo=o("LongformerForTokenClassification"),YLo=o(" (Longformer 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"),mD=a("a"),k7o=o("RobertaForTokenClassification"),R7o=o(" (RoBERTa model)"),S7o=l(),W3=a("li"),ase=a("strong"),P7o=o("roformer"),$7o=o(" \u2014 "),gD=a("a"),I7o=o("RoFormerForTokenClassification"),j7o=o(" (RoFormer model)"),D7o=l(),Q3=a("li"),nse=a("strong"),N7o=o("squeezebert"),q7o=o(" \u2014 "),hD=a("a"),O7o=o("SqueezeBertForTokenClassification"),G7o=o(" (SqueezeBERT model)"),X7o=l(),H3=a("li"),sse=a("strong"),V7o=o("xlm"),z7o=o(" \u2014 "),pD=a("a"),W7o=o("XLMForTokenClassification"),Q7o=o(" (XLM model)"),H7o=l(),U3=a("li"),lse=a("strong"),U7o=o("xlm-roberta"),J7o=o(" \u2014 "),_D=a("a"),Y7o=o("XLMRobertaForTokenClassification"),K7o=o(" (XLM-RoBERTa model)"),Z7o=l(),J3=a("li"),ise=a("strong"),eBo=o("xlm-roberta-xl"),oBo=o(" \u2014 "),uD=a("a"),rBo=o("XLMRobertaXLForTokenClassification"),tBo=o(" (XLM-RoBERTa-XL model)"),aBo=l(),Y3=a("li"),dse=a("strong"),nBo=o("xlnet"),sBo=o(" \u2014 "),bD=a("a"),lBo=o("XLNetForTokenClassification"),iBo=o(" (XLNet model)"),dBo=l(),K3=a("li"),cse=a("strong"),cBo=o("yoso"),fBo=o(" \u2014 "),vD=a("a"),mBo=o("YosoForTokenClassification"),gBo=o(" (YOSO model)"),hBo=l(),Z3=a("p"),pBo=o("The model is set in evaluation mode by default using "),fse=a("code"),_Bo=o("model.eval()"),uBo=o(` (so for instance, dropout modules are
deactivated). To train the model, you should first set it back in training mode with `),mse=a("code"),bBo=o("model.train()"),vBo=l(),gse=a("p"),TBo=o("Examples:"),FBo=l(),f($E.$$.fragment),jxe=l(),Ed=a("h2"),e5=a("a"),hse=a("span"),f(IE.$$.fragment),CBo=l(),pse=a("span"),MBo=o("AutoModelForQuestionAnswering"),Dxe=l(),nr=a("div"),f(jE.$$.fragment),EBo=l(),yd=a("p"),yBo=o(`This is a generic model class that will be instantiated as one of the model classes of the library (with a question answering head) when created
with the `),_se=a("code"),wBo=o("from_pretrained()"),ABo=o("class method or the "),use=a("code"),LBo=o("from_config()"),BBo=o(`class
method.`),xBo=l(),DE=a("p"),kBo=o("This class cannot be instantiated directly using "),bse=a("code"),RBo=o("__init__()"),SBo=o(" (throws an error)."),PBo=l(),Zr=a("div"),f(NE.$$.fragment),$Bo=l(),vse=a("p"),IBo=o("Instantiates one of the model classes of the library (with a question answering head) from a configuration."),jBo=l(),wd=a("p"),DBo=o(`Note:
Loading a model from its configuration file does `),Tse=a("strong"),NBo=o("not"),qBo=o(` load the model weights. It only affects the
model\u2019s configuration. Use `),Fse=a("code"),OBo=o("from_pretrained()"),GBo=o("to load the model weights."),XBo=l(),Cse=a("p"),VBo=o("Examples:"),zBo=l(),f(qE.$$.fragment),WBo=l(),We=a("div"),f(OE.$$.fragment),QBo=l(),Mse=a("p"),HBo=o("Instantiate one of the model classes of the library (with a question answering head) from a pretrained model."),UBo=l(),Za=a("p"),JBo=o("The model class to instantiate is selected based on the "),Ese=a("code"),YBo=o("model_type"),KBo=o(` property of the config object (either
passed as an argument or loaded from `),yse=a("code"),ZBo=o("pretrained_model_name_or_path"),exo=o(` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),wse=a("code"),oxo=o("pretrained_model_name_or_path"),rxo=o(":"),txo=l(),R=a("ul"),o5=a("li"),Ase=a("strong"),axo=o("albert"),nxo=o(" \u2014 "),TD=a("a"),sxo=o("AlbertForQuestionAnswering"),lxo=o(" (ALBERT model)"),ixo=l(),r5=a("li"),Lse=a("strong"),dxo=o("bart"),cxo=o(" \u2014 "),FD=a("a"),fxo=o("BartForQuestionAnswering"),mxo=o(" (BART model)"),gxo=l(),t5=a("li"),Bse=a("strong"),hxo=o("bert"),pxo=o(" \u2014 "),CD=a("a"),_xo=o("BertForQuestionAnswering"),uxo=o(" (BERT model)"),bxo=l(),a5=a("li"),xse=a("strong"),vxo=o("big_bird"),Txo=o(" \u2014 "),MD=a("a"),Fxo=o("BigBirdForQuestionAnswering"),Cxo=o(" (BigBird model)"),Mxo=l(),n5=a("li"),kse=a("strong"),Exo=o("bigbird_pegasus"),yxo=o(" \u2014 "),ED=a("a"),wxo=o("BigBirdPegasusForQuestionAnswering"),Axo=o(" (BigBirdPegasus model)"),Lxo=l(),s5=a("li"),Rse=a("strong"),Bxo=o("camembert"),xxo=o(" \u2014 "),yD=a("a"),kxo=o("CamembertForQuestionAnswering"),Rxo=o(" (CamemBERT model)"),Sxo=l(),l5=a("li"),Sse=a("strong"),Pxo=o("canine"),$xo=o(" \u2014 "),wD=a("a"),Ixo=o("CanineForQuestionAnswering"),jxo=o(" (Canine model)"),Dxo=l(),i5=a("li"),Pse=a("strong"),Nxo=o("convbert"),qxo=o(" \u2014 "),AD=a("a"),Oxo=o("ConvBertForQuestionAnswering"),Gxo=o(" (ConvBERT model)"),Xxo=l(),d5=a("li"),$se=a("strong"),Vxo=o("data2vec-text"),zxo=o(" \u2014 "),LD=a("a"),Wxo=o("Data2VecTextForQuestionAnswering"),Qxo=o(" (Data2VecText model)"),Hxo=l(),c5=a("li"),Ise=a("strong"),Uxo=o("deberta"),Jxo=o(" \u2014 "),BD=a("a"),Yxo=o("DebertaForQuestionAnswering"),Kxo=o(" (DeBERTa model)"),Zxo=l(),f5=a("li"),jse=a("strong"),eko=o("deberta-v2"),oko=o(" \u2014 "),xD=a("a"),rko=o("DebertaV2ForQuestionAnswering"),tko=o(" (DeBERTa-v2 model)"),ako=l(),m5=a("li"),Dse=a("strong"),nko=o("distilbert"),sko=o(" \u2014 "),kD=a("a"),lko=o("DistilBertForQuestionAnswering"),iko=o(" (DistilBERT model)"),dko=l(),g5=a("li"),Nse=a("strong"),cko=o("electra"),fko=o(" \u2014 "),RD=a("a"),mko=o("ElectraForQuestionAnswering"),gko=o(" (ELECTRA model)"),hko=l(),h5=a("li"),qse=a("strong"),pko=o("flaubert"),_ko=o(" \u2014 "),SD=a("a"),uko=o("FlaubertForQuestionAnsweringSimple"),bko=o(" (FlauBERT model)"),vko=l(),p5=a("li"),Ose=a("strong"),Tko=o("fnet"),Fko=o(" \u2014 "),PD=a("a"),Cko=o("FNetForQuestionAnswering"),Mko=o(" (FNet model)"),Eko=l(),_5=a("li"),Gse=a("strong"),yko=o("funnel"),wko=o(" \u2014 "),$D=a("a"),Ako=o("FunnelForQuestionAnswering"),Lko=o(" (Funnel Transformer model)"),Bko=l(),u5=a("li"),Xse=a("strong"),xko=o("gptj"),kko=o(" \u2014 "),ID=a("a"),Rko=o("GPTJForQuestionAnswering"),Sko=o(" (GPT-J model)"),Pko=l(),b5=a("li"),Vse=a("strong"),$ko=o("ibert"),Iko=o(" \u2014 "),jD=a("a"),jko=o("IBertForQuestionAnswering"),Dko=o(" (I-BERT model)"),Nko=l(),v5=a("li"),zse=a("strong"),qko=o("layoutlmv2"),Oko=o(" \u2014 "),DD=a("a"),Gko=o("LayoutLMv2ForQuestionAnswering"),Xko=o(" (LayoutLMv2 model)"),Vko=l(),T5=a("li"),Wse=a("strong"),zko=o("led"),Wko=o(" \u2014 "),ND=a("a"),Qko=o("LEDForQuestionAnswering"),Hko=o(" (LED model)"),Uko=l(),F5=a("li"),Qse=a("strong"),Jko=o("longformer"),Yko=o(" \u2014 "),qD=a("a"),Kko=o("LongformerForQuestionAnswering"),Zko=o(" (Longformer model)"),eRo=l(),C5=a("li"),Hse=a("strong"),oRo=o("lxmert"),rRo=o(" \u2014 "),OD=a("a"),tRo=o("LxmertForQuestionAnswering"),aRo=o(" (LXMERT model)"),nRo=l(),M5=a("li"),Use=a("strong"),sRo=o("mbart"),lRo=o(" \u2014 "),GD=a("a"),iRo=o("MBartForQuestionAnswering"),dRo=o(" (mBART model)"),cRo=l(),E5=a("li"),Jse=a("strong"),fRo=o("megatron-bert"),mRo=o(" \u2014 "),XD=a("a"),gRo=o("MegatronBertForQuestionAnswering"),hRo=o(" (MegatronBert model)"),pRo=l(),y5=a("li"),Yse=a("strong"),_Ro=o("mobilebert"),uRo=o(" \u2014 "),VD=a("a"),bRo=o("MobileBertForQuestionAnswering"),vRo=o(" (MobileBERT model)"),TRo=l(),w5=a("li"),Kse=a("strong"),FRo=o("mpnet"),CRo=o(" \u2014 "),zD=a("a"),MRo=o("MPNetForQuestionAnswering"),ERo=o(" (MPNet 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set in evaluation mode by default using "),mle=a("code"),DSo=o("model.eval()"),NSo=o(` (so for instance, dropout modules are
deactivated). To train the model, you should first set it back in training mode with `),gle=a("code"),qSo=o("model.train()"),OSo=l(),hle=a("p"),GSo=o("Examples:"),XSo=l(),f(GE.$$.fragment),Nxe=l(),Ad=a("h2"),O5=a("a"),ple=a("span"),f(XE.$$.fragment),VSo=l(),_le=a("span"),zSo=o("AutoModelForTableQuestionAnswering"),qxe=l(),sr=a("div"),f(VE.$$.fragment),WSo=l(),Ld=a("p"),QSo=o(`This is a generic model class that will be instantiated as one of the model classes of the library (with a table question answering head) when created
with the `),ule=a("code"),HSo=o("from_pretrained()"),USo=o("class method or the "),ble=a("code"),JSo=o("from_config()"),YSo=o(`class
method.`),KSo=l(),zE=a("p"),ZSo=o("This class cannot be instantiated directly using "),vle=a("code"),ePo=o("__init__()"),oPo=o(" (throws an error)."),rPo=l(),et=a("div"),f(WE.$$.fragment),tPo=l(),Tle=a("p"),aPo=o("Instantiates one of the model classes of the library (with a table question answering head) from a configuration."),nPo=l(),Bd=a("p"),sPo=o(`Note:
Loading a model from its configuration file does `),Fle=a("strong"),lPo=o("not"),iPo=o(` load the model weights. It only affects the
model\u2019s configuration. Use `),Cle=a("code"),dPo=o("from_pretrained()"),cPo=o("to load the model weights."),fPo=l(),Mle=a("p"),mPo=o("Examples:"),gPo=l(),f(QE.$$.fragment),hPo=l(),Qe=a("div"),f(HE.$$.fragment),pPo=l(),Ele=a("p"),_Po=o("Instantiate one of the model classes of the library (with a table question answering head) from a pretrained model."),uPo=l(),en=a("p"),bPo=o("The model class to instantiate is selected based on the "),yle=a("code"),vPo=o("model_type"),TPo=o(` property of the config object (either
passed as an argument or loaded from `),wle=a("code"),FPo=o("pretrained_model_name_or_path"),CPo=o(` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),Ale=a("code"),MPo=o("pretrained_model_name_or_path"),EPo=o(":"),yPo=l(),Lle=a("ul"),G5=a("li"),Ble=a("strong"),wPo=o("tapas"),APo=o(" \u2014 "),nN=a("a"),LPo=o("TapasForQuestionAnswering"),BPo=o(" (TAPAS model)"),xPo=l(),X5=a("p"),kPo=o("The model is set in evaluation mode by default using "),xle=a("code"),RPo=o("model.eval()"),SPo=o(` (so for instance, dropout modules are
deactivated). To train the model, you should first set it back in training mode with `),kle=a("code"),PPo=o("model.train()"),$Po=l(),Rle=a("p"),IPo=o("Examples:"),jPo=l(),f(UE.$$.fragment),Oxe=l(),xd=a("h2"),V5=a("a"),Sle=a("span"),f(JE.$$.fragment),DPo=l(),Ple=a("span"),NPo=o("AutoModelForImageClassification"),Gxe=l(),lr=a("div"),f(YE.$$.fragment),qPo=l(),kd=a("p"),OPo=o(`This is a generic model class that will be instantiated as one of the model classes of the library (with a image classification head) when created
with the `),$le=a("code"),GPo=o("from_pretrained()"),XPo=o("class method or the "),Ile=a("code"),VPo=o("from_config()"),zPo=o(`class
method.`),WPo=l(),KE=a("p"),QPo=o("This class cannot be instantiated directly using "),jle=a("code"),HPo=o("__init__()"),UPo=o(" (throws an error)."),JPo=l(),ot=a("div"),f(ZE.$$.fragment),YPo=l(),Dle=a("p"),KPo=o("Instantiates one of the model classes of the library (with a image classification head) from a configuration."),ZPo=l(),Rd=a("p"),e$o=o(`Note:
Loading a model from its configuration file does `),Nle=a("strong"),o$o=o("not"),r$o=o(` load the model weights. It only affects the
model\u2019s configuration. Use `),qle=a("code"),t$o=o("from_pretrained()"),a$o=o("to load the model weights."),n$o=l(),Ole=a("p"),s$o=o("Examples:"),l$o=l(),f(ey.$$.fragment),i$o=l(),He=a("div"),f(oy.$$.fragment),d$o=l(),Gle=a("p"),c$o=o("Instantiate one of the model classes of the library (with a image classification head) from a pretrained model."),f$o=l(),on=a("p"),m$o=o("The model class to instantiate is selected based on the "),Xle=a("code"),g$o=o("model_type"),h$o=o(` property of the config object (either
passed as an argument or loaded from `),Vle=a("code"),p$o=o("pretrained_model_name_or_path"),_$o=o(` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),zle=a("code"),u$o=o("pretrained_model_name_or_path"),b$o=o(":"),v$o=l(),Ce=a("ul"),z5=a("li"),Wle=a("strong"),T$o=o("beit"),F$o=o(" \u2014 "),sN=a("a"),C$o=o("BeitForImageClassification"),M$o=o(" (BEiT model)"),E$o=l(),W5=a("li"),Qle=a("strong"),y$o=o("convnext"),w$o=o(" \u2014 "),lN=a("a"),A$o=o("ConvNextForImageClassification"),L$o=o(" (ConvNext model)"),B$o=l(),qs=a("li"),Hle=a("strong"),x$o=o("deit"),k$o=o(" \u2014 "),iN=a("a"),R$o=o("DeiTForImageClassification"),S$o=o(" or "),dN=a("a"),P$o=o("DeiTForImageClassificationWithTeacher"),$$o=o(" (DeiT model)"),I$o=l(),Q5=a("li"),Ule=a("strong"),j$o=o("imagegpt"),D$o=o(" \u2014 "),cN=a("a"),N$o=o("ImageGPTForImageClassification"),q$o=o(" (ImageGPT model)"),O$o=l(),ma=a("li"),Jle=a("strong"),G$o=o("perceiver"),X$o=o(" \u2014 "),fN=a("a"),V$o=o("PerceiverForImageClassificationLearned"),z$o=o(" or "),mN=a("a"),W$o=o("PerceiverForImageClassificationFourier"),Q$o=o(" or "),gN=a("a"),H$o=o("PerceiverForImageClassificationConvProcessing"),U$o=o(" (Perceiver model)"),J$o=l(),H5=a("li"),Yle=a("strong"),Y$o=o("poolformer"),K$o=o(" \u2014 "),hN=a("a"),Z$o=o("PoolFormerForImageClassification"),eIo=o(" (PoolFormer model)"),oIo=l(),U5=a("li"),Kle=a("strong"),rIo=o("segformer"),tIo=o(" \u2014 "),pN=a("a"),aIo=o("SegformerForImageClassification"),nIo=o(" (SegFormer model)"),sIo=l(),J5=a("li"),Zle=a("strong"),lIo=o("swin"),iIo=o(" \u2014 "),_N=a("a"),dIo=o("SwinForImageClassification"),cIo=o(" (Swin model)"),fIo=l(),Y5=a("li"),eie=a("strong"),mIo=o("vit"),gIo=o(" \u2014 "),uN=a("a"),hIo=o("ViTForImageClassification"),pIo=o(" (ViT model)"),_Io=l(),K5=a("p"),uIo=o("The model is set in evaluation mode by default using "),oie=a("code"),bIo=o("model.eval()"),vIo=o(` (so for instance, dropout modules are
deactivated). To train the model, you should first set it back in training mode with `),rie=a("code"),TIo=o("model.train()"),FIo=l(),tie=a("p"),CIo=o("Examples:"),MIo=l(),f(ry.$$.fragment),Xxe=l(),Sd=a("h2"),Z5=a("a"),aie=a("span"),f(ty.$$.fragment),EIo=l(),nie=a("span"),yIo=o("AutoModelForVision2Seq"),Vxe=l(),ir=a("div"),f(ay.$$.fragment),wIo=l(),Pd=a("p"),AIo=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
with the `),sie=a("code"),LIo=o("from_pretrained()"),BIo=o("class method or the "),lie=a("code"),xIo=o("from_config()"),kIo=o(`class
method.`),RIo=l(),ny=a("p"),SIo=o("This class cannot be instantiated directly using "),iie=a("code"),PIo=o("__init__()"),$Io=o(" (throws an error)."),IIo=l(),rt=a("div"),f(sy.$$.fragment),jIo=l(),die=a("p"),DIo=o("Instantiates one of the model classes of the library (with a vision-to-text modeling head) from a configuration."),NIo=l(),$d=a("p"),qIo=o(`Note:
Loading a model from its configuration file does `),cie=a("strong"),OIo=o("not"),GIo=o(` load the model weights. It only affects the
model\u2019s configuration. Use `),fie=a("code"),XIo=o("from_pretrained()"),VIo=o("to load the model weights."),zIo=l(),mie=a("p"),WIo=o("Examples:"),QIo=l(),f(ly.$$.fragment),HIo=l(),Ue=a("div"),f(iy.$$.fragment),UIo=l(),gie=a("p"),JIo=o("Instantiate one of the model classes of the library (with a vision-to-text modeling head) from a pretrained model."),YIo=l(),rn=a("p"),KIo=o("The model class to instantiate is selected based on the "),hie=a("code"),ZIo=o("model_type"),ejo=o(` property of the config object (either
passed as an argument or loaded from `),pie=a("code"),ojo=o("pretrained_model_name_or_path"),rjo=o(` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),_ie=a("code"),tjo=o("pretrained_model_name_or_path"),ajo=o(":"),njo=l(),uie=a("ul"),e2=a("li"),bie=a("strong"),sjo=o("vision-encoder-decoder"),ljo=o(" \u2014 "),bN=a("a"),ijo=o("VisionEncoderDecoderModel"),djo=o(" (Vision Encoder decoder model)"),cjo=l(),o2=a("p"),fjo=o("The model is set in evaluation mode by default using "),vie=a("code"),mjo=o("model.eval()"),gjo=o(` (so for instance, dropout modules are
deactivated). To train the model, you should first set it back in training mode with `),Tie=a("code"),hjo=o("model.train()"),pjo=l(),Fie=a("p"),_jo=o("Examples:"),ujo=l(),f(dy.$$.fragment),zxe=l(),Id=a("h2"),r2=a("a"),Cie=a("span"),f(cy.$$.fragment),bjo=l(),Mie=a("span"),vjo=o("AutoModelForAudioClassification"),Wxe=l(),dr=a("div"),f(fy.$$.fragment),Tjo=l(),jd=a("p"),Fjo=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 `),Eie=a("code"),Cjo=o("from_pretrained()"),Mjo=o("class method or the "),yie=a("code"),Ejo=o("from_config()"),yjo=o(`class
method.`),wjo=l(),my=a("p"),Ajo=o("This class cannot be instantiated directly using "),wie=a("code"),Ljo=o("__init__()"),Bjo=o(" (throws an error)."),xjo=l(),tt=a("div"),f(gy.$$.fragment),kjo=l(),Aie=a("p"),Rjo=o("Instantiates one of the model classes of the library (with a audio classification head) from a configuration."),Sjo=l(),Dd=a("p"),Pjo=o(`Note:
Loading a model from its configuration file does `),Lie=a("strong"),$jo=o("not"),Ijo=o(` load the model weights. It only affects the
model\u2019s configuration. Use `),Bie=a("code"),jjo=o("from_pretrained()"),Djo=o("to load the model weights."),Njo=l(),xie=a("p"),qjo=o("Examples:"),Ojo=l(),f(hy.$$.fragment),Gjo=l(),Je=a("div"),f(py.$$.fragment),Xjo=l(),kie=a("p"),Vjo=o("Instantiate one of the model classes of the library (with a audio classification head) from a pretrained model."),zjo=l(),tn=a("p"),Wjo=o("The model class to instantiate is selected based on the "),Rie=a("code"),Qjo=o("model_type"),Hjo=o(` property of the config object (either
passed as an argument or loaded from `),Sie=a("code"),Ujo=o("pretrained_model_name_or_path"),Jjo=o(` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),Pie=a("code"),Yjo=o("pretrained_model_name_or_path"),Kjo=o(":"),Zjo=l(),xe=a("ul"),t2=a("li"),$ie=a("strong"),eDo=o("data2vec-audio"),oDo=o(" \u2014 "),vN=a("a"),rDo=o("Data2VecAudioForSequenceClassification"),tDo=o(" (Data2VecAudio model)"),aDo=l(),a2=a("li"),Iie=a("strong"),nDo=o("hubert"),sDo=o(" \u2014 "),TN=a("a"),lDo=o("HubertForSequenceClassification"),iDo=o(" (Hubert model)"),dDo=l(),n2=a("li"),jie=a("strong"),cDo=o("sew"),fDo=o(" \u2014 "),FN=a("a"),mDo=o("SEWForSequenceClassification"),gDo=o(" (SEW model)"),hDo=l(),s2=a("li"),Die=a("strong"),pDo=o("sew-d"),_Do=o(" \u2014 "),CN=a("a"),uDo=o("SEWDForSequenceClassification"),bDo=o(" (SEW-D model)"),vDo=l(),l2=a("li"),Nie=a("strong"),TDo=o("unispeech"),FDo=o(" \u2014 "),MN=a("a"),CDo=o("UniSpeechForSequenceClassification"),MDo=o(" (UniSpeech model)"),EDo=l(),i2=a("li"),qie=a("strong"),yDo=o("unispeech-sat"),wDo=o(" \u2014 "),EN=a("a"),ADo=o("UniSpeechSatForSequenceClassification"),LDo=o(" (UniSpeechSat model)"),BDo=l(),d2=a("li"),Oie=a("strong"),xDo=o("wav2vec2"),kDo=o(" \u2014 "),yN=a("a"),RDo=o("Wav2Vec2ForSequenceClassification"),SDo=o(" (Wav2Vec2 model)"),PDo=l(),c2=a("li"),Gie=a("strong"),$Do=o("wavlm"),IDo=o(" \u2014 "),wN=a("a"),jDo=o("WavLMForSequenceClassification"),DDo=o(" (WavLM model)"),NDo=l(),f2=a("p"),qDo=o("The model is set in evaluation mode by default using "),Xie=a("code"),ODo=o("model.eval()"),GDo=o(` (so for instance, dropout modules are
deactivated). To train the model, you should first set it back in training mode with `),Vie=a("code"),XDo=o("model.train()"),VDo=l(),zie=a("p"),zDo=o("Examples:"),WDo=l(),f(_y.$$.fragment),Qxe=l(),Nd=a("h2"),m2=a("a"),Wie=a("span"),f(uy.$$.fragment),QDo=l(),Qie=a("span"),HDo=o("AutoModelForAudioFrameClassification"),Hxe=l(),cr=a("div"),f(by.$$.fragment),UDo=l(),qd=a("p"),JDo=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 `),Hie=a("code"),YDo=o("from_pretrained()"),KDo=o("class method or the "),Uie=a("code"),ZDo=o("from_config()"),eNo=o(`class
method.`),oNo=l(),vy=a("p"),rNo=o("This class cannot be instantiated directly using "),Jie=a("code"),tNo=o("__init__()"),aNo=o(" (throws an error)."),nNo=l(),at=a("div"),f(Ty.$$.fragment),sNo=l(),Yie=a("p"),lNo=o("Instantiates one of the model classes of the library (with a audio frame (token) classification head) from a configuration."),iNo=l(),Od=a("p"),dNo=o(`Note:
Loading a model from its configuration file does `),Kie=a("strong"),cNo=o("not"),fNo=o(` load the model weights. It only affects the
model\u2019s configuration. Use `),Zie=a("code"),mNo=o("from_pretrained()"),gNo=o("to load the model weights."),hNo=l(),ede=a("p"),pNo=o("Examples:"),_No=l(),f(Fy.$$.fragment),uNo=l(),Ye=a("div"),f(Cy.$$.fragment),bNo=l(),ode=a("p"),vNo=o("Instantiate one of the model classes of the library (with a audio frame (token) classification head) from a pretrained model."),TNo=l(),an=a("p"),FNo=o("The model class to instantiate is selected based on the "),rde=a("code"),CNo=o("model_type"),MNo=o(` property of the config object (either
passed as an argument or loaded from `),tde=a("code"),ENo=o("pretrained_model_name_or_path"),yNo=o(` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),ade=a("code"),wNo=o("pretrained_model_name_or_path"),ANo=o(":"),LNo=l(),nn=a("ul"),g2=a("li"),nde=a("strong"),BNo=o("data2vec-audio"),xNo=o(" \u2014 "),AN=a("a"),kNo=o("Data2VecAudioForAudioFrameClassification"),RNo=o(" (Data2VecAudio model)"),SNo=l(),h2=a("li"),sde=a("strong"),PNo=o("unispeech-sat"),$No=o(" \u2014 "),LN=a("a"),INo=o("UniSpeechSatForAudioFrameClassification"),jNo=o(" (UniSpeechSat model)"),DNo=l(),p2=a("li"),lde=a("strong"),NNo=o("wav2vec2"),qNo=o(" \u2014 "),BN=a("a"),ONo=o("Wav2Vec2ForAudioFrameClassification"),GNo=o(" (Wav2Vec2 model)"),XNo=l(),_2=a("li"),ide=a("strong"),VNo=o("wavlm"),zNo=o(" \u2014 "),xN=a("a"),WNo=o("WavLMForAudioFrameClassification"),QNo=o(" (WavLM model)"),HNo=l(),u2=a("p"),UNo=o("The model is set in evaluation mode by default using "),dde=a("code"),JNo=o("model.eval()"),YNo=o(` (so for instance, dropout modules are
deactivated). To train the model, you should first set it back in training mode with `),cde=a("code"),KNo=o("model.train()"),ZNo=l(),fde=a("p"),eqo=o("Examples:"),oqo=l(),f(My.$$.fragment),Uxe=l(),Gd=a("h2"),b2=a("a"),mde=a("span"),f(Ey.$$.fragment),rqo=l(),gde=a("span"),tqo=o("AutoModelForCTC"),Jxe=l(),fr=a("div"),f(yy.$$.fragment),aqo=l(),Xd=a("p"),nqo=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 `),hde=a("code"),sqo=o("from_pretrained()"),lqo=o("class method or the "),pde=a("code"),iqo=o("from_config()"),dqo=o(`class
method.`),cqo=l(),wy=a("p"),fqo=o("This class cannot be instantiated directly using "),_de=a("code"),mqo=o("__init__()"),gqo=o(" (throws an error)."),hqo=l(),nt=a("div"),f(Ay.$$.fragment),pqo=l(),ude=a("p"),_qo=o("Instantiates one of the model classes of the library (with a connectionist temporal classification head) from a configuration."),uqo=l(),Vd=a("p"),bqo=o(`Note:
Loading a model from its configuration file does `),bde=a("strong"),vqo=o("not"),Tqo=o(` load the model weights. It only affects the
model\u2019s configuration. Use `),vde=a("code"),Fqo=o("from_pretrained()"),Cqo=o("to load the model weights."),Mqo=l(),Tde=a("p"),Eqo=o("Examples:"),yqo=l(),f(Ly.$$.fragment),wqo=l(),Ke=a("div"),f(By.$$.fragment),Aqo=l(),Fde=a("p"),Lqo=o("Instantiate one of the model classes of the library (with a connectionist temporal classification head) from a pretrained model."),Bqo=l(),sn=a("p"),xqo=o("The model class to instantiate is selected based on the "),Cde=a("code"),kqo=o("model_type"),Rqo=o(` property of the config object (either
passed as an argument or loaded from `),Mde=a("code"),Sqo=o("pretrained_model_name_or_path"),Pqo=o(` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),Ede=a("code"),$qo=o("pretrained_model_name_or_path"),Iqo=o(":"),jqo=l(),ke=a("ul"),v2=a("li"),yde=a("strong"),Dqo=o("data2vec-audio"),Nqo=o(" \u2014 "),kN=a("a"),qqo=o("Data2VecAudioForCTC"),Oqo=o(" (Data2VecAudio model)"),Gqo=l(),T2=a("li"),wde=a("strong"),Xqo=o("hubert"),Vqo=o(" \u2014 "),RN=a("a"),zqo=o("HubertForCTC"),Wqo=o(" (Hubert model)"),Qqo=l(),F2=a("li"),Ade=a("strong"),Hqo=o("sew"),Uqo=o(" \u2014 "),SN=a("a"),Jqo=o("SEWForCTC"),Yqo=o(" (SEW model)"),Kqo=l(),C2=a("li"),Lde=a("strong"),Zqo=o("sew-d"),eOo=o(" \u2014 "),PN=a("a"),oOo=o("SEWDForCTC"),rOo=o(" (SEW-D model)"),tOo=l(),M2=a("li"),Bde=a("strong"),aOo=o("unispeech"),nOo=o(" \u2014 "),$N=a("a"),sOo=o("UniSpeechForCTC"),lOo=o(" (UniSpeech model)"),iOo=l(),E2=a("li"),xde=a("strong"),dOo=o("unispeech-sat"),cOo=o(" \u2014 "),IN=a("a"),fOo=o("UniSpeechSatForCTC"),mOo=o(" (UniSpeechSat model)"),gOo=l(),y2=a("li"),kde=a("strong"),hOo=o("wav2vec2"),pOo=o(" \u2014 "),jN=a("a"),_Oo=o("Wav2Vec2ForCTC"),uOo=o(" (Wav2Vec2 model)"),bOo=l(),w2=a("li"),Rde=a("strong"),vOo=o("wavlm"),TOo=o(" \u2014 "),DN=a("a"),FOo=o("WavLMForCTC"),COo=o(" (WavLM model)"),MOo=l(),A2=a("p"),EOo=o("The model is set in evaluation mode by default using "),Sde=a("code"),yOo=o("model.eval()"),wOo=o(` (so for instance, dropout modules are
deactivated). To train the model, you should first set it back in training mode with `),Pde=a("code"),AOo=o("model.train()"),LOo=l(),$de=a("p"),BOo=o("Examples:"),xOo=l(),f(xy.$$.fragment),Yxe=l(),zd=a("h2"),L2=a("a"),Ide=a("span"),f(ky.$$.fragment),kOo=l(),jde=a("span"),ROo=o("AutoModelForSpeechSeq2Seq"),Kxe=l(),mr=a("div"),f(Ry.$$.fragment),SOo=l(),Wd=a("p"),POo=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 `),Dde=a("code"),$Oo=o("from_pretrained()"),IOo=o("class method or the "),Nde=a("code"),jOo=o("from_config()"),DOo=o(`class
method.`),NOo=l(),Sy=a("p"),qOo=o("This class cannot be instantiated directly using "),qde=a("code"),OOo=o("__init__()"),GOo=o(" (throws an error)."),XOo=l(),st=a("div"),f(Py.$$.fragment),VOo=l(),Ode=a("p"),zOo=o("Instantiates one of the model classes of the library (with a sequence-to-sequence speech-to-text modeling head) from a configuration."),WOo=l(),Qd=a("p"),QOo=o(`Note:
Loading a model from its configuration file does `),Gde=a("strong"),HOo=o("not"),UOo=o(` load the model weights. It only affects the
model\u2019s configuration. Use `),Xde=a("code"),JOo=o("from_pretrained()"),YOo=o("to load the model weights."),KOo=l(),Vde=a("p"),ZOo=o("Examples:"),eGo=l(),f($y.$$.fragment),oGo=l(),Ze=a("div"),f(Iy.$$.fragment),rGo=l(),zde=a("p"),tGo=o("Instantiate one of the model classes of the library (with a sequence-to-sequence speech-to-text modeling head) from a pretrained model."),aGo=l(),ln=a("p"),nGo=o("The model class to instantiate is selected based on the "),Wde=a("code"),sGo=o("model_type"),lGo=o(` property of the config object (either
passed as an argument or loaded from `),Qde=a("code"),iGo=o("pretrained_model_name_or_path"),dGo=o(` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),Hde=a("code"),cGo=o("pretrained_model_name_or_path"),fGo=o(":"),mGo=l(),jy=a("ul"),B2=a("li"),Ude=a("strong"),gGo=o("speech-encoder-decoder"),hGo=o(" \u2014 "),NN=a("a"),pGo=o("SpeechEncoderDecoderModel"),_Go=o(" (Speech Encoder decoder model)"),uGo=l(),x2=a("li"),Jde=a("strong"),bGo=o("speech_to_text"),vGo=o(" \u2014 "),qN=a("a"),TGo=o("Speech2TextForConditionalGeneration"),FGo=o(" (Speech2Text model)"),CGo=l(),k2=a("p"),MGo=o("The model is set in evaluation mode by default using "),Yde=a("code"),EGo=o("model.eval()"),yGo=o(` (so for instance, dropout modules are
deactivated). To train the model, you should first set it back in training mode with `),Kde=a("code"),wGo=o("model.train()"),AGo=l(),Zde=a("p"),LGo=o("Examples:"),BGo=l(),f(Dy.$$.fragment),Zxe=l(),Hd=a("h2"),R2=a("a"),ece=a("span"),f(Ny.$$.fragment),xGo=l(),oce=a("span"),kGo=o("AutoModelForAudioXVector"),eke=l(),gr=a("div"),f(qy.$$.fragment),RGo=l(),Ud=a("p"),SGo=o(`This is a generic model class that will be instantiated as one of the model classes of the library (with a audio retrieval via x-vector head) when created
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method.`),DGo=l(),Oy=a("p"),NGo=o("This class cannot be instantiated directly using "),ace=a("code"),qGo=o("__init__()"),OGo=o(" (throws an error)."),GGo=l(),lt=a("div"),f(Gy.$$.fragment),XGo=l(),nce=a("p"),VGo=o("Instantiates one of the model classes of the library (with a audio retrieval via x-vector head) from a configuration."),zGo=l(),Jd=a("p"),WGo=o(`Note:
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passed as an argument or loaded from `),fce=a("code"),lXo=o("pretrained_model_name_or_path"),iXo=o(` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),mce=a("code"),dXo=o("pretrained_model_name_or_path"),cXo=o(":"),fXo=l(),cn=a("ul"),S2=a("li"),gce=a("strong"),mXo=o("data2vec-audio"),gXo=o(" \u2014 "),ON=a("a"),hXo=o("Data2VecAudioForXVector"),pXo=o(" (Data2VecAudio model)"),_Xo=l(),P2=a("li"),hce=a("strong"),uXo=o("unispeech-sat"),bXo=o(" \u2014 "),GN=a("a"),vXo=o("UniSpeechSatForXVector"),TXo=o(" (UniSpeechSat model)"),FXo=l(),$2=a("li"),pce=a("strong"),CXo=o("wav2vec2"),MXo=o(" \u2014 "),XN=a("a"),EXo=o("Wav2Vec2ForXVector"),yXo=o(" (Wav2Vec2 model)"),wXo=l(),I2=a("li"),_ce=a("strong"),AXo=o("wavlm"),LXo=o(" \u2014 "),VN=a("a"),BXo=o("WavLMForXVector"),xXo=o(" (WavLM model)"),kXo=l(),j2=a("p"),RXo=o("The model is set in evaluation mode by default using "),uce=a("code"),SXo=o("model.eval()"),PXo=o(` (so for instance, dropout modules are
deactivated). To train the model, you should first set it back in training mode with `),bce=a("code"),$Xo=o("model.train()"),IXo=l(),vce=a("p"),jXo=o("Examples:"),DXo=l(),f(zy.$$.fragment),oke=l(),Yd=a("h2"),D2=a("a"),Tce=a("span"),f(Wy.$$.fragment),NXo=l(),Fce=a("span"),qXo=o("AutoModelForMaskedImageModeling"),rke=l(),hr=a("div"),f(Qy.$$.fragment),OXo=l(),Kd=a("p"),GXo=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 `),Cce=a("code"),XXo=o("from_pretrained()"),VXo=o("class method or the "),Mce=a("code"),zXo=o("from_config()"),WXo=o(`class
method.`),QXo=l(),Hy=a("p"),HXo=o("This class cannot be instantiated directly using "),Ece=a("code"),UXo=o("__init__()"),JXo=o(" (throws an error)."),YXo=l(),it=a("div"),f(Uy.$$.fragment),KXo=l(),yce=a("p"),ZXo=o("Instantiates one of the model classes of the library (with a masked image modeling head) from a configuration."),eVo=l(),Zd=a("p"),oVo=o(`Note:
Loading a model from its configuration file does `),wce=a("strong"),rVo=o("not"),tVo=o(` load the model weights. It only affects the
model\u2019s configuration. Use `),Ace=a("code"),aVo=o("from_pretrained()"),nVo=o("to load the model weights."),sVo=l(),Lce=a("p"),lVo=o("Examples:"),iVo=l(),f(Jy.$$.fragment),dVo=l(),oo=a("div"),f(Yy.$$.fragment),cVo=l(),Bce=a("p"),fVo=o("Instantiate one of the model classes of the library (with a masked image modeling head) from a pretrained model."),mVo=l(),fn=a("p"),gVo=o("The model class to instantiate is selected based on the "),xce=a("code"),hVo=o("model_type"),pVo=o(` property of the config object (either
passed as an argument or loaded from `),kce=a("code"),_Vo=o("pretrained_model_name_or_path"),uVo=o(` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),Rce=a("code"),bVo=o("pretrained_model_name_or_path"),vVo=o(":"),TVo=l(),ec=a("ul"),N2=a("li"),Sce=a("strong"),FVo=o("deit"),CVo=o(" \u2014 "),zN=a("a"),MVo=o("DeiTForMaskedImageModeling"),EVo=o(" (DeiT model)"),yVo=l(),q2=a("li"),Pce=a("strong"),wVo=o("swin"),AVo=o(" \u2014 "),WN=a("a"),LVo=o("SwinForMaskedImageModeling"),BVo=o(" (Swin model)"),xVo=l(),O2=a("li"),$ce=a("strong"),kVo=o("vit"),RVo=o(" \u2014 "),QN=a("a"),SVo=o("ViTForMaskedImageModeling"),PVo=o(" (ViT model)"),$Vo=l(),G2=a("p"),IVo=o("The model is set in evaluation mode by default using "),Ice=a("code"),jVo=o("model.eval()"),DVo=o(` (so for instance, dropout modules are
deactivated). To train the model, you should first set it back in training mode with `),jce=a("code"),NVo=o("model.train()"),qVo=l(),Dce=a("p"),OVo=o("Examples:"),GVo=l(),f(Ky.$$.fragment),tke=l(),oc=a("h2"),X2=a("a"),Nce=a("span"),f(Zy.$$.fragment),XVo=l(),qce=a("span"),VVo=o("AutoModelForObjectDetection"),ake=l(),pr=a("div"),f(ew.$$.fragment),zVo=l(),rc=a("p"),WVo=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 `),Oce=a("code"),QVo=o("from_pretrained()"),HVo=o("class method or the "),Gce=a("code"),UVo=o("from_config()"),JVo=o(`class
method.`),YVo=l(),ow=a("p"),KVo=o("This class cannot be instantiated directly using "),Xce=a("code"),ZVo=o("__init__()"),ezo=o(" (throws an error)."),ozo=l(),dt=a("div"),f(rw.$$.fragment),rzo=l(),Vce=a("p"),tzo=o("Instantiates one of the model classes of the library (with a object detection head) from a configuration."),azo=l(),tc=a("p"),nzo=o(`Note:
Loading a model from its configuration file does `),zce=a("strong"),szo=o("not"),lzo=o(` load the model weights. It only affects the
model\u2019s configuration. Use `),Wce=a("code"),izo=o("from_pretrained()"),dzo=o("to load the model weights."),czo=l(),Qce=a("p"),fzo=o("Examples:"),mzo=l(),f(tw.$$.fragment),gzo=l(),ro=a("div"),f(aw.$$.fragment),hzo=l(),Hce=a("p"),pzo=o("Instantiate one of the model classes of the library (with a object detection head) from a pretrained model."),_zo=l(),mn=a("p"),uzo=o("The model class to instantiate is selected based on the "),Uce=a("code"),bzo=o("model_type"),vzo=o(` property of the config object (either
passed as an argument or loaded from `),Jce=a("code"),Tzo=o("pretrained_model_name_or_path"),Fzo=o(` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),Yce=a("code"),Czo=o("pretrained_model_name_or_path"),Mzo=o(":"),Ezo=l(),Kce=a("ul"),V2=a("li"),Zce=a("strong"),yzo=o("detr"),wzo=o(" \u2014 "),HN=a("a"),Azo=o("DetrForObjectDetection"),Lzo=o(" (DETR model)"),Bzo=l(),z2=a("p"),xzo=o("The model is set in evaluation mode by default using "),efe=a("code"),kzo=o("model.eval()"),Rzo=o(` (so for instance, dropout modules are
deactivated). To train the model, you should first set it back in training mode with `),ofe=a("code"),Szo=o("model.train()"),Pzo=l(),rfe=a("p"),$zo=o("Examples:"),Izo=l(),f(nw.$$.fragment),nke=l(),ac=a("h2"),W2=a("a"),tfe=a("span"),f(sw.$$.fragment),jzo=l(),afe=a("span"),Dzo=o("AutoModelForImageSegmentation"),ske=l(),_r=a("div"),f(lw.$$.fragment),Nzo=l(),nc=a("p"),qzo=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 `),nfe=a("code"),Ozo=o("from_pretrained()"),Gzo=o("class method or the "),sfe=a("code"),Xzo=o("from_config()"),Vzo=o(`class
method.`),zzo=l(),iw=a("p"),Wzo=o("This class cannot be instantiated directly using "),lfe=a("code"),Qzo=o("__init__()"),Hzo=o(" (throws an error)."),Uzo=l(),ct=a("div"),f(dw.$$.fragment),Jzo=l(),ife=a("p"),Yzo=o("Instantiates one of the model classes of the library (with a image segmentation head) from a configuration."),Kzo=l(),sc=a("p"),Zzo=o(`Note:
Loading a model from its configuration file does `),dfe=a("strong"),eWo=o("not"),oWo=o(` load the model weights. It only affects the
model\u2019s configuration. Use `),cfe=a("code"),rWo=o("from_pretrained()"),tWo=o("to load the model weights."),aWo=l(),ffe=a("p"),nWo=o("Examples:"),sWo=l(),f(cw.$$.fragment),lWo=l(),to=a("div"),f(fw.$$.fragment),iWo=l(),mfe=a("p"),dWo=o("Instantiate one of the model classes of the library (with a image segmentation head) from a pretrained model."),cWo=l(),gn=a("p"),fWo=o("The model class to instantiate is selected based on the "),gfe=a("code"),mWo=o("model_type"),gWo=o(` property of the config object (either
passed as an argument or loaded from `),hfe=a("code"),hWo=o("pretrained_model_name_or_path"),pWo=o(` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),pfe=a("code"),_Wo=o("pretrained_model_name_or_path"),uWo=o(":"),bWo=l(),_fe=a("ul"),Q2=a("li"),ufe=a("strong"),vWo=o("detr"),TWo=o(" \u2014 "),UN=a("a"),FWo=o("DetrForSegmentation"),CWo=o(" (DETR model)"),MWo=l(),H2=a("p"),EWo=o("The model is set in evaluation mode by default using "),bfe=a("code"),yWo=o("model.eval()"),wWo=o(` (so for instance, dropout modules are
deactivated). To train the model, you should first set it back in training mode with `),vfe=a("code"),AWo=o("model.train()"),LWo=l(),Tfe=a("p"),BWo=o("Examples:"),xWo=l(),f(mw.$$.fragment),lke=l(),lc=a("h2"),U2=a("a"),Ffe=a("span"),f(gw.$$.fragment),kWo=l(),Cfe=a("span"),RWo=o("AutoModelForSemanticSegmentation"),ike=l(),ur=a("div"),f(hw.$$.fragment),SWo=l(),ic=a("p"),PWo=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 `),Mfe=a("code"),$Wo=o("from_pretrained()"),IWo=o("class method or the "),Efe=a("code"),jWo=o("from_config()"),DWo=o(`class
method.`),NWo=l(),pw=a("p"),qWo=o("This class cannot be instantiated directly using "),yfe=a("code"),OWo=o("__init__()"),GWo=o(" (throws an error)."),XWo=l(),ft=a("div"),f(_w.$$.fragment),VWo=l(),wfe=a("p"),zWo=o("Instantiates one of the model classes of the library (with a semantic segmentation head) from a configuration."),WWo=l(),dc=a("p"),QWo=o(`Note:
Loading a model from its configuration file does `),Afe=a("strong"),HWo=o("not"),UWo=o(` load the model weights. It only affects the
model\u2019s configuration. Use `),Lfe=a("code"),JWo=o("from_pretrained()"),YWo=o("to load the model weights."),KWo=l(),Bfe=a("p"),ZWo=o("Examples:"),eQo=l(),f(uw.$$.fragment),oQo=l(),ao=a("div"),f(bw.$$.fragment),rQo=l(),xfe=a("p"),tQo=o("Instantiate one of the model classes of the library (with a semantic segmentation head) from a pretrained model."),aQo=l(),hn=a("p"),nQo=o("The model class to instantiate is selected based on the "),kfe=a("code"),sQo=o("model_type"),lQo=o(` property of the config object (either
passed as an argument or loaded from `),Rfe=a("code"),iQo=o("pretrained_model_name_or_path"),dQo=o(` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),Sfe=a("code"),cQo=o("pretrained_model_name_or_path"),fQo=o(":"),mQo=l(),vw=a("ul"),J2=a("li"),Pfe=a("strong"),gQo=o("beit"),hQo=o(" \u2014 "),JN=a("a"),pQo=o("BeitForSemanticSegmentation"),_Qo=o(" (BEiT model)"),uQo=l(),Y2=a("li"),$fe=a("strong"),bQo=o("segformer"),vQo=o(" \u2014 "),YN=a("a"),TQo=o("SegformerForSemanticSegmentation"),FQo=o(" (SegFormer model)"),CQo=l(),K2=a("p"),MQo=o("The model is set in evaluation mode by default using "),Ife=a("code"),EQo=o("model.eval()"),yQo=o(` (so for instance, dropout modules are
deactivated). To train the model, you should first set it back in training mode with `),jfe=a("code"),wQo=o("model.train()"),AQo=l(),Dfe=a("p"),LQo=o("Examples:"),BQo=l(),f(Tw.$$.fragment),dke=l(),cc=a("h2"),Z2=a("a"),Nfe=a("span"),f(Fw.$$.fragment),xQo=l(),qfe=a("span"),kQo=o("AutoModelForInstanceSegmentation"),cke=l(),br=a("div"),f(Cw.$$.fragment),RQo=l(),fc=a("p"),SQo=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 `),Ofe=a("code"),PQo=o("from_pretrained()"),$Qo=o("class method or the "),Gfe=a("code"),IQo=o("from_config()"),jQo=o(`class
method.`),DQo=l(),Mw=a("p"),NQo=o("This class cannot be instantiated directly using "),Xfe=a("code"),qQo=o("__init__()"),OQo=o(" (throws an error)."),GQo=l(),mt=a("div"),f(Ew.$$.fragment),XQo=l(),Vfe=a("p"),VQo=o("Instantiates one of the model classes of the library (with a instance segmentation head) from a configuration."),zQo=l(),mc=a("p"),WQo=o(`Note:
Loading a model from its configuration file does `),zfe=a("strong"),QQo=o("not"),HQo=o(` load the model weights. It only affects the
model\u2019s configuration. Use `),Wfe=a("code"),UQo=o("from_pretrained()"),JQo=o("to load the model weights."),YQo=l(),Qfe=a("p"),KQo=o("Examples:"),ZQo=l(),f(yw.$$.fragment),eHo=l(),no=a("div"),f(ww.$$.fragment),oHo=l(),Hfe=a("p"),rHo=o("Instantiate one of the model classes of the library (with a instance segmentation head) from a pretrained model."),tHo=l(),pn=a("p"),aHo=o("The model class to instantiate is selected based on the "),Ufe=a("code"),nHo=o("model_type"),sHo=o(` property of the config object (either
passed as an argument or loaded from `),Jfe=a("code"),lHo=o("pretrained_model_name_or_path"),iHo=o(` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),Yfe=a("code"),dHo=o("pretrained_model_name_or_path"),cHo=o(":"),fHo=l(),Kfe=a("ul"),ev=a("li"),Zfe=a("strong"),mHo=o("maskformer"),gHo=o(" \u2014 "),KN=a("a"),hHo=o("MaskFormerForInstanceSegmentation"),pHo=o(" (MaskFormer model)"),_Ho=l(),ov=a("p"),uHo=o("The model is set in evaluation mode by default using "),eme=a("code"),bHo=o("model.eval()"),vHo=o(` (so for instance, dropout modules are
deactivated). To train the model, you should first set it back in training mode with `),ome=a("code"),THo=o("model.train()"),FHo=l(),rme=a("p"),CHo=o("Examples:"),MHo=l(),f(Aw.$$.fragment),fke=l(),gc=a("h2"),rv=a("a"),tme=a("span"),f(Lw.$$.fragment),EHo=l(),ame=a("span"),yHo=o("TFAutoModel"),mke=l(),vr=a("div"),f(Bw.$$.fragment),wHo=l(),hc=a("p"),AHo=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 `),nme=a("code"),LHo=o("from_pretrained()"),BHo=o("class method or the "),sme=a("code"),xHo=o("from_config()"),kHo=o(`class
method.`),RHo=l(),xw=a("p"),SHo=o("This class cannot be instantiated directly using "),lme=a("code"),PHo=o("__init__()"),$Ho=o(" (throws an error)."),IHo=l(),gt=a("div"),f(kw.$$.fragment),jHo=l(),ime=a("p"),DHo=o("Instantiates one of the base model classes of the library from a configuration."),NHo=l(),pc=a("p"),qHo=o(`Note:
Loading a model from its configuration file does `),dme=a("strong"),OHo=o("not"),GHo=o(` load the model weights. It only affects the
model\u2019s configuration. Use `),cme=a("code"),XHo=o("from_pretrained()"),VHo=o("to load the model weights."),zHo=l(),fme=a("p"),WHo=o("Examples:"),QHo=l(),f(Rw.$$.fragment),HHo=l(),ho=a("div"),f(Sw.$$.fragment),UHo=l(),mme=a("p"),JHo=o("Instantiate one of the base model classes of the library from a pretrained model."),YHo=l(),_n=a("p"),KHo=o("The model class to instantiate is selected based on the "),gme=a("code"),ZHo=o("model_type"),eUo=o(` property of the config object (either
passed as an argument or loaded from `),hme=a("code"),oUo=o("pretrained_model_name_or_path"),rUo=o(` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),pme=a("code"),tUo=o("pretrained_model_name_or_path"),aUo=o(":"),nUo=l(),B=a("ul"),tv=a("li"),_me=a("strong"),sUo=o("albert"),lUo=o(" \u2014 "),ZN=a("a"),iUo=o("TFAlbertModel"),dUo=o(" (ALBERT model)"),cUo=l(),av=a("li"),ume=a("strong"),fUo=o("bart"),mUo=o(" \u2014 "),eq=a("a"),gUo=o("TFBartModel"),hUo=o(" (BART model)"),pUo=l(),nv=a("li"),bme=a("strong"),_Uo=o("bert"),uUo=o(" \u2014 "),oq=a("a"),bUo=o("TFBertModel"),vUo=o(" (BERT model)"),TUo=l(),sv=a("li"),vme=a("strong"),FUo=o("blenderbot"),CUo=o(" \u2014 "),rq=a("a"),MUo=o("TFBlenderbotModel"),EUo=o(" (Blenderbot model)"),yUo=l(),lv=a("li"),Tme=a("strong"),wUo=o("blenderbot-small"),AUo=o(" \u2014 "),tq=a("a"),LUo=o("TFBlenderbotSmallModel"),BUo=o(" (BlenderbotSmall model)"),xUo=l(),iv=a("li"),Fme=a("strong"),kUo=o("camembert"),RUo=o(" \u2014 "),aq=a("a"),SUo=o("TFCamembertModel"),PUo=o(" (CamemBERT model)"),$Uo=l(),dv=a("li"),Cme=a("strong"),IUo=o("clip"),jUo=o(" \u2014 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model)"),FJo=l(),uv=a("li"),xme=a("strong"),CJo=o("electra"),MJo=o(" \u2014 "),gq=a("a"),EJo=o("TFElectraModel"),yJo=o(" (ELECTRA model)"),wJo=l(),bv=a("li"),kme=a("strong"),AJo=o("flaubert"),LJo=o(" \u2014 "),hq=a("a"),BJo=o("TFFlaubertModel"),xJo=o(" (FlauBERT model)"),kJo=l(),Os=a("li"),Rme=a("strong"),RJo=o("funnel"),SJo=o(" \u2014 "),pq=a("a"),PJo=o("TFFunnelModel"),$Jo=o(" or "),_q=a("a"),IJo=o("TFFunnelBaseModel"),jJo=o(" (Funnel Transformer model)"),DJo=l(),vv=a("li"),Sme=a("strong"),NJo=o("gpt2"),qJo=o(" \u2014 "),uq=a("a"),OJo=o("TFGPT2Model"),GJo=o(" (OpenAI GPT-2 model)"),XJo=l(),Tv=a("li"),Pme=a("strong"),VJo=o("hubert"),zJo=o(" \u2014 "),bq=a("a"),WJo=o("TFHubertModel"),QJo=o(" (Hubert model)"),HJo=l(),Fv=a("li"),$me=a("strong"),UJo=o("layoutlm"),JJo=o(" \u2014 "),vq=a("a"),YJo=o("TFLayoutLMModel"),KJo=o(" (LayoutLM model)"),ZJo=l(),Cv=a("li"),Ime=a("strong"),eYo=o("led"),oYo=o(" \u2014 "),Tq=a("a"),rYo=o("TFLEDModel"),tYo=o(" (LED 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model\u2019s configuration. Use `),ppe=a("code"),Tlr=o("from_pretrained()"),Flr=o("to load the model weights."),Clr=l(),_pe=a("p"),Mlr=o("Examples:"),Elr=l(),f(h6.$$.fragment),ylr=l(),vo=a("div"),f(p6.$$.fragment),wlr=l(),upe=a("p"),Alr=o("Instantiate one of the model classes of the library (with a sequence-to-sequence language modeling head) from a pretrained model."),Llr=l(),Fn=a("p"),Blr=o("The model class to instantiate is selected based on the "),bpe=a("code"),xlr=o("model_type"),klr=o(` property of the config object (either
passed as an argument or loaded from `),vpe=a("code"),Rlr=o("pretrained_model_name_or_path"),Slr=o(` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),Tpe=a("code"),Plr=o("pretrained_model_name_or_path"),$lr=o(":"),Ilr=l(),_e=a("ul"),JT=a("li"),Fpe=a("strong"),jlr=o("bart"),Dlr=o(" \u2014 "),XO=a("a"),Nlr=o("TFBartForConditionalGeneration"),qlr=o(" (BART model)"),Olr=l(),YT=a("li"),Cpe=a("strong"),Glr=o("blenderbot"),Xlr=o(" \u2014 "),VO=a("a"),Vlr=o("TFBlenderbotForConditionalGeneration"),zlr=o(" (Blenderbot model)"),Wlr=l(),KT=a("li"),Mpe=a("strong"),Qlr=o("blenderbot-small"),Hlr=o(" \u2014 "),zO=a("a"),Ulr=o("TFBlenderbotSmallForConditionalGeneration"),Jlr=o(" (BlenderbotSmall model)"),Ylr=l(),ZT=a("li"),Epe=a("strong"),Klr=o("encoder-decoder"),Zlr=o(" \u2014 "),WO=a("a"),eir=o("TFEncoderDecoderModel"),oir=o(" (Encoder decoder model)"),rir=l(),e8=a("li"),ype=a("strong"),tir=o("led"),air=o(" \u2014 "),QO=a("a"),nir=o("TFLEDForConditionalGeneration"),sir=o(" (LED model)"),lir=l(),o8=a("li"),wpe=a("strong"),iir=o("marian"),dir=o(" \u2014 "),HO=a("a"),cir=o("TFMarianMTModel"),fir=o(" (Marian model)"),mir=l(),r8=a("li"),Ape=a("strong"),gir=o("mbart"),hir=o(" \u2014 "),UO=a("a"),pir=o("TFMBartForConditionalGeneration"),_ir=o(" (mBART model)"),uir=l(),t8=a("li"),Lpe=a("strong"),bir=o("mt5"),vir=o(" \u2014 "),JO=a("a"),Tir=o("TFMT5ForConditionalGeneration"),Fir=o(" (mT5 model)"),Cir=l(),a8=a("li"),Bpe=a("strong"),Mir=o("pegasus"),Eir=o(" \u2014 "),YO=a("a"),yir=o("TFPegasusForConditionalGeneration"),wir=o(" (Pegasus model)"),Air=l(),n8=a("li"),xpe=a("strong"),Lir=o("t5"),Bir=o(" \u2014 "),KO=a("a"),xir=o("TFT5ForConditionalGeneration"),kir=o(" (T5 model)"),Rir=l(),kpe=a("p"),Sir=o("Examples:"),Pir=l(),f(_6.$$.fragment),Mke=l(),kc=a("h2"),s8=a("a"),Rpe=a("span"),f(u6.$$.fragment),$ir=l(),Spe=a("span"),Iir=o("TFAutoModelForSequenceClassification"),Eke=l(),yr=a("div"),f(b6.$$.fragment),jir=l(),Rc=a("p"),Dir=o(`This is a generic model class that will be instantiated as one of the model classes of the library (with a sequence classification head) when created
with the `),Ppe=a("code"),Nir=o("from_pretrained()"),qir=o("class method or the "),$pe=a("code"),Oir=o("from_config()"),Gir=o(`class
method.`),Xir=l(),v6=a("p"),Vir=o("This class cannot be instantiated directly using "),Ipe=a("code"),zir=o("__init__()"),Wir=o(" (throws an error)."),Qir=l(),vt=a("div"),f(T6.$$.fragment),Hir=l(),jpe=a("p"),Uir=o("Instantiates one of the model classes of the library (with a sequence classification head) from a configuration."),Jir=l(),Sc=a("p"),Yir=o(`Note:
Loading a model from its configuration file does `),Dpe=a("strong"),Kir=o("not"),Zir=o(` load the model weights. It only affects the
model\u2019s configuration. Use `),Npe=a("code"),edr=o("from_pretrained()"),odr=o("to load the model weights."),rdr=l(),qpe=a("p"),tdr=o("Examples:"),adr=l(),f(F6.$$.fragment),ndr=l(),To=a("div"),f(C6.$$.fragment),sdr=l(),Ope=a("p"),ldr=o("Instantiate one of the model classes of the library (with a sequence classification head) from a pretrained model."),idr=l(),Cn=a("p"),ddr=o("The model class to instantiate is selected based on the "),Gpe=a("code"),cdr=o("model_type"),fdr=o(` property of the config object (either
passed as an argument or loaded from `),Xpe=a("code"),mdr=o("pretrained_model_name_or_path"),gdr=o(` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),Vpe=a("code"),hdr=o("pretrained_model_name_or_path"),pdr=o(":"),_dr=l(),V=a("ul"),l8=a("li"),zpe=a("strong"),udr=o("albert"),bdr=o(" \u2014 "),ZO=a("a"),vdr=o("TFAlbertForSequenceClassification"),Tdr=o(" (ALBERT model)"),Fdr=l(),i8=a("li"),Wpe=a("strong"),Cdr=o("bert"),Mdr=o(" \u2014 "),eG=a("a"),Edr=o("TFBertForSequenceClassification"),ydr=o(" (BERT model)"),wdr=l(),d8=a("li"),Qpe=a("strong"),Adr=o("camembert"),Ldr=o(" \u2014 "),oG=a("a"),Bdr=o("TFCamembertForSequenceClassification"),xdr=o(" (CamemBERT model)"),kdr=l(),c8=a("li"),Hpe=a("strong"),Rdr=o("convbert"),Sdr=o(" \u2014 "),rG=a("a"),Pdr=o("TFConvBertForSequenceClassification"),$dr=o(" (ConvBERT model)"),Idr=l(),f8=a("li"),Upe=a("strong"),jdr=o("ctrl"),Ddr=o(" \u2014 "),tG=a("a"),Ndr=o("TFCTRLForSequenceClassification"),qdr=o(" (CTRL model)"),Odr=l(),m8=a("li"),Jpe=a("strong"),Gdr=o("deberta"),Xdr=o(" \u2014 "),aG=a("a"),Vdr=o("TFDebertaForSequenceClassification"),zdr=o(" (DeBERTa model)"),Wdr=l(),g8=a("li"),Ype=a("strong"),Qdr=o("deberta-v2"),Hdr=o(" \u2014 "),nG=a("a"),Udr=o("TFDebertaV2ForSequenceClassification"),Jdr=o(" (DeBERTa-v2 model)"),Ydr=l(),h8=a("li"),Kpe=a("strong"),Kdr=o("distilbert"),Zdr=o(" \u2014 "),sG=a("a"),ecr=o("TFDistilBertForSequenceClassification"),ocr=o(" (DistilBERT model)"),rcr=l(),p8=a("li"),Zpe=a("strong"),tcr=o("electra"),acr=o(" \u2014 "),lG=a("a"),ncr=o("TFElectraForSequenceClassification"),scr=o(" (ELECTRA model)"),lcr=l(),_8=a("li"),e_e=a("strong"),icr=o("flaubert"),dcr=o(" \u2014 "),iG=a("a"),ccr=o("TFFlaubertForSequenceClassification"),fcr=o(" (FlauBERT model)"),mcr=l(),u8=a("li"),o_e=a("strong"),gcr=o("funnel"),hcr=o(" \u2014 "),dG=a("a"),pcr=o("TFFunnelForSequenceClassification"),_cr=o(" (Funnel Transformer model)"),ucr=l(),b8=a("li"),r_e=a("strong"),bcr=o("gpt2"),vcr=o(" \u2014 "),cG=a("a"),Tcr=o("TFGPT2ForSequenceClassification"),Fcr=o(" (OpenAI GPT-2 model)"),Ccr=l(),v8=a("li"),t_e=a("strong"),Mcr=o("layoutlm"),Ecr=o(" \u2014 "),fG=a("a"),ycr=o("TFLayoutLMForSequenceClassification"),wcr=o(" (LayoutLM model)"),Acr=l(),T8=a("li"),a_e=a("strong"),Lcr=o("longformer"),Bcr=o(" \u2014 "),mG=a("a"),xcr=o("TFLongformerForSequenceClassification"),kcr=o(" (Longformer model)"),Rcr=l(),F8=a("li"),n_e=a("strong"),Scr=o("mobilebert"),Pcr=o(" \u2014 "),gG=a("a"),$cr=o("TFMobileBertForSequenceClassification"),Icr=o(" (MobileBERT model)"),jcr=l(),C8=a("li"),s_e=a("strong"),Dcr=o("mpnet"),Ncr=o(" \u2014 "),hG=a("a"),qcr=o("TFMPNetForSequenceClassification"),Ocr=o(" (MPNet model)"),Gcr=l(),M8=a("li"),l_e=a("strong"),Xcr=o("openai-gpt"),Vcr=o(" \u2014 "),pG=a("a"),zcr=o("TFOpenAIGPTForSequenceClassification"),Wcr=o(" (OpenAI GPT model)"),Qcr=l(),E8=a("li"),i_e=a("strong"),Hcr=o("rembert"),Ucr=o(" \u2014 "),_G=a("a"),Jcr=o("TFRemBertForSequenceClassification"),Ycr=o(" (RemBERT model)"),Kcr=l(),y8=a("li"),d_e=a("strong"),Zcr=o("roberta"),efr=o(" \u2014 "),uG=a("a"),ofr=o("TFRobertaForSequenceClassification"),rfr=o(" (RoBERTa model)"),tfr=l(),w8=a("li"),c_e=a("strong"),afr=o("roformer"),nfr=o(" \u2014 "),bG=a("a"),sfr=o("TFRoFormerForSequenceClassification"),lfr=o(" (RoFormer model)"),ifr=l(),A8=a("li"),f_e=a("strong"),dfr=o("tapas"),cfr=o(" \u2014 "),vG=a("a"),ffr=o("TFTapasForSequenceClassification"),mfr=o(" (TAPAS model)"),gfr=l(),L8=a("li"),m_e=a("strong"),hfr=o("transfo-xl"),pfr=o(" \u2014 "),TG=a("a"),_fr=o("TFTransfoXLForSequenceClassification"),ufr=o(" (Transformer-XL model)"),bfr=l(),B8=a("li"),g_e=a("strong"),vfr=o("xlm"),Tfr=o(" \u2014 "),FG=a("a"),Ffr=o("TFXLMForSequenceClassification"),Cfr=o(" (XLM model)"),Mfr=l(),x8=a("li"),h_e=a("strong"),Efr=o("xlm-roberta"),yfr=o(" \u2014 "),CG=a("a"),wfr=o("TFXLMRobertaForSequenceClassification"),Afr=o(" (XLM-RoBERTa model)"),Lfr=l(),k8=a("li"),p_e=a("strong"),Bfr=o("xlnet"),xfr=o(" \u2014 "),MG=a("a"),kfr=o("TFXLNetForSequenceClassification"),Rfr=o(" (XLNet model)"),Sfr=l(),__e=a("p"),Pfr=o("Examples:"),$fr=l(),f(M6.$$.fragment),yke=l(),Pc=a("h2"),R8=a("a"),u_e=a("span"),f(E6.$$.fragment),Ifr=l(),b_e=a("span"),jfr=o("TFAutoModelForMultipleChoice"),wke=l(),wr=a("div"),f(y6.$$.fragment),Dfr=l(),$c=a("p"),Nfr=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 `),v_e=a("code"),qfr=o("from_pretrained()"),Ofr=o("class method or the "),T_e=a("code"),Gfr=o("from_config()"),Xfr=o(`class
method.`),Vfr=l(),w6=a("p"),zfr=o("This class cannot be instantiated directly using "),F_e=a("code"),Wfr=o("__init__()"),Qfr=o(" (throws an error)."),Hfr=l(),Tt=a("div"),f(A6.$$.fragment),Ufr=l(),C_e=a("p"),Jfr=o("Instantiates one of the model classes of the library (with a multiple choice head) from a configuration."),Yfr=l(),Ic=a("p"),Kfr=o(`Note:
Loading a model from its configuration file does `),M_e=a("strong"),Zfr=o("not"),emr=o(` load the model weights. It only affects the
model\u2019s configuration. Use `),E_e=a("code"),omr=o("from_pretrained()"),rmr=o("to load the model weights."),tmr=l(),y_e=a("p"),amr=o("Examples:"),nmr=l(),f(L6.$$.fragment),smr=l(),Fo=a("div"),f(B6.$$.fragment),lmr=l(),w_e=a("p"),imr=o("Instantiate one of the model classes of the library (with a multiple choice head) from a pretrained model."),dmr=l(),Mn=a("p"),cmr=o("The model class to instantiate is selected based on the "),A_e=a("code"),fmr=o("model_type"),mmr=o(` property of the config object (either
passed as an argument or loaded from `),L_e=a("code"),gmr=o("pretrained_model_name_or_path"),hmr=o(` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),B_e=a("code"),pmr=o("pretrained_model_name_or_path"),_mr=o(":"),umr=l(),te=a("ul"),S8=a("li"),x_e=a("strong"),bmr=o("albert"),vmr=o(" \u2014 "),EG=a("a"),Tmr=o("TFAlbertForMultipleChoice"),Fmr=o(" (ALBERT model)"),Cmr=l(),P8=a("li"),k_e=a("strong"),Mmr=o("bert"),Emr=o(" \u2014 "),yG=a("a"),ymr=o("TFBertForMultipleChoice"),wmr=o(" (BERT model)"),Amr=l(),$8=a("li"),R_e=a("strong"),Lmr=o("camembert"),Bmr=o(" \u2014 "),wG=a("a"),xmr=o("TFCamembertForMultipleChoice"),kmr=o(" (CamemBERT model)"),Rmr=l(),I8=a("li"),S_e=a("strong"),Smr=o("convbert"),Pmr=o(" \u2014 "),AG=a("a"),$mr=o("TFConvBertForMultipleChoice"),Imr=o(" (ConvBERT model)"),jmr=l(),j8=a("li"),P_e=a("strong"),Dmr=o("distilbert"),Nmr=o(" \u2014 "),LG=a("a"),qmr=o("TFDistilBertForMultipleChoice"),Omr=o(" (DistilBERT model)"),Gmr=l(),D8=a("li"),$_e=a("strong"),Xmr=o("electra"),Vmr=o(" \u2014 "),BG=a("a"),zmr=o("TFElectraForMultipleChoice"),Wmr=o(" (ELECTRA model)"),Qmr=l(),N8=a("li"),I_e=a("strong"),Hmr=o("flaubert"),Umr=o(" \u2014 "),xG=a("a"),Jmr=o("TFFlaubertForMultipleChoice"),Ymr=o(" (FlauBERT model)"),Kmr=l(),q8=a("li"),j_e=a("strong"),Zmr=o("funnel"),egr=o(" \u2014 "),kG=a("a"),ogr=o("TFFunnelForMultipleChoice"),rgr=o(" (Funnel Transformer model)"),tgr=l(),O8=a("li"),D_e=a("strong"),agr=o("longformer"),ngr=o(" \u2014 "),RG=a("a"),sgr=o("TFLongformerForMultipleChoice"),lgr=o(" (Longformer model)"),igr=l(),G8=a("li"),N_e=a("strong"),dgr=o("mobilebert"),cgr=o(" \u2014 "),SG=a("a"),fgr=o("TFMobileBertForMultipleChoice"),mgr=o(" (MobileBERT model)"),ggr=l(),X8=a("li"),q_e=a("strong"),hgr=o("mpnet"),pgr=o(" \u2014 "),PG=a("a"),_gr=o("TFMPNetForMultipleChoice"),ugr=o(" (MPNet model)"),bgr=l(),V8=a("li"),O_e=a("strong"),vgr=o("rembert"),Tgr=o(" \u2014 "),$G=a("a"),Fgr=o("TFRemBertForMultipleChoice"),Cgr=o(" (RemBERT model)"),Mgr=l(),z8=a("li"),G_e=a("strong"),Egr=o("roberta"),ygr=o(" \u2014 "),IG=a("a"),wgr=o("TFRobertaForMultipleChoice"),Agr=o(" (RoBERTa model)"),Lgr=l(),W8=a("li"),X_e=a("strong"),Bgr=o("roformer"),xgr=o(" \u2014 "),jG=a("a"),kgr=o("TFRoFormerForMultipleChoice"),Rgr=o(" (RoFormer model)"),Sgr=l(),Q8=a("li"),V_e=a("strong"),Pgr=o("xlm"),$gr=o(" \u2014 "),DG=a("a"),Igr=o("TFXLMForMultipleChoice"),jgr=o(" (XLM model)"),Dgr=l(),H8=a("li"),z_e=a("strong"),Ngr=o("xlm-roberta"),qgr=o(" \u2014 "),NG=a("a"),Ogr=o("TFXLMRobertaForMultipleChoice"),Ggr=o(" (XLM-RoBERTa model)"),Xgr=l(),U8=a("li"),W_e=a("strong"),Vgr=o("xlnet"),zgr=o(" \u2014 "),qG=a("a"),Wgr=o("TFXLNetForMultipleChoice"),Qgr=o(" (XLNet model)"),Hgr=l(),Q_e=a("p"),Ugr=o("Examples:"),Jgr=l(),f(x6.$$.fragment),Ake=l(),jc=a("h2"),J8=a("a"),H_e=a("span"),f(k6.$$.fragment),Ygr=l(),U_e=a("span"),Kgr=o("TFAutoModelForTableQuestionAnswering"),Lke=l(),Ar=a("div"),f(R6.$$.fragment),Zgr=l(),Dc=a("p"),ehr=o(`This is a generic model class that will be instantiated as one of the model classes of the library (with a table question answering head) when created
with the `),J_e=a("code"),ohr=o("from_pretrained()"),rhr=o("class method or the "),Y_e=a("code"),thr=o("from_config()"),ahr=o(`class
method.`),nhr=l(),S6=a("p"),shr=o("This class cannot be instantiated directly using "),K_e=a("code"),lhr=o("__init__()"),ihr=o(" (throws an error)."),dhr=l(),Ft=a("div"),f(P6.$$.fragment),chr=l(),Z_e=a("p"),fhr=o("Instantiates one of the model classes of the library (with a table question answering head) from a configuration."),mhr=l(),Nc=a("p"),ghr=o(`Note:
Loading a model from its configuration file does `),eue=a("strong"),hhr=o("not"),phr=o(` load the model weights. It only affects the
model\u2019s configuration. Use `),oue=a("code"),_hr=o("from_pretrained()"),uhr=o("to load the model weights."),bhr=l(),rue=a("p"),vhr=o("Examples:"),Thr=l(),f($6.$$.fragment),Fhr=l(),Co=a("div"),f(I6.$$.fragment),Chr=l(),tue=a("p"),Mhr=o("Instantiate one of the model classes of the library (with a table question answering head) from a pretrained model."),Ehr=l(),En=a("p"),yhr=o("The model class to instantiate is selected based on the "),aue=a("code"),whr=o("model_type"),Ahr=o(` property of the config object (either
passed as an argument or loaded from `),nue=a("code"),Lhr=o("pretrained_model_name_or_path"),Bhr=o(` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),sue=a("code"),xhr=o("pretrained_model_name_or_path"),khr=o(":"),Rhr=l(),lue=a("ul"),Y8=a("li"),iue=a("strong"),Shr=o("tapas"),Phr=o(" \u2014 "),OG=a("a"),$hr=o("TFTapasForQuestionAnswering"),Ihr=o(" (TAPAS model)"),jhr=l(),due=a("p"),Dhr=o("Examples:"),Nhr=l(),f(j6.$$.fragment),Bke=l(),qc=a("h2"),K8=a("a"),cue=a("span"),f(D6.$$.fragment),qhr=l(),fue=a("span"),Ohr=o("TFAutoModelForTokenClassification"),xke=l(),Lr=a("div"),f(N6.$$.fragment),Ghr=l(),Oc=a("p"),Xhr=o(`This is a generic model class that will be instantiated as one of the model classes of the library (with a token classification head) when created
with the `),mue=a("code"),Vhr=o("from_pretrained()"),zhr=o("class method or the "),gue=a("code"),Whr=o("from_config()"),Qhr=o(`class
method.`),Hhr=l(),q6=a("p"),Uhr=o("This class cannot be instantiated directly using "),hue=a("code"),Jhr=o("__init__()"),Yhr=o(" (throws an error)."),Khr=l(),Ct=a("div"),f(O6.$$.fragment),Zhr=l(),pue=a("p"),epr=o("Instantiates one of the model classes of the library (with a token classification head) from a configuration."),opr=l(),Gc=a("p"),rpr=o(`Note:
Loading a model from its configuration file does `),_ue=a("strong"),tpr=o("not"),apr=o(` load the model weights. It only affects the
model\u2019s configuration. Use `),uue=a("code"),npr=o("from_pretrained()"),spr=o("to load the model weights."),lpr=l(),bue=a("p"),ipr=o("Examples:"),dpr=l(),f(G6.$$.fragment),cpr=l(),Mo=a("div"),f(X6.$$.fragment),fpr=l(),vue=a("p"),mpr=o("Instantiate one of the model classes of the library (with a token classification head) from a pretrained model."),gpr=l(),yn=a("p"),hpr=o("The model class to instantiate is selected based on the "),Tue=a("code"),ppr=o("model_type"),_pr=o(` property of the config object (either
passed as an argument or loaded from `),Fue=a("code"),upr=o("pretrained_model_name_or_path"),bpr=o(` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),Cue=a("code"),vpr=o("pretrained_model_name_or_path"),Tpr=o(":"),Fpr=l(),K=a("ul"),Z8=a("li"),Mue=a("strong"),Cpr=o("albert"),Mpr=o(" \u2014 "),GG=a("a"),Epr=o("TFAlbertForTokenClassification"),ypr=o(" (ALBERT model)"),wpr=l(),eF=a("li"),Eue=a("strong"),Apr=o("bert"),Lpr=o(" \u2014 "),XG=a("a"),Bpr=o("TFBertForTokenClassification"),xpr=o(" (BERT model)"),kpr=l(),oF=a("li"),yue=a("strong"),Rpr=o("camembert"),Spr=o(" \u2014 "),VG=a("a"),Ppr=o("TFCamembertForTokenClassification"),$pr=o(" (CamemBERT model)"),Ipr=l(),rF=a("li"),wue=a("strong"),jpr=o("convbert"),Dpr=o(" \u2014 "),zG=a("a"),Npr=o("TFConvBertForTokenClassification"),qpr=o(" (ConvBERT model)"),Opr=l(),tF=a("li"),Aue=a("strong"),Gpr=o("deberta"),Xpr=o(" \u2014 "),WG=a("a"),Vpr=o("TFDebertaForTokenClassification"),zpr=o(" (DeBERTa model)"),Wpr=l(),aF=a("li"),Lue=a("strong"),Qpr=o("deberta-v2"),Hpr=o(" \u2014 "),QG=a("a"),Upr=o("TFDebertaV2ForTokenClassification"),Jpr=o(" (DeBERTa-v2 model)"),Ypr=l(),nF=a("li"),Bue=a("strong"),Kpr=o("distilbert"),Zpr=o(" \u2014 "),HG=a("a"),e_r=o("TFDistilBertForTokenClassification"),o_r=o(" (DistilBERT model)"),r_r=l(),sF=a("li"),xue=a("strong"),t_r=o("electra"),a_r=o(" \u2014 "),UG=a("a"),n_r=o("TFElectraForTokenClassification"),s_r=o(" (ELECTRA model)"),l_r=l(),lF=a("li"),kue=a("strong"),i_r=o("flaubert"),d_r=o(" \u2014 "),JG=a("a"),c_r=o("TFFlaubertForTokenClassification"),f_r=o(" (FlauBERT model)"),m_r=l(),iF=a("li"),Rue=a("strong"),g_r=o("funnel"),h_r=o(" \u2014 "),YG=a("a"),p_r=o("TFFunnelForTokenClassification"),__r=o(" (Funnel Transformer model)"),u_r=l(),dF=a("li"),Sue=a("strong"),b_r=o("layoutlm"),v_r=o(" \u2014 "),KG=a("a"),T_r=o("TFLayoutLMForTokenClassification"),F_r=o(" (LayoutLM model)"),C_r=l(),cF=a("li"),Pue=a("strong"),M_r=o("longformer"),E_r=o(" \u2014 "),ZG=a("a"),y_r=o("TFLongformerForTokenClassification"),w_r=o(" (Longformer model)"),A_r=l(),fF=a("li"),$ue=a("strong"),L_r=o("mobilebert"),B_r=o(" \u2014 "),eX=a("a"),x_r=o("TFMobileBertForTokenClassification"),k_r=o(" (MobileBERT model)"),R_r=l(),mF=a("li"),Iue=a("strong"),S_r=o("mpnet"),P_r=o(" \u2014 "),oX=a("a"),$_r=o("TFMPNetForTokenClassification"),I_r=o(" (MPNet model)"),j_r=l(),gF=a("li"),jue=a("strong"),D_r=o("rembert"),N_r=o(" \u2014 "),rX=a("a"),q_r=o("TFRemBertForTokenClassification"),O_r=o(" (RemBERT model)"),G_r=l(),hF=a("li"),Due=a("strong"),X_r=o("roberta"),V_r=o(" \u2014 "),tX=a("a"),z_r=o("TFRobertaForTokenClassification"),W_r=o(" (RoBERTa model)"),Q_r=l(),pF=a("li"),Nue=a("strong"),H_r=o("roformer"),U_r=o(" \u2014 "),aX=a("a"),J_r=o("TFRoFormerForTokenClassification"),Y_r=o(" (RoFormer model)"),K_r=l(),_F=a("li"),que=a("strong"),Z_r=o("xlm"),eur=o(" \u2014 "),nX=a("a"),our=o("TFXLMForTokenClassification"),rur=o(" (XLM model)"),tur=l(),uF=a("li"),Oue=a("strong"),aur=o("xlm-roberta"),nur=o(" \u2014 "),sX=a("a"),sur=o("TFXLMRobertaForTokenClassification"),lur=o(" (XLM-RoBERTa model)"),iur=l(),bF=a("li"),Gue=a("strong"),dur=o("xlnet"),cur=o(" \u2014 "),lX=a("a"),fur=o("TFXLNetForTokenClassification"),mur=o(" (XLNet model)"),gur=l(),Xue=a("p"),hur=o("Examples:"),pur=l(),f(V6.$$.fragment),kke=l(),Xc=a("h2"),vF=a("a"),Vue=a("span"),f(z6.$$.fragment),_ur=l(),zue=a("span"),uur=o("TFAutoModelForQuestionAnswering"),Rke=l(),Br=a("div"),f(W6.$$.fragment),bur=l(),Vc=a("p"),vur=o(`This is a generic model class that will be instantiated as one of the model classes of the library (with a question answering head) when created
with the `),Wue=a("code"),Tur=o("from_pretrained()"),Fur=o("class method or the "),Que=a("code"),Cur=o("from_config()"),Mur=o(`class
method.`),Eur=l(),Q6=a("p"),yur=o("This class cannot be instantiated directly using "),Hue=a("code"),wur=o("__init__()"),Aur=o(" (throws an error)."),Lur=l(),Mt=a("div"),f(H6.$$.fragment),Bur=l(),Uue=a("p"),xur=o("Instantiates one of the model classes of the library (with a question answering head) from a configuration."),kur=l(),zc=a("p"),Rur=o(`Note:
Loading a model from its configuration file does `),Jue=a("strong"),Sur=o("not"),Pur=o(` load the model weights. It only affects the
model\u2019s configuration. Use `),Yue=a("code"),$ur=o("from_pretrained()"),Iur=o("to load the model weights."),jur=l(),Kue=a("p"),Dur=o("Examples:"),Nur=l(),f(U6.$$.fragment),qur=l(),Eo=a("div"),f(J6.$$.fragment),Our=l(),Zue=a("p"),Gur=o("Instantiate one of the model classes of the library (with a question answering head) from a pretrained model."),Xur=l(),wn=a("p"),Vur=o("The model class to instantiate is selected based on the "),e1e=a("code"),zur=o("model_type"),Wur=o(` property of the config object (either
passed as an argument or loaded from `),o1e=a("code"),Qur=o("pretrained_model_name_or_path"),Hur=o(` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),r1e=a("code"),Uur=o("pretrained_model_name_or_path"),Jur=o(":"),Yur=l(),Z=a("ul"),TF=a("li"),t1e=a("strong"),Kur=o("albert"),Zur=o(" \u2014 "),iX=a("a"),e1r=o("TFAlbertForQuestionAnswering"),o1r=o(" (ALBERT model)"),r1r=l(),FF=a("li"),a1e=a("strong"),t1r=o("bert"),a1r=o(" \u2014 "),dX=a("a"),n1r=o("TFBertForQuestionAnswering"),s1r=o(" (BERT model)"),l1r=l(),CF=a("li"),n1e=a("strong"),i1r=o("camembert"),d1r=o(" \u2014 "),cX=a("a"),c1r=o("TFCamembertForQuestionAnswering"),f1r=o(" (CamemBERT model)"),m1r=l(),MF=a("li"),s1e=a("strong"),g1r=o("convbert"),h1r=o(" \u2014 "),fX=a("a"),p1r=o("TFConvBertForQuestionAnswering"),_1r=o(" (ConvBERT model)"),u1r=l(),EF=a("li"),l1e=a("strong"),b1r=o("deberta"),v1r=o(" \u2014 "),mX=a("a"),T1r=o("TFDebertaForQuestionAnswering"),F1r=o(" (DeBERTa model)"),C1r=l(),yF=a("li"),i1e=a("strong"),M1r=o("deberta-v2"),E1r=o(" \u2014 "),gX=a("a"),y1r=o("TFDebertaV2ForQuestionAnswering"),w1r=o(" (DeBERTa-v2 model)"),A1r=l(),wF=a("li"),d1e=a("strong"),L1r=o("distilbert"),B1r=o(" \u2014 "),hX=a("a"),x1r=o("TFDistilBertForQuestionAnswering"),k1r=o(" (DistilBERT model)"),R1r=l(),AF=a("li"),c1e=a("strong"),S1r=o("electra"),P1r=o(" \u2014 "),pX=a("a"),$1r=o("TFElectraForQuestionAnswering"),I1r=o(" (ELECTRA model)"),j1r=l(),LF=a("li"),f1e=a("strong"),D1r=o("flaubert"),N1r=o(" \u2014 "),_X=a("a"),q1r=o("TFFlaubertForQuestionAnsweringSimple"),O1r=o(" (FlauBERT model)"),G1r=l(),BF=a("li"),m1e=a("strong"),X1r=o("funnel"),V1r=o(" \u2014 "),uX=a("a"),z1r=o("TFFunnelForQuestionAnswering"),W1r=o(" (Funnel Transformer model)"),Q1r=l(),xF=a("li"),g1e=a("strong"),H1r=o("longformer"),U1r=o(" \u2014 "),bX=a("a"),J1r=o("TFLongformerForQuestionAnswering"),Y1r=o(" (Longformer model)"),K1r=l(),kF=a("li"),h1e=a("strong"),Z1r=o("mobilebert"),ebr=o(" \u2014 "),vX=a("a"),obr=o("TFMobileBertForQuestionAnswering"),rbr=o(" (MobileBERT model)"),tbr=l(),RF=a("li"),p1e=a("strong"),abr=o("mpnet"),nbr=o(" \u2014 "),TX=a("a"),sbr=o("TFMPNetForQuestionAnswering"),lbr=o(" (MPNet model)"),ibr=l(),SF=a("li"),_1e=a("strong"),dbr=o("rembert"),cbr=o(" \u2014 "),FX=a("a"),fbr=o("TFRemBertForQuestionAnswering"),mbr=o(" (RemBERT model)"),gbr=l(),PF=a("li"),u1e=a("strong"),hbr=o("roberta"),pbr=o(" \u2014 "),CX=a("a"),_br=o("TFRobertaForQuestionAnswering"),ubr=o(" (RoBERTa model)"),bbr=l(),$F=a("li"),b1e=a("strong"),vbr=o("roformer"),Tbr=o(" \u2014 "),MX=a("a"),Fbr=o("TFRoFormerForQuestionAnswering"),Cbr=o(" (RoFormer model)"),Mbr=l(),IF=a("li"),v1e=a("strong"),Ebr=o("xlm"),ybr=o(" \u2014 "),EX=a("a"),wbr=o("TFXLMForQuestionAnsweringSimple"),Abr=o(" (XLM model)"),Lbr=l(),jF=a("li"),T1e=a("strong"),Bbr=o("xlm-roberta"),xbr=o(" \u2014 "),yX=a("a"),kbr=o("TFXLMRobertaForQuestionAnswering"),Rbr=o(" (XLM-RoBERTa model)"),Sbr=l(),DF=a("li"),F1e=a("strong"),Pbr=o("xlnet"),$br=o(" \u2014 "),wX=a("a"),Ibr=o("TFXLNetForQuestionAnsweringSimple"),jbr=o(" (XLNet model)"),Dbr=l(),C1e=a("p"),Nbr=o("Examples:"),qbr=l(),f(Y6.$$.fragment),Ske=l(),Wc=a("h2"),NF=a("a"),M1e=a("span"),f(K6.$$.fragment),Obr=l(),E1e=a("span"),Gbr=o("TFAutoModelForVision2Seq"),Pke=l(),xr=a("div"),f(Z6.$$.fragment),Xbr=l(),Qc=a("p"),Vbr=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
with the `),y1e=a("code"),zbr=o("from_pretrained()"),Wbr=o("class method or the "),w1e=a("code"),Qbr=o("from_config()"),Hbr=o(`class
method.`),Ubr=l(),eA=a("p"),Jbr=o("This class cannot be instantiated directly using "),A1e=a("code"),Ybr=o("__init__()"),Kbr=o(" (throws an error)."),Zbr=l(),Et=a("div"),f(oA.$$.fragment),e3r=l(),L1e=a("p"),o3r=o("Instantiates one of the model classes of the library (with a vision-to-text modeling head) from a configuration."),r3r=l(),Hc=a("p"),t3r=o(`Note:
Loading a model from its configuration file does `),B1e=a("strong"),a3r=o("not"),n3r=o(` load the model weights. It only affects the
model\u2019s configuration. Use `),x1e=a("code"),s3r=o("from_pretrained()"),l3r=o("to load the model weights."),i3r=l(),k1e=a("p"),d3r=o("Examples:"),c3r=l(),f(rA.$$.fragment),f3r=l(),yo=a("div"),f(tA.$$.fragment),m3r=l(),R1e=a("p"),g3r=o("Instantiate one of the model classes of the library (with a vision-to-text modeling head) from a pretrained model."),h3r=l(),An=a("p"),p3r=o("The model class to instantiate is selected based on the "),S1e=a("code"),_3r=o("model_type"),u3r=o(` property of the config object (either
passed as an argument or loaded from `),P1e=a("code"),b3r=o("pretrained_model_name_or_path"),v3r=o(` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),$1e=a("code"),T3r=o("pretrained_model_name_or_path"),F3r=o(":"),C3r=l(),I1e=a("ul"),qF=a("li"),j1e=a("strong"),M3r=o("vision-encoder-decoder"),E3r=o(" \u2014 "),AX=a("a"),y3r=o("TFVisionEncoderDecoderModel"),w3r=o(" (Vision Encoder decoder model)"),A3r=l(),D1e=a("p"),L3r=o("Examples:"),B3r=l(),f(aA.$$.fragment),$ke=l(),Uc=a("h2"),OF=a("a"),N1e=a("span"),f(nA.$$.fragment),x3r=l(),q1e=a("span"),k3r=o("TFAutoModelForSpeechSeq2Seq"),Ike=l(),kr=a("div"),f(sA.$$.fragment),R3r=l(),Jc=a("p"),S3r=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 `),O1e=a("code"),P3r=o("from_pretrained()"),$3r=o("class method or the "),G1e=a("code"),I3r=o("from_config()"),j3r=o(`class
method.`),D3r=l(),lA=a("p"),N3r=o("This class cannot be instantiated directly using "),X1e=a("code"),q3r=o("__init__()"),O3r=o(" (throws an error)."),G3r=l(),yt=a("div"),f(iA.$$.fragment),X3r=l(),V1e=a("p"),V3r=o("Instantiates one of the model classes of the library (with a sequence-to-sequence speech-to-text modeling head) from a configuration."),z3r=l(),Yc=a("p"),W3r=o(`Note:
Loading a model from its configuration file does `),z1e=a("strong"),Q3r=o("not"),H3r=o(` load the model weights. It only affects the
model\u2019s configuration. Use `),W1e=a("code"),U3r=o("from_pretrained()"),J3r=o("to load the model weights."),Y3r=l(),Q1e=a("p"),K3r=o("Examples:"),Z3r=l(),f(dA.$$.fragment),e5r=l(),wo=a("div"),f(cA.$$.fragment),o5r=l(),H1e=a("p"),r5r=o("Instantiate one of the model classes of the library (with a sequence-to-sequence speech-to-text modeling head) from a pretrained model."),t5r=l(),Ln=a("p"),a5r=o("The model class to instantiate is selected based on the "),U1e=a("code"),n5r=o("model_type"),s5r=o(` property of the config object (either
passed as an argument or loaded from `),J1e=a("code"),l5r=o("pretrained_model_name_or_path"),i5r=o(` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),Y1e=a("code"),d5r=o("pretrained_model_name_or_path"),c5r=o(":"),f5r=l(),K1e=a("ul"),GF=a("li"),Z1e=a("strong"),m5r=o("speech_to_text"),g5r=o(" \u2014 "),LX=a("a"),h5r=o("TFSpeech2TextForConditionalGeneration"),p5r=o(" (Speech2Text model)"),_5r=l(),ebe=a("p"),u5r=o("Examples:"),b5r=l(),f(fA.$$.fragment),jke=l(),Kc=a("h2"),XF=a("a"),obe=a("span"),f(mA.$$.fragment),v5r=l(),rbe=a("span"),T5r=o("FlaxAutoModel"),Dke=l(),Rr=a("div"),f(gA.$$.fragment),F5r=l(),Zc=a("p"),C5r=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 `),tbe=a("code"),M5r=o("from_pretrained()"),E5r=o("class method or the "),abe=a("code"),y5r=o("from_config()"),w5r=o(`class
method.`),A5r=l(),hA=a("p"),L5r=o("This class cannot be instantiated directly using "),nbe=a("code"),B5r=o("__init__()"),x5r=o(" (throws an error)."),k5r=l(),wt=a("div"),f(pA.$$.fragment),R5r=l(),sbe=a("p"),S5r=o("Instantiates one of the base model classes of the library from a configuration."),P5r=l(),ef=a("p"),$5r=o(`Note:
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model\u2019s configuration. Use `),ibe=a("code"),D5r=o("from_pretrained()"),N5r=o("to load the model weights."),q5r=l(),dbe=a("p"),O5r=o("Examples:"),G5r=l(),f(_A.$$.fragment),X5r=l(),Ao=a("div"),f(uA.$$.fragment),V5r=l(),cbe=a("p"),z5r=o("Instantiate one of the base model classes of the library from a pretrained model."),W5r=l(),Bn=a("p"),Q5r=o("The model class to instantiate is selected based on the "),fbe=a("code"),H5r=o("model_type"),U5r=o(` property of the config object (either
passed as an argument or loaded from `),mbe=a("code"),J5r=o("pretrained_model_name_or_path"),Y5r=o(` if possible), or when it\u2019s missing, by
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are supplying to the `),Ni=n(iRe,"CODE",{});var cBr=s(Ni);QM=r(cBr,"from_pretrained()"),cBr.forEach(t),jf=r(iRe,` method. AutoClasses are here to do this job for you so that you
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`),Vn=n(HC,"A",{href:!0});var gBr=s(Vn);JM=r(gBr,"AutoTokenizer"),gBr.forEach(t),Gi=r(HC," will directly create a class of the relevant architecture. For instance"),HC.forEach(t),Df=i(d),m(qa.$$.fragment,d),fo=i(d),pe=n(d,"P",{});var dRe=s(pe);qL=r(dRe,"will create a model that is an instance of "),Xi=n(dRe,"A",{href:!0});var hBr=s(Xi);OL=r(hBr,"BertModel"),hBr.forEach(t),GL=r(dRe,"."),dRe.forEach(t),zo=i(d),Oa=n(d,"P",{});var cRe=s(Oa);XL=r(cRe,"There is one class of "),Nf=n(cRe,"CODE",{});var pBr=s(Nf);VL=r(pBr,"AutoModel"),pBr.forEach(t),TPe=r(cRe," for each task, and for each backend (PyTorch, TensorFlow, or Flax)."),cRe.forEach(t),sxe=i(d),Vi=n(d,"H2",{class:!0});var fRe=s(Vi);qf=n(fRe,"A",{id:!0,class:!0,href:!0});var _Br=s(qf);hQ=n(_Br,"SPAN",{});var uBr=s(hQ);m(YM.$$.fragment,uBr),uBr.forEach(t),_Br.forEach(t),FPe=i(fRe),pQ=n(fRe,"SPAN",{});var bBr=s(pQ);CPe=r(bBr,"Extending the Auto Classes"),bBr.forEach(t),fRe.forEach(t),lxe=i(d),zn=n(d,"P",{});var Cz=s(zn);MPe=r(Cz,`Each of the auto classes has a method to be extended with your custom classes. For instance, if you have defined a
custom class of model `),_Q=n(Cz,"CODE",{});var vBr=s(_Q);EPe=r(vBr,"NewModel"),vBr.forEach(t),yPe=r(Cz,", make sure you have a "),uQ=n(Cz,"CODE",{});var TBr=s(uQ);wPe=r(TBr,"NewModelConfig"),TBr.forEach(t),APe=r(Cz,` then you can add those to the auto
classes like this:`),Cz.forEach(t),ixe=i(d),m(KM.$$.fragment,d),dxe=i(d),zL=n(d,"P",{});var FBr=s(zL);LPe=r(FBr,"You will then be able to use the auto classes like you would usually do!"),FBr.forEach(t),cxe=i(d),m(Of.$$.fragment,d),fxe=i(d),zi=n(d,"H2",{class:!0});var mRe=s(zi);Gf=n(mRe,"A",{id:!0,class:!0,href:!0});var CBr=s(Gf);bQ=n(CBr,"SPAN",{});var MBr=s(bQ);m(ZM.$$.fragment,MBr),MBr.forEach(t),CBr.forEach(t),BPe=i(mRe),vQ=n(mRe,"SPAN",{});var EBr=s(vQ);xPe=r(EBr,"AutoConfig"),EBr.forEach(t),mRe.forEach(t),mxe=i(d),Wo=n(d,"DIV",{class:!0});var Gs=s(Wo);m(e4.$$.fragment,Gs),kPe=i(Gs),o4=n(Gs,"P",{});var gRe=s(o4);RPe=r(gRe,`This is a generic configuration class that will be instantiated as one of the configuration classes of the library
when created with the `),WL=n(gRe,"A",{href:!0});var yBr=s(WL);SPe=r(yBr,"from_pretrained()"),yBr.forEach(t),PPe=r(gRe," class method."),gRe.forEach(t),$Pe=i(Gs),r4=n(Gs,"P",{});var hRe=s(r4);IPe=r(hRe,"This class cannot be instantiated directly using "),TQ=n(hRe,"CODE",{});var wBr=s(TQ);jPe=r(wBr,"__init__()"),wBr.forEach(t),DPe=r(hRe," (throws an error)."),hRe.forEach(t),NPe=i(Gs),mo=n(Gs,"DIV",{class:!0});var ga=s(mo);m(t4.$$.fragment,ga),qPe=i(ga),FQ=n(ga,"P",{});var ABr=s(FQ);OPe=r(ABr,"Instantiate one of the configuration classes of the library from a pretrained model configuration."),ABr.forEach(t),GPe=i(ga),Wi=n(ga,"P",{});var Mz=s(Wi);XPe=r(Mz,"The configuration class to instantiate is selected based on the "),CQ=n(Mz,"CODE",{});var LBr=s(CQ);VPe=r(LBr,"model_type"),LBr.forEach(t),zPe=r(Mz,` property of the config object that
is loaded, or when it\u2019s missing, by falling back to using pattern matching on `),MQ=n(Mz,"CODE",{});var BBr=s(MQ);WPe=r(BBr,"pretrained_model_name_or_path"),BBr.forEach(t),QPe=r(Mz,":"),Mz.forEach(t),HPe=i(ga),v=n(ga,"UL",{});var T=s(v);Xf=n(T,"LI",{});var aTe=s(Xf);EQ=n(aTe,"STRONG",{});var xBr=s(EQ);UPe=r(xBr,"albert"),xBr.forEach(t),JPe=r(aTe," \u2014 "),QL=n(aTe,"A",{href:!0});var kBr=s(QL);YPe=r(kBr,"AlbertConfig"),kBr.forEach(t),KPe=r(aTe," (ALBERT model)"),aTe.forEach(t),ZPe=i(T),Vf=n(T,"LI",{});var nTe=s(Vf);yQ=n(nTe,"STRONG",{});var RBr=s(yQ);e$e=r(RBr,"bart"),RBr.forEach(t),o$e=r(nTe," \u2014 "),HL=n(nTe,"A",{href:!0});var SBr=s(HL);r$e=r(SBr,"BartConfig"),SBr.forEach(t),t$e=r(nTe," (BART model)"),nTe.forEach(t),a$e=i(T),zf=n(T,"LI",{});var sTe=s(zf);wQ=n(sTe,"STRONG",{});var PBr=s(wQ);n$e=r(PBr,"beit"),PBr.forEach(t),s$e=r(sTe," \u2014 "),UL=n(sTe,"A",{href:!0});var $Br=s(UL);l$e=r($Br,"BeitConfig"),$Br.forEach(t),i$e=r(sTe," (BEiT 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JBr=s(PQ);z$e=r(JBr,"canine"),JBr.forEach(t),W$e=r(hTe," \u2014 "),t7=n(hTe,"A",{href:!0});var YBr=s(t7);Q$e=r(YBr,"CanineConfig"),YBr.forEach(t),H$e=r(hTe," (Canine model)"),hTe.forEach(t),U$e=i(T),em=n(T,"LI",{});var pTe=s(em);$Q=n(pTe,"STRONG",{});var KBr=s($Q);J$e=r(KBr,"clip"),KBr.forEach(t),Y$e=r(pTe," \u2014 "),a7=n(pTe,"A",{href:!0});var ZBr=s(a7);K$e=r(ZBr,"CLIPConfig"),ZBr.forEach(t),Z$e=r(pTe," (CLIP model)"),pTe.forEach(t),eIe=i(T),om=n(T,"LI",{});var _Te=s(om);IQ=n(_Te,"STRONG",{});var exr=s(IQ);oIe=r(exr,"convbert"),exr.forEach(t),rIe=r(_Te," \u2014 "),n7=n(_Te,"A",{href:!0});var oxr=s(n7);tIe=r(oxr,"ConvBertConfig"),oxr.forEach(t),aIe=r(_Te," (ConvBERT model)"),_Te.forEach(t),nIe=i(T),rm=n(T,"LI",{});var uTe=s(rm);jQ=n(uTe,"STRONG",{});var rxr=s(jQ);sIe=r(rxr,"convnext"),rxr.forEach(t),lIe=r(uTe," \u2014 "),s7=n(uTe,"A",{href:!0});var txr=s(s7);iIe=r(txr,"ConvNextConfig"),txr.forEach(t),dIe=r(uTe," (ConvNext model)"),uTe.forEach(t),cIe=i(T),tm=n(T,"LI",{});var bTe=s(tm);DQ=n(bTe,"STRONG",{});var axr=s(DQ);fIe=r(axr,"ctrl"),axr.forEach(t),mIe=r(bTe," \u2014 "),l7=n(bTe,"A",{href:!0});var nxr=s(l7);gIe=r(nxr,"CTRLConfig"),nxr.forEach(t),hIe=r(bTe," (CTRL model)"),bTe.forEach(t),pIe=i(T),am=n(T,"LI",{});var vTe=s(am);NQ=n(vTe,"STRONG",{});var sxr=s(NQ);_Ie=r(sxr,"data2vec-audio"),sxr.forEach(t),uIe=r(vTe," \u2014 "),i7=n(vTe,"A",{href:!0});var lxr=s(i7);bIe=r(lxr,"Data2VecAudioConfig"),lxr.forEach(t),vIe=r(vTe," (Data2VecAudio model)"),vTe.forEach(t),TIe=i(T),nm=n(T,"LI",{});var TTe=s(nm);qQ=n(TTe,"STRONG",{});var ixr=s(qQ);FIe=r(ixr,"data2vec-text"),ixr.forEach(t),CIe=r(TTe," \u2014 "),d7=n(TTe,"A",{href:!0});var dxr=s(d7);MIe=r(dxr,"Data2VecTextConfig"),dxr.forEach(t),EIe=r(TTe," (Data2VecText model)"),TTe.forEach(t),yIe=i(T),sm=n(T,"LI",{});var FTe=s(sm);OQ=n(FTe,"STRONG",{});var cxr=s(OQ);wIe=r(cxr,"deberta"),cxr.forEach(t),AIe=r(FTe," \u2014 "),c7=n(FTe,"A",{href:!0});var fxr=s(c7);LIe=r(fxr,"DebertaConfig"),fxr.forEach(t),BIe=r(FTe," 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iIr=s(Ck);oYe=r(iIr,"RetriBertTokenizer"),iIr.forEach(t),rYe=r(LL," or "),Mk=n(LL,"A",{href:!0});var dIr=s(Mk);tYe=r(dIr,"RetriBertTokenizerFast"),dIr.forEach(t),aYe=r(LL," (RetriBERT model)"),LL.forEach(t),nYe=i(y),ks=n(y,"LI",{});var BL=s(ks);wJ=n(BL,"STRONG",{});var cIr=s(wJ);sYe=r(cIr,"roberta"),cIr.forEach(t),lYe=r(BL," \u2014 "),Ek=n(BL,"A",{href:!0});var fIr=s(Ek);iYe=r(fIr,"RobertaTokenizer"),fIr.forEach(t),dYe=r(BL," or "),yk=n(BL,"A",{href:!0});var mIr=s(yk);cYe=r(mIr,"RobertaTokenizerFast"),mIr.forEach(t),fYe=r(BL," (RoBERTa model)"),BL.forEach(t),mYe=i(y),Rs=n(y,"LI",{});var xL=s(Rs);AJ=n(xL,"STRONG",{});var gIr=s(AJ);gYe=r(gIr,"roformer"),gIr.forEach(t),hYe=r(xL," \u2014 "),wk=n(xL,"A",{href:!0});var hIr=s(wk);pYe=r(hIr,"RoFormerTokenizer"),hIr.forEach(t),_Ye=r(xL," or "),Ak=n(xL,"A",{href:!0});var pIr=s(Ak);uYe=r(pIr,"RoFormerTokenizerFast"),pIr.forEach(t),bYe=r(xL," (RoFormer model)"),xL.forEach(t),vYe=i(y),Kg=n(y,"LI",{});var fFe=s(Kg);LJ=n(fFe,"STRONG",{});var 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model)"),PL.forEach(t),MKe=i(y),ah=n(y,"LI",{});var uFe=s(ah);DJ=n(uFe,"STRONG",{});var qIr=s(DJ);EKe=r(qIr,"xlm"),qIr.forEach(t),yKe=r(uFe," \u2014 "),Gk=n(uFe,"A",{href:!0});var OIr=s(Gk);wKe=r(OIr,"XLMTokenizer"),OIr.forEach(t),AKe=r(uFe," (XLM model)"),uFe.forEach(t),LKe=i(y),nh=n(y,"LI",{});var bFe=s(nh);NJ=n(bFe,"STRONG",{});var GIr=s(NJ);BKe=r(GIr,"xlm-prophetnet"),GIr.forEach(t),xKe=r(bFe," \u2014 "),Xk=n(bFe,"A",{href:!0});var XIr=s(Xk);kKe=r(XIr,"XLMProphetNetTokenizer"),XIr.forEach(t),RKe=r(bFe," (XLMProphetNet model)"),bFe.forEach(t),SKe=i(y),js=n(y,"LI",{});var $L=s(js);qJ=n($L,"STRONG",{});var VIr=s(qJ);PKe=r(VIr,"xlm-roberta"),VIr.forEach(t),$Ke=r($L," \u2014 "),Vk=n($L,"A",{href:!0});var zIr=s(Vk);IKe=r(zIr,"XLMRobertaTokenizer"),zIr.forEach(t),jKe=r($L," or "),zk=n($L,"A",{href:!0});var WIr=s(zk);DKe=r(WIr,"XLMRobertaTokenizerFast"),WIr.forEach(t),NKe=r($L," (XLM-RoBERTa model)"),$L.forEach(t),qKe=i(y),Ds=n(y,"LI",{});var IL=s(Ds);OJ=n(IL,"STRONG",{});var QIr=s(OJ);OKe=r(QIr,"xlnet"),QIr.forEach(t),GKe=r(IL," \u2014 "),Wk=n(IL,"A",{href:!0});var HIr=s(Wk);XKe=r(HIr,"XLNetTokenizer"),HIr.forEach(t),VKe=r(IL," or "),Qk=n(IL,"A",{href:!0});var UIr=s(Qk);zKe=r(UIr,"XLNetTokenizerFast"),UIr.forEach(t),WKe=r(IL," (XLNet model)"),IL.forEach(t),y.forEach(t),QKe=i(ha),GJ=n(ha,"P",{});var JIr=s(GJ);HKe=r(JIr,"Examples:"),JIr.forEach(t),UKe=i(ha),m(f4.$$.fragment,ha),ha.forEach(t),JKe=i(Xs),sh=n(Xs,"DIV",{class:!0});var vRe=s(sh);m(m4.$$.fragment,vRe),YKe=i(vRe),XJ=n(vRe,"P",{});var YIr=s(XJ);KKe=r(YIr,"Register a new tokenizer in this mapping."),YIr.forEach(t),vRe.forEach(t),Xs.forEach(t),pxe=i(d),Hi=n(d,"H2",{class:!0});var TRe=s(Hi);lh=n(TRe,"A",{id:!0,class:!0,href:!0});var KIr=s(lh);VJ=n(KIr,"SPAN",{});var ZIr=s(VJ);m(g4.$$.fragment,ZIr),ZIr.forEach(t),KIr.forEach(t),ZKe=i(TRe),zJ=n(TRe,"SPAN",{});var ejr=s(zJ);eZe=r(ejr,"AutoFeatureExtractor"),ejr.forEach(t),TRe.forEach(t),_xe=i(d),Ho=n(d,"DIV",{class:!0});var 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library when created with the `),Hk=n(FRe,"A",{href:!0});var ojr=s(Hk);tZe=r(ojr,"AutoFeatureExtractor.from_pretrained()"),ojr.forEach(t),aZe=r(FRe," class method."),FRe.forEach(t),nZe=i(Vs),_4=n(Vs,"P",{});var CRe=s(_4);sZe=r(CRe,"This class cannot be instantiated directly using "),WJ=n(CRe,"CODE",{});var rjr=s(WJ);lZe=r(rjr,"__init__()"),rjr.forEach(t),iZe=r(CRe," (throws an error)."),CRe.forEach(t),dZe=i(Vs),$e=n(Vs,"DIV",{class:!0});var Dt=s($e);m(u4.$$.fragment,Dt),cZe=i(Dt),QJ=n(Dt,"P",{});var tjr=s(QJ);fZe=r(tjr,"Instantiate one of the feature extractor classes of the library from a pretrained model vocabulary."),tjr.forEach(t),mZe=i(Dt),Xa=n(Dt,"P",{});var JC=s(Xa);gZe=r(JC,"The feature extractor class to instantiate is selected based on the "),HJ=n(JC,"CODE",{});var ajr=s(HJ);hZe=r(ajr,"model_type"),ajr.forEach(t),pZe=r(JC,` property of the config object
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missing, by falling back to using pattern matching on `),JJ=n(JC,"CODE",{});var sjr=s(JJ);bZe=r(sjr,"pretrained_model_name_or_path"),sjr.forEach(t),vZe=r(JC,":"),JC.forEach(t),TZe=i(Dt),ae=n(Dt,"UL",{});var le=s(ae);ih=n(le,"LI",{});var vFe=s(ih);YJ=n(vFe,"STRONG",{});var ljr=s(YJ);FZe=r(ljr,"beit"),ljr.forEach(t),CZe=r(vFe," \u2014 "),Uk=n(vFe,"A",{href:!0});var ijr=s(Uk);MZe=r(ijr,"BeitFeatureExtractor"),ijr.forEach(t),EZe=r(vFe," (BEiT model)"),vFe.forEach(t),yZe=i(le),dh=n(le,"LI",{});var TFe=s(dh);KJ=n(TFe,"STRONG",{});var djr=s(KJ);wZe=r(djr,"clip"),djr.forEach(t),AZe=r(TFe," \u2014 "),Jk=n(TFe,"A",{href:!0});var cjr=s(Jk);LZe=r(cjr,"CLIPFeatureExtractor"),cjr.forEach(t),BZe=r(TFe," (CLIP model)"),TFe.forEach(t),xZe=i(le),ch=n(le,"LI",{});var FFe=s(ch);ZJ=n(FFe,"STRONG",{});var fjr=s(ZJ);kZe=r(fjr,"convnext"),fjr.forEach(t),RZe=r(FFe," \u2014 "),Yk=n(FFe,"A",{href:!0});var mjr=s(Yk);SZe=r(mjr,"ConvNextFeatureExtractor"),mjr.forEach(t),PZe=r(FFe," (ConvNext 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Tjr=s(oR);ZZe=r(Tjr,"LayoutLMv2FeatureExtractor"),Tjr.forEach(t),eeo=r(yFe," (LayoutLMv2 model)"),yFe.forEach(t),oeo=i(le),ph=n(le,"LI",{});var wFe=s(ph);aY=n(wFe,"STRONG",{});var Fjr=s(aY);reo=r(Fjr,"maskformer"),Fjr.forEach(t),teo=r(wFe," \u2014 "),rR=n(wFe,"A",{href:!0});var Cjr=s(rR);aeo=r(Cjr,"MaskFormerFeatureExtractor"),Cjr.forEach(t),neo=r(wFe," (MaskFormer model)"),wFe.forEach(t),seo=i(le),_h=n(le,"LI",{});var AFe=s(_h);nY=n(AFe,"STRONG",{});var Mjr=s(nY);leo=r(Mjr,"perceiver"),Mjr.forEach(t),ieo=r(AFe," \u2014 "),tR=n(AFe,"A",{href:!0});var Ejr=s(tR);deo=r(Ejr,"PerceiverFeatureExtractor"),Ejr.forEach(t),ceo=r(AFe," (Perceiver model)"),AFe.forEach(t),feo=i(le),uh=n(le,"LI",{});var LFe=s(uh);sY=n(LFe,"STRONG",{});var yjr=s(sY);meo=r(yjr,"poolformer"),yjr.forEach(t),geo=r(LFe," \u2014 "),aR=n(LFe,"A",{href:!0});var wjr=s(aR);heo=r(wjr,"PoolFormerFeatureExtractor"),wjr.forEach(t),peo=r(LFe," (PoolFormer model)"),LFe.forEach(t),_eo=i(le),bh=n(le,"LI",{});var 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Pjr=s(iR);Peo=r(Pjr,"ViTFeatureExtractor"),Pjr.forEach(t),$eo=r(RFe," (ViT model)"),RFe.forEach(t),Ieo=i(le),Ch=n(le,"LI",{});var SFe=s(Ch);fY=n(SFe,"STRONG",{});var $jr=s(fY);jeo=r($jr,"vit_mae"),$jr.forEach(t),Deo=r(SFe," \u2014 "),dR=n(SFe,"A",{href:!0});var Ijr=s(dR);Neo=r(Ijr,"ViTFeatureExtractor"),Ijr.forEach(t),qeo=r(SFe," (ViTMAE model)"),SFe.forEach(t),Oeo=i(le),Mh=n(le,"LI",{});var PFe=s(Mh);mY=n(PFe,"STRONG",{});var jjr=s(mY);Geo=r(jjr,"wav2vec2"),jjr.forEach(t),Xeo=r(PFe," \u2014 "),cR=n(PFe,"A",{href:!0});var Djr=s(cR);Veo=r(Djr,"Wav2Vec2FeatureExtractor"),Djr.forEach(t),zeo=r(PFe," (Wav2Vec2 model)"),PFe.forEach(t),le.forEach(t),Weo=i(Dt),m(Eh.$$.fragment,Dt),Qeo=i(Dt),gY=n(Dt,"P",{});var Njr=s(gY);Heo=r(Njr,"Examples:"),Njr.forEach(t),Ueo=i(Dt),m(b4.$$.fragment,Dt),Dt.forEach(t),Jeo=i(Vs),yh=n(Vs,"DIV",{class:!0});var MRe=s(yh);m(v4.$$.fragment,MRe),Yeo=i(MRe),hY=n(MRe,"P",{});var qjr=s(hY);Keo=r(qjr,"Register a new feature extractor for this class."),qjr.forEach(t),MRe.forEach(t),Vs.forEach(t),uxe=i(d),Ui=n(d,"H2",{class:!0});var ERe=s(Ui);wh=n(ERe,"A",{id:!0,class:!0,href:!0});var Ojr=s(wh);pY=n(Ojr,"SPAN",{});var Gjr=s(pY);m(T4.$$.fragment,Gjr),Gjr.forEach(t),Ojr.forEach(t),Zeo=i(ERe),_Y=n(ERe,"SPAN",{});var Xjr=s(_Y);eoo=r(Xjr,"AutoProcessor"),Xjr.forEach(t),ERe.forEach(t),bxe=i(d),Uo=n(d,"DIV",{class:!0});var zs=s(Uo);m(F4.$$.fragment,zs),ooo=i(zs),C4=n(zs,"P",{});var yRe=s(C4);roo=r(yRe,`This is a generic processor class that will be instantiated as one of the processor classes of the library when
created with the `),fR=n(yRe,"A",{href:!0});var Vjr=s(fR);too=r(Vjr,"AutoProcessor.from_pretrained()"),Vjr.forEach(t),aoo=r(yRe," class method."),yRe.forEach(t),noo=i(zs),M4=n(zs,"P",{});var wRe=s(M4);soo=r(wRe,"This class cannot be instantiated directly using "),uY=n(wRe,"CODE",{});var zjr=s(uY);loo=r(zjr,"__init__()"),zjr.forEach(t),ioo=r(wRe," (throws an error)."),wRe.forEach(t),doo=i(zs),Ie=n(zs,"DIV",{class:!0});var Nt=s(Ie);m(E4.$$.fragment,Nt),coo=i(Nt),bY=n(Nt,"P",{});var Wjr=s(bY);foo=r(Wjr,"Instantiate one of the processor classes of the library from a pretrained model vocabulary."),Wjr.forEach(t),moo=i(Nt),Ji=n(Nt,"P",{});var Ez=s(Ji);goo=r(Ez,"The processor class to instantiate is selected based on the "),vY=n(Ez,"CODE",{});var Qjr=s(vY);hoo=r(Qjr,"model_type"),Qjr.forEach(t),poo=r(Ez,` property of the config object (either
passed as an argument or loaded from `),TY=n(Ez,"CODE",{});var Hjr=s(TY);_oo=r(Hjr,"pretrained_model_name_or_path"),Hjr.forEach(t),uoo=r(Ez," if possible):"),Ez.forEach(t),boo=i(Nt),Be=n(Nt,"UL",{});var No=s(Be);Ah=n(No,"LI",{});var $Fe=s(Ah);FY=n($Fe,"STRONG",{});var Ujr=s(FY);voo=r(Ujr,"clip"),Ujr.forEach(t),Too=r($Fe," \u2014 "),mR=n($Fe,"A",{href:!0});var Jjr=s(mR);Foo=r(Jjr,"CLIPProcessor"),Jjr.forEach(t),Coo=r($Fe," (CLIP model)"),$Fe.forEach(t),Moo=i(No),Lh=n(No,"LI",{});var IFe=s(Lh);CY=n(IFe,"STRONG",{});var Yjr=s(CY);Eoo=r(Yjr,"layoutlmv2"),Yjr.forEach(t),yoo=r(IFe," \u2014 "),gR=n(IFe,"A",{href:!0});var Kjr=s(gR);woo=r(Kjr,"LayoutLMv2Processor"),Kjr.forEach(t),Aoo=r(IFe," (LayoutLMv2 model)"),IFe.forEach(t),Loo=i(No),Bh=n(No,"LI",{});var jFe=s(Bh);MY=n(jFe,"STRONG",{});var Zjr=s(MY);Boo=r(Zjr,"layoutxlm"),Zjr.forEach(t),xoo=r(jFe," \u2014 "),hR=n(jFe,"A",{href:!0});var eDr=s(hR);koo=r(eDr,"LayoutXLMProcessor"),eDr.forEach(t),Roo=r(jFe," (LayoutXLM model)"),jFe.forEach(t),Soo=i(No),xh=n(No,"LI",{});var DFe=s(xh);EY=n(DFe,"STRONG",{});var oDr=s(EY);Poo=r(oDr,"speech_to_text"),oDr.forEach(t),$oo=r(DFe," \u2014 "),pR=n(DFe,"A",{href:!0});var rDr=s(pR);Ioo=r(rDr,"Speech2TextProcessor"),rDr.forEach(t),joo=r(DFe," (Speech2Text model)"),DFe.forEach(t),Doo=i(No),kh=n(No,"LI",{});var NFe=s(kh);yY=n(NFe,"STRONG",{});var tDr=s(yY);Noo=r(tDr,"speech_to_text_2"),tDr.forEach(t),qoo=r(NFe," \u2014 "),_R=n(NFe,"A",{href:!0});var aDr=s(_R);Ooo=r(aDr,"Speech2Text2Processor"),aDr.forEach(t),Goo=r(NFe," (Speech2Text2 model)"),NFe.forEach(t),Xoo=i(No),Rh=n(No,"LI",{});var qFe=s(Rh);wY=n(qFe,"STRONG",{});var nDr=s(wY);Voo=r(nDr,"trocr"),nDr.forEach(t),zoo=r(qFe," \u2014 "),uR=n(qFe,"A",{href:!0});var sDr=s(uR);Woo=r(sDr,"TrOCRProcessor"),sDr.forEach(t),Qoo=r(qFe," (TrOCR model)"),qFe.forEach(t),Hoo=i(No),Sh=n(No,"LI",{});var OFe=s(Sh);AY=n(OFe,"STRONG",{});var lDr=s(AY);Uoo=r(lDr,"vision-text-dual-encoder"),lDr.forEach(t),Joo=r(OFe," \u2014 "),bR=n(OFe,"A",{href:!0});var iDr=s(bR);Yoo=r(iDr,"VisionTextDualEncoderProcessor"),iDr.forEach(t),Koo=r(OFe," (VisionTextDualEncoder model)"),OFe.forEach(t),Zoo=i(No),Ph=n(No,"LI",{});var GFe=s(Ph);LY=n(GFe,"STRONG",{});var dDr=s(LY);ero=r(dDr,"wav2vec2"),dDr.forEach(t),oro=r(GFe," \u2014 "),vR=n(GFe,"A",{href:!0});var cDr=s(vR);rro=r(cDr,"Wav2Vec2Processor"),cDr.forEach(t),tro=r(GFe," (Wav2Vec2 model)"),GFe.forEach(t),No.forEach(t),aro=i(Nt),m($h.$$.fragment,Nt),nro=i(Nt),BY=n(Nt,"P",{});var fDr=s(BY);sro=r(fDr,"Examples:"),fDr.forEach(t),lro=i(Nt),m(y4.$$.fragment,Nt),Nt.forEach(t),iro=i(zs),Ih=n(zs,"DIV",{class:!0});var ARe=s(Ih);m(w4.$$.fragment,ARe),dro=i(ARe),xY=n(ARe,"P",{});var mDr=s(xY);cro=r(mDr,"Register a new processor for this class."),mDr.forEach(t),ARe.forEach(t),zs.forEach(t),vxe=i(d),Yi=n(d,"H2",{class:!0});var LRe=s(Yi);jh=n(LRe,"A",{id:!0,class:!0,href:!0});var gDr=s(jh);kY=n(gDr,"SPAN",{});var hDr=s(kY);m(A4.$$.fragment,hDr),hDr.forEach(t),gDr.forEach(t),fro=i(LRe),RY=n(LRe,"SPAN",{});var pDr=s(RY);mro=r(pDr,"AutoModel"),pDr.forEach(t),LRe.forEach(t),Txe=i(d),Jo=n(d,"DIV",{class:!0});var Ws=s(Jo);m(L4.$$.fragment,Ws),gro=i(Ws),Ki=n(Ws,"P",{});var yz=s(Ki);hro=r(yz,`This is a generic model class that will be instantiated as one of the base model classes of the library when created
with the `),SY=n(yz,"CODE",{});var _Dr=s(SY);pro=r(_Dr,"from_pretrained()"),_Dr.forEach(t),_ro=r(yz,"class method or the "),PY=n(yz,"CODE",{});var uDr=s(PY);uro=r(uDr,"from_config()"),uDr.forEach(t),bro=r(yz,`class
method.`),yz.forEach(t),vro=i(Ws),B4=n(Ws,"P",{});var BRe=s(B4);Tro=r(BRe,"This class cannot be instantiated directly using "),$Y=n(BRe,"CODE",{});var bDr=s($Y);Fro=r(bDr,"__init__()"),bDr.forEach(t),Cro=r(BRe," (throws an error)."),BRe.forEach(t),Mro=i(Ws),Vr=n(Ws,"DIV",{class:!0});var Qs=s(Vr);m(x4.$$.fragment,Qs),Ero=i(Qs),IY=n(Qs,"P",{});var vDr=s(IY);yro=r(vDr,"Instantiates one of the base model classes of the library from a configuration."),vDr.forEach(t),wro=i(Qs),Zi=n(Qs,"P",{});var wz=s(Zi);Aro=r(wz,`Note:
Loading a model from its configuration file does `),jY=n(wz,"STRONG",{});var TDr=s(jY);Lro=r(TDr,"not"),TDr.forEach(t),Bro=r(wz,` load the model weights. It only affects the
model\u2019s configuration. Use `),DY=n(wz,"CODE",{});var FDr=s(DY);xro=r(FDr,"from_pretrained()"),FDr.forEach(t),kro=r(wz,"to load the model weights."),wz.forEach(t),Rro=i(Qs),NY=n(Qs,"P",{});var CDr=s(NY);Sro=r(CDr,"Examples:"),CDr.forEach(t),Pro=i(Qs),m(k4.$$.fragment,Qs),Qs.forEach(t),$ro=i(Ws),je=n(Ws,"DIV",{class:!0});var qt=s(je);m(R4.$$.fragment,qt),Iro=i(qt),qY=n(qt,"P",{});var MDr=s(qY);jro=r(MDr,"Instantiate one of the base model classes of the library from a pretrained model."),MDr.forEach(t),Dro=i(qt),Va=n(qt,"P",{});var YC=s(Va);Nro=r(YC,"The model class to instantiate is selected based on the "),OY=n(YC,"CODE",{});var EDr=s(OY);qro=r(EDr,"model_type"),EDr.forEach(t),Oro=r(YC,` property of the config object (either
passed as an argument or loaded from `),GY=n(YC,"CODE",{});var yDr=s(GY);Gro=r(yDr,"pretrained_model_name_or_path"),yDr.forEach(t),Xro=r(YC,` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),XY=n(YC,"CODE",{});var wDr=s(XY);Vro=r(wDr,"pretrained_model_name_or_path"),wDr.forEach(t),zro=r(YC,":"),YC.forEach(t),Wro=i(qt),F=n(qt,"UL",{});var C=s(F);Dh=n(C,"LI",{});var XFe=s(Dh);VY=n(XFe,"STRONG",{});var ADr=s(VY);Qro=r(ADr,"albert"),ADr.forEach(t),Hro=r(XFe," \u2014 "),TR=n(XFe,"A",{href:!0});var LDr=s(TR);Uro=r(LDr,"AlbertModel"),LDr.forEach(t),Jro=r(XFe," (ALBERT model)"),XFe.forEach(t),Yro=i(C),Nh=n(C,"LI",{});var VFe=s(Nh);zY=n(VFe,"STRONG",{});var BDr=s(zY);Kro=r(BDr,"bart"),BDr.forEach(t),Zro=r(VFe," \u2014 "),FR=n(VFe,"A",{href:!0});var xDr=s(FR);eto=r(xDr,"BartModel"),xDr.forEach(t),oto=r(VFe," (BART model)"),VFe.forEach(t),rto=i(C),qh=n(C,"LI",{});var zFe=s(qh);WY=n(zFe,"STRONG",{});var kDr=s(WY);tto=r(kDr,"beit"),kDr.forEach(t),ato=r(zFe," \u2014 "),CR=n(zFe,"A",{href:!0});var RDr=s(CR);nto=r(RDr,"BeitModel"),RDr.forEach(t),sto=r(zFe," (BEiT model)"),zFe.forEach(t),lto=i(C),Oh=n(C,"LI",{});var WFe=s(Oh);QY=n(WFe,"STRONG",{});var SDr=s(QY);ito=r(SDr,"bert"),SDr.forEach(t),dto=r(WFe," \u2014 "),MR=n(WFe,"A",{href:!0});var PDr=s(MR);cto=r(PDr,"BertModel"),PDr.forEach(t),fto=r(WFe," (BERT model)"),WFe.forEach(t),mto=i(C),Gh=n(C,"LI",{});var QFe=s(Gh);HY=n(QFe,"STRONG",{});var $Dr=s(HY);gto=r($Dr,"bert-generation"),$Dr.forEach(t),hto=r(QFe," \u2014 "),ER=n(QFe,"A",{href:!0});var IDr=s(ER);pto=r(IDr,"BertGenerationEncoder"),IDr.forEach(t),_to=r(QFe," (Bert Generation model)"),QFe.forEach(t),uto=i(C),Xh=n(C,"LI",{});var HFe=s(Xh);UY=n(HFe,"STRONG",{});var jDr=s(UY);bto=r(jDr,"big_bird"),jDr.forEach(t),vto=r(HFe," \u2014 "),yR=n(HFe,"A",{href:!0});var DDr=s(yR);Tto=r(DDr,"BigBirdModel"),DDr.forEach(t),Fto=r(HFe," (BigBird model)"),HFe.forEach(t),Cto=i(C),Vh=n(C,"LI",{});var UFe=s(Vh);JY=n(UFe,"STRONG",{});var NDr=s(JY);Mto=r(NDr,"bigbird_pegasus"),NDr.forEach(t),Eto=r(UFe," \u2014 "),wR=n(UFe,"A",{href:!0});var qDr=s(wR);yto=r(qDr,"BigBirdPegasusModel"),qDr.forEach(t),wto=r(UFe," (BigBirdPegasus model)"),UFe.forEach(t),Ato=i(C),zh=n(C,"LI",{});var JFe=s(zh);YY=n(JFe,"STRONG",{});var ODr=s(YY);Lto=r(ODr,"blenderbot"),ODr.forEach(t),Bto=r(JFe," \u2014 "),AR=n(JFe,"A",{href:!0});var GDr=s(AR);xto=r(GDr,"BlenderbotModel"),GDr.forEach(t),kto=r(JFe," (Blenderbot model)"),JFe.forEach(t),Rto=i(C),Wh=n(C,"LI",{});var YFe=s(Wh);KY=n(YFe,"STRONG",{});var XDr=s(KY);Sto=r(XDr,"blenderbot-small"),XDr.forEach(t),Pto=r(YFe," \u2014 "),LR=n(YFe,"A",{href:!0});var VDr=s(LR);$to=r(VDr,"BlenderbotSmallModel"),VDr.forEach(t),Ito=r(YFe," (BlenderbotSmall model)"),YFe.forEach(t),jto=i(C),Qh=n(C,"LI",{});var KFe=s(Qh);ZY=n(KFe,"STRONG",{});var zDr=s(ZY);Dto=r(zDr,"camembert"),zDr.forEach(t),Nto=r(KFe," \u2014 "),BR=n(KFe,"A",{href:!0});var WDr=s(BR);qto=r(WDr,"CamembertModel"),WDr.forEach(t),Oto=r(KFe," (CamemBERT model)"),KFe.forEach(t),Gto=i(C),Hh=n(C,"LI",{});var ZFe=s(Hh);eK=n(ZFe,"STRONG",{});var QDr=s(eK);Xto=r(QDr,"canine"),QDr.forEach(t),Vto=r(ZFe," \u2014 "),xR=n(ZFe,"A",{href:!0});var HDr=s(xR);zto=r(HDr,"CanineModel"),HDr.forEach(t),Wto=r(ZFe," (Canine model)"),ZFe.forEach(t),Qto=i(C),Uh=n(C,"LI",{});var e9e=s(Uh);oK=n(e9e,"STRONG",{});var UDr=s(oK);Hto=r(UDr,"clip"),UDr.forEach(t),Uto=r(e9e," \u2014 "),kR=n(e9e,"A",{href:!0});var JDr=s(kR);Jto=r(JDr,"CLIPModel"),JDr.forEach(t),Yto=r(e9e," (CLIP model)"),e9e.forEach(t),Kto=i(C),Jh=n(C,"LI",{});var o9e=s(Jh);rK=n(o9e,"STRONG",{});var YDr=s(rK);Zto=r(YDr,"convbert"),YDr.forEach(t),eao=r(o9e," \u2014 "),RR=n(o9e,"A",{href:!0});var KDr=s(RR);oao=r(KDr,"ConvBertModel"),KDr.forEach(t),rao=r(o9e," (ConvBERT model)"),o9e.forEach(t),tao=i(C),Yh=n(C,"LI",{});var r9e=s(Yh);tK=n(r9e,"STRONG",{});var ZDr=s(tK);aao=r(ZDr,"convnext"),ZDr.forEach(t),nao=r(r9e," \u2014 "),SR=n(r9e,"A",{href:!0});var eNr=s(SR);sao=r(eNr,"ConvNextModel"),eNr.forEach(t),lao=r(r9e," (ConvNext model)"),r9e.forEach(t),iao=i(C),Kh=n(C,"LI",{});var t9e=s(Kh);aK=n(t9e,"STRONG",{});var oNr=s(aK);dao=r(oNr,"ctrl"),oNr.forEach(t),cao=r(t9e," \u2014 "),PR=n(t9e,"A",{href:!0});var rNr=s(PR);fao=r(rNr,"CTRLModel"),rNr.forEach(t),mao=r(t9e," (CTRL model)"),t9e.forEach(t),gao=i(C),Zh=n(C,"LI",{});var a9e=s(Zh);nK=n(a9e,"STRONG",{});var tNr=s(nK);hao=r(tNr,"data2vec-audio"),tNr.forEach(t),pao=r(a9e," \u2014 "),$R=n(a9e,"A",{href:!0});var aNr=s($R);_ao=r(aNr,"Data2VecAudioModel"),aNr.forEach(t),uao=r(a9e," (Data2VecAudio model)"),a9e.forEach(t),bao=i(C),ep=n(C,"LI",{});var n9e=s(ep);sK=n(n9e,"STRONG",{});var nNr=s(sK);vao=r(nNr,"data2vec-text"),nNr.forEach(t),Tao=r(n9e," \u2014 "),IR=n(n9e,"A",{href:!0});var sNr=s(IR);Fao=r(sNr,"Data2VecTextModel"),sNr.forEach(t),Cao=r(n9e," (Data2VecText model)"),n9e.forEach(t),Mao=i(C),op=n(C,"LI",{});var s9e=s(op);lK=n(s9e,"STRONG",{});var lNr=s(lK);Eao=r(lNr,"deberta"),lNr.forEach(t),yao=r(s9e," \u2014 "),jR=n(s9e,"A",{href:!0});var iNr=s(jR);wao=r(iNr,"DebertaModel"),iNr.forEach(t),Aao=r(s9e," (DeBERTa 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sXr=s(LP);Fho=r(sXr,"TapasForMaskedLM"),sXr.forEach(t),Cho=r(JCe," (TAPAS model)"),JCe.forEach(t),Mho=i(S),Q_=n(S,"LI",{});var YCe=s(Q_);hee=n(YCe,"STRONG",{});var lXr=s(hee);Eho=r(lXr,"transfo-xl"),lXr.forEach(t),yho=r(YCe," \u2014 "),BP=n(YCe,"A",{href:!0});var iXr=s(BP);who=r(iXr,"TransfoXLLMHeadModel"),iXr.forEach(t),Aho=r(YCe," (Transformer-XL model)"),YCe.forEach(t),Lho=i(S),H_=n(S,"LI",{});var KCe=s(H_);pee=n(KCe,"STRONG",{});var dXr=s(pee);Bho=r(dXr,"unispeech"),dXr.forEach(t),xho=r(KCe," \u2014 "),xP=n(KCe,"A",{href:!0});var cXr=s(xP);kho=r(cXr,"UniSpeechForPreTraining"),cXr.forEach(t),Rho=r(KCe," (UniSpeech model)"),KCe.forEach(t),Sho=i(S),U_=n(S,"LI",{});var ZCe=s(U_);_ee=n(ZCe,"STRONG",{});var fXr=s(_ee);Pho=r(fXr,"unispeech-sat"),fXr.forEach(t),$ho=r(ZCe," \u2014 "),kP=n(ZCe,"A",{href:!0});var mXr=s(kP);Iho=r(mXr,"UniSpeechSatForPreTraining"),mXr.forEach(t),jho=r(ZCe," (UniSpeechSat model)"),ZCe.forEach(t),Dho=i(S),J_=n(S,"LI",{});var eMe=s(J_);uee=n(eMe,"STRONG",{});var 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AXr=s(Eee);Fpo=r(AXr,"model.eval()"),AXr.forEach(t),Cpo=r(lMe,` (so for instance, dropout modules are
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with the `),Bee=n(Bz,"CODE",{});var SXr=s(Bee);kpo=r(SXr,"from_pretrained()"),SXr.forEach(t),Rpo=r(Bz,"class method or the "),xee=n(Bz,"CODE",{});var PXr=s(xee);Spo=r(PXr,"from_config()"),PXr.forEach(t),Ppo=r(Bz,`class
method.`),Bz.forEach(t),$po=i(Js),X4=n(Js,"P",{});var SRe=s(X4);Ipo=r(SRe,"This class cannot be instantiated directly using "),kee=n(SRe,"CODE",{});var $Xr=s(kee);jpo=r($Xr,"__init__()"),$Xr.forEach(t),Dpo=r(SRe," (throws an error)."),SRe.forEach(t),Npo=i(Js),Wr=n(Js,"DIV",{class:!0});var Ys=s(Wr);m(V4.$$.fragment,Ys),qpo=i(Ys),Ree=n(Ys,"P",{});var IXr=s(Ree);Opo=r(IXr,"Instantiates one of the model classes of the library (with a causal language modeling head) from a configuration."),IXr.forEach(t),Gpo=i(Ys),nd=n(Ys,"P",{});var xz=s(nd);Xpo=r(xz,`Note:
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model\u2019s configuration. Use `),Pee=n(xz,"CODE",{});var DXr=s(Pee);Wpo=r(DXr,"from_pretrained()"),DXr.forEach(t),Qpo=r(xz,"to load the model weights."),xz.forEach(t),Hpo=i(Ys),$ee=n(Ys,"P",{});var NXr=s($ee);Upo=r(NXr,"Examples:"),NXr.forEach(t),Jpo=i(Ys),m(z4.$$.fragment,Ys),Ys.forEach(t),Ypo=i(Js),Ne=n(Js,"DIV",{class:!0});var Gt=s(Ne);m(W4.$$.fragment,Gt),Kpo=i(Gt),Iee=n(Gt,"P",{});var qXr=s(Iee);Zpo=r(qXr,"Instantiate one of the model classes of the library (with a causal language modeling head) from a pretrained model."),qXr.forEach(t),e_o=i(Gt),Wa=n(Gt,"P",{});var ZC=s(Wa);o_o=r(ZC,"The model class to instantiate is selected based on the "),jee=n(ZC,"CODE",{});var OXr=s(jee);r_o=r(OXr,"model_type"),OXr.forEach(t),t_o=r(ZC,` property of the config object (either
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falling back to using pattern matching on `),Nee=n(ZC,"CODE",{});var XXr=s(Nee);s_o=r(XXr,"pretrained_model_name_or_path"),XXr.forEach(t),l_o=r(ZC,":"),ZC.forEach(t),i_o=i(Gt),$=n(Gt,"UL",{});var j=s($);nu=n(j,"LI",{});var iMe=s(nu);qee=n(iMe,"STRONG",{});var VXr=s(qee);d_o=r(VXr,"bart"),VXr.forEach(t),c_o=r(iMe," \u2014 "),NP=n(iMe,"A",{href:!0});var zXr=s(NP);f_o=r(zXr,"BartForCausalLM"),zXr.forEach(t),m_o=r(iMe," (BART model)"),iMe.forEach(t),g_o=i(j),su=n(j,"LI",{});var dMe=s(su);Oee=n(dMe,"STRONG",{});var WXr=s(Oee);h_o=r(WXr,"bert"),WXr.forEach(t),p_o=r(dMe," \u2014 "),qP=n(dMe,"A",{href:!0});var QXr=s(qP);__o=r(QXr,"BertLMHeadModel"),QXr.forEach(t),u_o=r(dMe," (BERT model)"),dMe.forEach(t),b_o=i(j),lu=n(j,"LI",{});var cMe=s(lu);Gee=n(cMe,"STRONG",{});var HXr=s(Gee);v_o=r(HXr,"bert-generation"),HXr.forEach(t),T_o=r(cMe," \u2014 "),OP=n(cMe,"A",{href:!0});var UXr=s(OP);F_o=r(UXr,"BertGenerationDecoder"),UXr.forEach(t),C_o=r(cMe," (Bert Generation 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bMe=s(pu);Jee=n(bMe,"STRONG",{});var cVr=s(Jee);nuo=r(cVr,"electra"),cVr.forEach(t),suo=r(bMe," \u2014 "),UP=n(bMe,"A",{href:!0});var fVr=s(UP);luo=r(fVr,"ElectraForCausalLM"),fVr.forEach(t),iuo=r(bMe," (ELECTRA model)"),bMe.forEach(t),duo=i(j),_u=n(j,"LI",{});var vMe=s(_u);Yee=n(vMe,"STRONG",{});var mVr=s(Yee);cuo=r(mVr,"gpt2"),mVr.forEach(t),fuo=r(vMe," \u2014 "),JP=n(vMe,"A",{href:!0});var gVr=s(JP);muo=r(gVr,"GPT2LMHeadModel"),gVr.forEach(t),guo=r(vMe," (OpenAI GPT-2 model)"),vMe.forEach(t),huo=i(j),uu=n(j,"LI",{});var TMe=s(uu);Kee=n(TMe,"STRONG",{});var hVr=s(Kee);puo=r(hVr,"gpt_neo"),hVr.forEach(t),_uo=r(TMe," \u2014 "),YP=n(TMe,"A",{href:!0});var pVr=s(YP);uuo=r(pVr,"GPTNeoForCausalLM"),pVr.forEach(t),buo=r(TMe," (GPT Neo model)"),TMe.forEach(t),vuo=i(j),bu=n(j,"LI",{});var FMe=s(bu);Zee=n(FMe,"STRONG",{});var _Vr=s(Zee);Tuo=r(_Vr,"gptj"),_Vr.forEach(t),Fuo=r(FMe," \u2014 "),KP=n(FMe,"A",{href:!0});var uVr=s(KP);Cuo=r(uVr,"GPTJForCausalLM"),uVr.forEach(t),Muo=r(FMe," (GPT-J 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yVr=s(r$);Guo=r(yVr,"OpenAIGPTLMHeadModel"),yVr.forEach(t),Xuo=r(yMe," (OpenAI GPT model)"),yMe.forEach(t),Vuo=i(j),Mu=n(j,"LI",{});var wMe=s(Mu);aoe=n(wMe,"STRONG",{});var wVr=s(aoe);zuo=r(wVr,"pegasus"),wVr.forEach(t),Wuo=r(wMe," \u2014 "),t$=n(wMe,"A",{href:!0});var AVr=s(t$);Quo=r(AVr,"PegasusForCausalLM"),AVr.forEach(t),Huo=r(wMe," (Pegasus model)"),wMe.forEach(t),Uuo=i(j),Eu=n(j,"LI",{});var AMe=s(Eu);noe=n(AMe,"STRONG",{});var LVr=s(noe);Juo=r(LVr,"plbart"),LVr.forEach(t),Yuo=r(AMe," \u2014 "),a$=n(AMe,"A",{href:!0});var BVr=s(a$);Kuo=r(BVr,"PLBartForCausalLM"),BVr.forEach(t),Zuo=r(AMe," (PLBart model)"),AMe.forEach(t),e1o=i(j),yu=n(j,"LI",{});var LMe=s(yu);soe=n(LMe,"STRONG",{});var xVr=s(soe);o1o=r(xVr,"prophetnet"),xVr.forEach(t),r1o=r(LMe," \u2014 "),n$=n(LMe,"A",{href:!0});var kVr=s(n$);t1o=r(kVr,"ProphetNetForCausalLM"),kVr.forEach(t),a1o=r(LMe," (ProphetNet model)"),LMe.forEach(t),n1o=i(j),wu=n(j,"LI",{});var BMe=s(wu);loe=n(BMe,"STRONG",{});var RVr=s(loe);s1o=r(RVr,"qdqbert"),RVr.forEach(t),l1o=r(BMe," \u2014 "),s$=n(BMe,"A",{href:!0});var SVr=s(s$);i1o=r(SVr,"QDQBertLMHeadModel"),SVr.forEach(t),d1o=r(BMe," (QDQBert model)"),BMe.forEach(t),c1o=i(j),Au=n(j,"LI",{});var xMe=s(Au);ioe=n(xMe,"STRONG",{});var PVr=s(ioe);f1o=r(PVr,"reformer"),PVr.forEach(t),m1o=r(xMe," \u2014 "),l$=n(xMe,"A",{href:!0});var $Vr=s(l$);g1o=r($Vr,"ReformerModelWithLMHead"),$Vr.forEach(t),h1o=r(xMe," (Reformer model)"),xMe.forEach(t),p1o=i(j),Lu=n(j,"LI",{});var kMe=s(Lu);doe=n(kMe,"STRONG",{});var IVr=s(doe);_1o=r(IVr,"rembert"),IVr.forEach(t),u1o=r(kMe," \u2014 "),i$=n(kMe,"A",{href:!0});var jVr=s(i$);b1o=r(jVr,"RemBertForCausalLM"),jVr.forEach(t),v1o=r(kMe," (RemBERT model)"),kMe.forEach(t),T1o=i(j),Bu=n(j,"LI",{});var RMe=s(Bu);coe=n(RMe,"STRONG",{});var DVr=s(coe);F1o=r(DVr,"roberta"),DVr.forEach(t),C1o=r(RMe," \u2014 "),d$=n(RMe,"A",{href:!0});var NVr=s(d$);M1o=r(NVr,"RobertaForCausalLM"),NVr.forEach(t),E1o=r(RMe," (RoBERTa model)"),RMe.forEach(t),y1o=i(j),xu=n(j,"LI",{});var SMe=s(xu);foe=n(SMe,"STRONG",{});var qVr=s(foe);w1o=r(qVr,"roformer"),qVr.forEach(t),A1o=r(SMe," \u2014 "),c$=n(SMe,"A",{href:!0});var OVr=s(c$);L1o=r(OVr,"RoFormerForCausalLM"),OVr.forEach(t),B1o=r(SMe," (RoFormer model)"),SMe.forEach(t),x1o=i(j),ku=n(j,"LI",{});var PMe=s(ku);moe=n(PMe,"STRONG",{});var GVr=s(moe);k1o=r(GVr,"speech_to_text_2"),GVr.forEach(t),R1o=r(PMe," \u2014 "),f$=n(PMe,"A",{href:!0});var XVr=s(f$);S1o=r(XVr,"Speech2Text2ForCausalLM"),XVr.forEach(t),P1o=r(PMe," (Speech2Text2 model)"),PMe.forEach(t),$1o=i(j),Ru=n(j,"LI",{});var $Me=s(Ru);goe=n($Me,"STRONG",{});var VVr=s(goe);I1o=r(VVr,"transfo-xl"),VVr.forEach(t),j1o=r($Me," \u2014 "),m$=n($Me,"A",{href:!0});var zVr=s(m$);D1o=r(zVr,"TransfoXLLMHeadModel"),zVr.forEach(t),N1o=r($Me," (Transformer-XL model)"),$Me.forEach(t),q1o=i(j),Su=n(j,"LI",{});var IMe=s(Su);hoe=n(IMe,"STRONG",{});var WVr=s(hoe);O1o=r(WVr,"trocr"),WVr.forEach(t),G1o=r(IMe," \u2014 "),g$=n(IMe,"A",{href:!0});var QVr=s(g$);X1o=r(QVr,"TrOCRForCausalLM"),QVr.forEach(t),V1o=r(IMe," (TrOCR model)"),IMe.forEach(t),z1o=i(j),Pu=n(j,"LI",{});var jMe=s(Pu);poe=n(jMe,"STRONG",{});var HVr=s(poe);W1o=r(HVr,"xglm"),HVr.forEach(t),Q1o=r(jMe," \u2014 "),h$=n(jMe,"A",{href:!0});var UVr=s(h$);H1o=r(UVr,"XGLMForCausalLM"),UVr.forEach(t),U1o=r(jMe," (XGLM model)"),jMe.forEach(t),J1o=i(j),$u=n(j,"LI",{});var DMe=s($u);_oe=n(DMe,"STRONG",{});var JVr=s(_oe);Y1o=r(JVr,"xlm"),JVr.forEach(t),K1o=r(DMe," \u2014 "),p$=n(DMe,"A",{href:!0});var YVr=s(p$);Z1o=r(YVr,"XLMWithLMHeadModel"),YVr.forEach(t),ebo=r(DMe," (XLM model)"),DMe.forEach(t),obo=i(j),Iu=n(j,"LI",{});var NMe=s(Iu);uoe=n(NMe,"STRONG",{});var KVr=s(uoe);rbo=r(KVr,"xlm-prophetnet"),KVr.forEach(t),tbo=r(NMe," \u2014 "),_$=n(NMe,"A",{href:!0});var ZVr=s(_$);abo=r(ZVr,"XLMProphetNetForCausalLM"),ZVr.forEach(t),nbo=r(NMe," (XLMProphetNet model)"),NMe.forEach(t),sbo=i(j),ju=n(j,"LI",{});var qMe=s(ju);boe=n(qMe,"STRONG",{});var ezr=s(boe);lbo=r(ezr,"xlm-roberta"),ezr.forEach(t),ibo=r(qMe," \u2014 "),u$=n(qMe,"A",{href:!0});var ozr=s(u$);dbo=r(ozr,"XLMRobertaForCausalLM"),ozr.forEach(t),cbo=r(qMe," (XLM-RoBERTa model)"),qMe.forEach(t),fbo=i(j),Du=n(j,"LI",{});var OMe=s(Du);voe=n(OMe,"STRONG",{});var rzr=s(voe);mbo=r(rzr,"xlm-roberta-xl"),rzr.forEach(t),gbo=r(OMe," \u2014 "),b$=n(OMe,"A",{href:!0});var tzr=s(b$);hbo=r(tzr,"XLMRobertaXLForCausalLM"),tzr.forEach(t),pbo=r(OMe," (XLM-RoBERTa-XL model)"),OMe.forEach(t),_bo=i(j),Nu=n(j,"LI",{});var GMe=s(Nu);Toe=n(GMe,"STRONG",{});var azr=s(Toe);ubo=r(azr,"xlnet"),azr.forEach(t),bbo=r(GMe," \u2014 "),v$=n(GMe,"A",{href:!0});var nzr=s(v$);vbo=r(nzr,"XLNetLMHeadModel"),nzr.forEach(t),Tbo=r(GMe," (XLNet model)"),GMe.forEach(t),j.forEach(t),Fbo=i(Gt),qu=n(Gt,"P",{});var XMe=s(qu);Cbo=r(XMe,"The model is set in evaluation mode by default using "),Foe=n(XMe,"CODE",{});var szr=s(Foe);Mbo=r(szr,"model.eval()"),szr.forEach(t),Ebo=r(XMe,` (so for instance, dropout modules are
deactivated). To train the model, you should first set it back in training mode with `),Coe=n(XMe,"CODE",{});var lzr=s(Coe);ybo=r(lzr,"model.train()"),lzr.forEach(t),XMe.forEach(t),wbo=i(Gt),Moe=n(Gt,"P",{});var izr=s(Moe);Abo=r(izr,"Examples:"),izr.forEach(t),Lbo=i(Gt),m(Q4.$$.fragment,Gt),Gt.forEach(t),Js.forEach(t),yxe=i(d),sd=n(d,"H2",{class:!0});var PRe=s(sd);Ou=n(PRe,"A",{id:!0,class:!0,href:!0});var dzr=s(Ou);Eoe=n(dzr,"SPAN",{});var czr=s(Eoe);m(H4.$$.fragment,czr),czr.forEach(t),dzr.forEach(t),Bbo=i(PRe),yoe=n(PRe,"SPAN",{});var fzr=s(yoe);xbo=r(fzr,"AutoModelForMaskedLM"),fzr.forEach(t),PRe.forEach(t),wxe=i(d),Zo=n(d,"DIV",{class:!0});var Ks=s(Zo);m(U4.$$.fragment,Ks),kbo=i(Ks),ld=n(Ks,"P",{});var kz=s(ld);Rbo=r(kz,`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 `),woe=n(kz,"CODE",{});var mzr=s(woe);Sbo=r(mzr,"from_pretrained()"),mzr.forEach(t),Pbo=r(kz,"class method or the "),Aoe=n(kz,"CODE",{});var gzr=s(Aoe);$bo=r(gzr,"from_config()"),gzr.forEach(t),Ibo=r(kz,`class
method.`),kz.forEach(t),jbo=i(Ks),J4=n(Ks,"P",{});var $Re=s(J4);Dbo=r($Re,"This class cannot be instantiated directly using "),Loe=n($Re,"CODE",{});var hzr=s(Loe);Nbo=r(hzr,"__init__()"),hzr.forEach(t),qbo=r($Re," (throws an error)."),$Re.forEach(t),Obo=i(Ks),Qr=n(Ks,"DIV",{class:!0});var Zs=s(Qr);m(Y4.$$.fragment,Zs),Gbo=i(Zs),Boe=n(Zs,"P",{});var pzr=s(Boe);Xbo=r(pzr,"Instantiates one of the model classes of the library (with a masked language modeling head) from a configuration."),pzr.forEach(t),Vbo=i(Zs),id=n(Zs,"P",{});var Rz=s(id);zbo=r(Rz,`Note:
Loading a model from its configuration file does `),xoe=n(Rz,"STRONG",{});var _zr=s(xoe);Wbo=r(_zr,"not"),_zr.forEach(t),Qbo=r(Rz,` load the model weights. It only affects the
model\u2019s configuration. Use `),koe=n(Rz,"CODE",{});var uzr=s(koe);Hbo=r(uzr,"from_pretrained()"),uzr.forEach(t),Ubo=r(Rz,"to load the model weights."),Rz.forEach(t),Jbo=i(Zs),Roe=n(Zs,"P",{});var bzr=s(Roe);Ybo=r(bzr,"Examples:"),bzr.forEach(t),Kbo=i(Zs),m(K4.$$.fragment,Zs),Zs.forEach(t),Zbo=i(Ks),qe=n(Ks,"DIV",{class:!0});var Xt=s(qe);m(Z4.$$.fragment,Xt),e3o=i(Xt),Soe=n(Xt,"P",{});var vzr=s(Soe);o3o=r(vzr,"Instantiate one of the model classes of the library (with a masked language modeling head) from a pretrained model."),vzr.forEach(t),r3o=i(Xt),Qa=n(Xt,"P",{});var eM=s(Qa);t3o=r(eM,"The model class to instantiate is selected based on the "),Poe=n(eM,"CODE",{});var Tzr=s(Poe);a3o=r(Tzr,"model_type"),Tzr.forEach(t),n3o=r(eM,` property of the config object (either
passed as an argument or loaded from `),$oe=n(eM,"CODE",{});var Fzr=s($oe);s3o=r(Fzr,"pretrained_model_name_or_path"),Fzr.forEach(t),l3o=r(eM,` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),Ioe=n(eM,"CODE",{});var Czr=s(Ioe);i3o=r(Czr,"pretrained_model_name_or_path"),Czr.forEach(t),d3o=r(eM,":"),eM.forEach(t),c3o=i(Xt),I=n(Xt,"UL",{});var D=s(I);Gu=n(D,"LI",{});var VMe=s(Gu);joe=n(VMe,"STRONG",{});var Mzr=s(joe);f3o=r(Mzr,"albert"),Mzr.forEach(t),m3o=r(VMe," \u2014 "),T$=n(VMe,"A",{href:!0});var Ezr=s(T$);g3o=r(Ezr,"AlbertForMaskedLM"),Ezr.forEach(t),h3o=r(VMe," (ALBERT model)"),VMe.forEach(t),p3o=i(D),Xu=n(D,"LI",{});var zMe=s(Xu);Doe=n(zMe,"STRONG",{});var yzr=s(Doe);_3o=r(yzr,"bart"),yzr.forEach(t),u3o=r(zMe," \u2014 "),F$=n(zMe,"A",{href:!0});var wzr=s(F$);b3o=r(wzr,"BartForConditionalGeneration"),wzr.forEach(t),v3o=r(zMe," (BART model)"),zMe.forEach(t),T3o=i(D),Vu=n(D,"LI",{});var WMe=s(Vu);Noe=n(WMe,"STRONG",{});var Azr=s(Noe);F3o=r(Azr,"bert"),Azr.forEach(t),C3o=r(WMe," \u2014 "),C$=n(WMe,"A",{href:!0});var Lzr=s(C$);M3o=r(Lzr,"BertForMaskedLM"),Lzr.forEach(t),E3o=r(WMe," (BERT model)"),WMe.forEach(t),y3o=i(D),zu=n(D,"LI",{});var QMe=s(zu);qoe=n(QMe,"STRONG",{});var Bzr=s(qoe);w3o=r(Bzr,"big_bird"),Bzr.forEach(t),A3o=r(QMe," \u2014 "),M$=n(QMe,"A",{href:!0});var xzr=s(M$);L3o=r(xzr,"BigBirdForMaskedLM"),xzr.forEach(t),B3o=r(QMe," (BigBird model)"),QMe.forEach(t),x3o=i(D),Wu=n(D,"LI",{});var HMe=s(Wu);Ooe=n(HMe,"STRONG",{});var kzr=s(Ooe);k3o=r(kzr,"camembert"),kzr.forEach(t),R3o=r(HMe," \u2014 "),E$=n(HMe,"A",{href:!0});var Rzr=s(E$);S3o=r(Rzr,"CamembertForMaskedLM"),Rzr.forEach(t),P3o=r(HMe," (CamemBERT model)"),HMe.forEach(t),$3o=i(D),Qu=n(D,"LI",{});var UMe=s(Qu);Goe=n(UMe,"STRONG",{});var Szr=s(Goe);I3o=r(Szr,"convbert"),Szr.forEach(t),j3o=r(UMe," \u2014 "),y$=n(UMe,"A",{href:!0});var Pzr=s(y$);D3o=r(Pzr,"ConvBertForMaskedLM"),Pzr.forEach(t),N3o=r(UMe," (ConvBERT model)"),UMe.forEach(t),q3o=i(D),Hu=n(D,"LI",{});var JMe=s(Hu);Xoe=n(JMe,"STRONG",{});var $zr=s(Xoe);O3o=r($zr,"data2vec-text"),$zr.forEach(t),G3o=r(JMe," \u2014 "),w$=n(JMe,"A",{href:!0});var Izr=s(w$);X3o=r(Izr,"Data2VecTextForMaskedLM"),Izr.forEach(t),V3o=r(JMe," (Data2VecText model)"),JMe.forEach(t),z3o=i(D),Uu=n(D,"LI",{});var YMe=s(Uu);Voe=n(YMe,"STRONG",{});var jzr=s(Voe);W3o=r(jzr,"deberta"),jzr.forEach(t),Q3o=r(YMe," \u2014 "),A$=n(YMe,"A",{href:!0});var Dzr=s(A$);H3o=r(Dzr,"DebertaForMaskedLM"),Dzr.forEach(t),U3o=r(YMe," (DeBERTa model)"),YMe.forEach(t),J3o=i(D),Ju=n(D,"LI",{});var KMe=s(Ju);zoe=n(KMe,"STRONG",{});var Nzr=s(zoe);Y3o=r(Nzr,"deberta-v2"),Nzr.forEach(t),K3o=r(KMe," \u2014 "),L$=n(KMe,"A",{href:!0});var qzr=s(L$);Z3o=r(qzr,"DebertaV2ForMaskedLM"),qzr.forEach(t),e5o=r(KMe," (DeBERTa-v2 model)"),KMe.forEach(t),o5o=i(D),Yu=n(D,"LI",{});var ZMe=s(Yu);Woe=n(ZMe,"STRONG",{});var Ozr=s(Woe);r5o=r(Ozr,"distilbert"),Ozr.forEach(t),t5o=r(ZMe," \u2014 "),B$=n(ZMe,"A",{href:!0});var Gzr=s(B$);a5o=r(Gzr,"DistilBertForMaskedLM"),Gzr.forEach(t),n5o=r(ZMe," (DistilBERT model)"),ZMe.forEach(t),s5o=i(D),Ku=n(D,"LI",{});var e4e=s(Ku);Qoe=n(e4e,"STRONG",{});var Xzr=s(Qoe);l5o=r(Xzr,"electra"),Xzr.forEach(t),i5o=r(e4e," \u2014 "),x$=n(e4e,"A",{href:!0});var Vzr=s(x$);d5o=r(Vzr,"ElectraForMaskedLM"),Vzr.forEach(t),c5o=r(e4e," (ELECTRA model)"),e4e.forEach(t),f5o=i(D),Zu=n(D,"LI",{});var o4e=s(Zu);Hoe=n(o4e,"STRONG",{});var zzr=s(Hoe);m5o=r(zzr,"flaubert"),zzr.forEach(t),g5o=r(o4e," \u2014 "),k$=n(o4e,"A",{href:!0});var Wzr=s(k$);h5o=r(Wzr,"FlaubertWithLMHeadModel"),Wzr.forEach(t),p5o=r(o4e," (FlauBERT model)"),o4e.forEach(t),_5o=i(D),e1=n(D,"LI",{});var r4e=s(e1);Uoe=n(r4e,"STRONG",{});var Qzr=s(Uoe);u5o=r(Qzr,"fnet"),Qzr.forEach(t),b5o=r(r4e," \u2014 "),R$=n(r4e,"A",{href:!0});var Hzr=s(R$);v5o=r(Hzr,"FNetForMaskedLM"),Hzr.forEach(t),T5o=r(r4e," (FNet model)"),r4e.forEach(t),F5o=i(D),o1=n(D,"LI",{});var t4e=s(o1);Joe=n(t4e,"STRONG",{});var Uzr=s(Joe);C5o=r(Uzr,"funnel"),Uzr.forEach(t),M5o=r(t4e," \u2014 "),S$=n(t4e,"A",{href:!0});var Jzr=s(S$);E5o=r(Jzr,"FunnelForMaskedLM"),Jzr.forEach(t),y5o=r(t4e," (Funnel Transformer 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wWr=s(H$);z2o=r(wWr,"SqueezeBertForMaskedLM"),wWr.forEach(t),W2o=r(b4e," (SqueezeBERT model)"),b4e.forEach(t),Q2o=i(D),u1=n(D,"LI",{});var v4e=s(u1);mre=n(v4e,"STRONG",{});var AWr=s(mre);H2o=r(AWr,"tapas"),AWr.forEach(t),U2o=r(v4e," \u2014 "),U$=n(v4e,"A",{href:!0});var LWr=s(U$);J2o=r(LWr,"TapasForMaskedLM"),LWr.forEach(t),Y2o=r(v4e," (TAPAS model)"),v4e.forEach(t),K2o=i(D),b1=n(D,"LI",{});var T4e=s(b1);gre=n(T4e,"STRONG",{});var BWr=s(gre);Z2o=r(BWr,"wav2vec2"),BWr.forEach(t),evo=r(T4e," \u2014 "),hre=n(T4e,"CODE",{});var xWr=s(hre);ovo=r(xWr,"Wav2Vec2ForMaskedLM"),xWr.forEach(t),rvo=r(T4e,"(Wav2Vec2 model)"),T4e.forEach(t),tvo=i(D),v1=n(D,"LI",{});var F4e=s(v1);pre=n(F4e,"STRONG",{});var kWr=s(pre);avo=r(kWr,"xlm"),kWr.forEach(t),nvo=r(F4e," \u2014 "),J$=n(F4e,"A",{href:!0});var RWr=s(J$);svo=r(RWr,"XLMWithLMHeadModel"),RWr.forEach(t),lvo=r(F4e," (XLM model)"),F4e.forEach(t),ivo=i(D),T1=n(D,"LI",{});var C4e=s(T1);_re=n(C4e,"STRONG",{});var SWr=s(_re);dvo=r(SWr,"xlm-roberta"),SWr.forEach(t),cvo=r(C4e," \u2014 "),Y$=n(C4e,"A",{href:!0});var PWr=s(Y$);fvo=r(PWr,"XLMRobertaForMaskedLM"),PWr.forEach(t),mvo=r(C4e," (XLM-RoBERTa model)"),C4e.forEach(t),gvo=i(D),F1=n(D,"LI",{});var M4e=s(F1);ure=n(M4e,"STRONG",{});var $Wr=s(ure);hvo=r($Wr,"xlm-roberta-xl"),$Wr.forEach(t),pvo=r(M4e," \u2014 "),K$=n(M4e,"A",{href:!0});var IWr=s(K$);_vo=r(IWr,"XLMRobertaXLForMaskedLM"),IWr.forEach(t),uvo=r(M4e," (XLM-RoBERTa-XL model)"),M4e.forEach(t),bvo=i(D),C1=n(D,"LI",{});var E4e=s(C1);bre=n(E4e,"STRONG",{});var jWr=s(bre);vvo=r(jWr,"yoso"),jWr.forEach(t),Tvo=r(E4e," \u2014 "),Z$=n(E4e,"A",{href:!0});var DWr=s(Z$);Fvo=r(DWr,"YosoForMaskedLM"),DWr.forEach(t),Cvo=r(E4e," (YOSO model)"),E4e.forEach(t),D.forEach(t),Mvo=i(Xt),M1=n(Xt,"P",{});var y4e=s(M1);Evo=r(y4e,"The model is set in evaluation mode by default using "),vre=n(y4e,"CODE",{});var NWr=s(vre);yvo=r(NWr,"model.eval()"),NWr.forEach(t),wvo=r(y4e,` (so for instance, dropout modules are
deactivated). To train the model, you should first set it back in training mode with `),Tre=n(y4e,"CODE",{});var qWr=s(Tre);Avo=r(qWr,"model.train()"),qWr.forEach(t),y4e.forEach(t),Lvo=i(Xt),Fre=n(Xt,"P",{});var OWr=s(Fre);Bvo=r(OWr,"Examples:"),OWr.forEach(t),xvo=i(Xt),m(eE.$$.fragment,Xt),Xt.forEach(t),Ks.forEach(t),Axe=i(d),dd=n(d,"H2",{class:!0});var IRe=s(dd);E1=n(IRe,"A",{id:!0,class:!0,href:!0});var GWr=s(E1);Cre=n(GWr,"SPAN",{});var XWr=s(Cre);m(oE.$$.fragment,XWr),XWr.forEach(t),GWr.forEach(t),kvo=i(IRe),Mre=n(IRe,"SPAN",{});var VWr=s(Mre);Rvo=r(VWr,"AutoModelForSeq2SeqLM"),VWr.forEach(t),IRe.forEach(t),Lxe=i(d),er=n(d,"DIV",{class:!0});var el=s(er);m(rE.$$.fragment,el),Svo=i(el),cd=n(el,"P",{});var Sz=s(cd);Pvo=r(Sz,`This is a generic model class that will be instantiated as one of the model classes of the library (with a sequence-to-sequence language modeling head) when created
with the `),Ere=n(Sz,"CODE",{});var zWr=s(Ere);$vo=r(zWr,"from_pretrained()"),zWr.forEach(t),Ivo=r(Sz,"class method or the "),yre=n(Sz,"CODE",{});var WWr=s(yre);jvo=r(WWr,"from_config()"),WWr.forEach(t),Dvo=r(Sz,`class
method.`),Sz.forEach(t),Nvo=i(el),tE=n(el,"P",{});var jRe=s(tE);qvo=r(jRe,"This class cannot be instantiated directly using "),wre=n(jRe,"CODE",{});var QWr=s(wre);Ovo=r(QWr,"__init__()"),QWr.forEach(t),Gvo=r(jRe," (throws an error)."),jRe.forEach(t),Xvo=i(el),Hr=n(el,"DIV",{class:!0});var ol=s(Hr);m(aE.$$.fragment,ol),Vvo=i(ol),Are=n(ol,"P",{});var HWr=s(Are);zvo=r(HWr,"Instantiates one of the model classes of the library (with a sequence-to-sequence language modeling head) from a configuration."),HWr.forEach(t),Wvo=i(ol),fd=n(ol,"P",{});var Pz=s(fd);Qvo=r(Pz,`Note:
Loading a model from its configuration file does `),Lre=n(Pz,"STRONG",{});var UWr=s(Lre);Hvo=r(UWr,"not"),UWr.forEach(t),Uvo=r(Pz,` load the model weights. It only affects the
model\u2019s configuration. Use `),Bre=n(Pz,"CODE",{});var JWr=s(Bre);Jvo=r(JWr,"from_pretrained()"),JWr.forEach(t),Yvo=r(Pz,"to load the model weights."),Pz.forEach(t),Kvo=i(ol),xre=n(ol,"P",{});var YWr=s(xre);Zvo=r(YWr,"Examples:"),YWr.forEach(t),eTo=i(ol),m(nE.$$.fragment,ol),ol.forEach(t),oTo=i(el),Oe=n(el,"DIV",{class:!0});var Vt=s(Oe);m(sE.$$.fragment,Vt),rTo=i(Vt),kre=n(Vt,"P",{});var KWr=s(kre);tTo=r(KWr,"Instantiate one of the model classes of the library (with a sequence-to-sequence language modeling head) from a pretrained model."),KWr.forEach(t),aTo=i(Vt),Ha=n(Vt,"P",{});var oM=s(Ha);nTo=r(oM,"The model class to instantiate is selected based on the "),Rre=n(oM,"CODE",{});var ZWr=s(Rre);sTo=r(ZWr,"model_type"),ZWr.forEach(t),lTo=r(oM,` property of the config object (either
passed as an argument or loaded from `),Sre=n(oM,"CODE",{});var eQr=s(Sre);iTo=r(eQr,"pretrained_model_name_or_path"),eQr.forEach(t),dTo=r(oM,` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),Pre=n(oM,"CODE",{});var oQr=s(Pre);cTo=r(oQr,"pretrained_model_name_or_path"),oQr.forEach(t),fTo=r(oM,":"),oM.forEach(t),mTo=i(Vt),ne=n(Vt,"UL",{});var ie=s(ne);y1=n(ie,"LI",{});var w4e=s(y1);$re=n(w4e,"STRONG",{});var rQr=s($re);gTo=r(rQr,"bart"),rQr.forEach(t),hTo=r(w4e," \u2014 "),eI=n(w4e,"A",{href:!0});var tQr=s(eI);pTo=r(tQr,"BartForConditionalGeneration"),tQr.forEach(t),_To=r(w4e," (BART model)"),w4e.forEach(t),uTo=i(ie),w1=n(ie,"LI",{});var A4e=s(w1);Ire=n(A4e,"STRONG",{});var aQr=s(Ire);bTo=r(aQr,"bigbird_pegasus"),aQr.forEach(t),vTo=r(A4e," \u2014 "),oI=n(A4e,"A",{href:!0});var nQr=s(oI);TTo=r(nQr,"BigBirdPegasusForConditionalGeneration"),nQr.forEach(t),FTo=r(A4e," (BigBirdPegasus model)"),A4e.forEach(t),CTo=i(ie),A1=n(ie,"LI",{});var L4e=s(A1);jre=n(L4e,"STRONG",{});var sQr=s(jre);MTo=r(sQr,"blenderbot"),sQr.forEach(t),ETo=r(L4e," \u2014 "),rI=n(L4e,"A",{href:!0});var lQr=s(rI);yTo=r(lQr,"BlenderbotForConditionalGeneration"),lQr.forEach(t),wTo=r(L4e," (Blenderbot model)"),L4e.forEach(t),ATo=i(ie),L1=n(ie,"LI",{});var B4e=s(L1);Dre=n(B4e,"STRONG",{});var iQr=s(Dre);LTo=r(iQr,"blenderbot-small"),iQr.forEach(t),BTo=r(B4e," \u2014 "),tI=n(B4e,"A",{href:!0});var dQr=s(tI);xTo=r(dQr,"BlenderbotSmallForConditionalGeneration"),dQr.forEach(t),kTo=r(B4e," (BlenderbotSmall model)"),B4e.forEach(t),RTo=i(ie),B1=n(ie,"LI",{});var x4e=s(B1);Nre=n(x4e,"STRONG",{});var cQr=s(Nre);STo=r(cQr,"encoder-decoder"),cQr.forEach(t),PTo=r(x4e," \u2014 "),aI=n(x4e,"A",{href:!0});var fQr=s(aI);$To=r(fQr,"EncoderDecoderModel"),fQr.forEach(t),ITo=r(x4e," (Encoder decoder model)"),x4e.forEach(t),jTo=i(ie),x1=n(ie,"LI",{});var k4e=s(x1);qre=n(k4e,"STRONG",{});var mQr=s(qre);DTo=r(mQr,"fsmt"),mQr.forEach(t),NTo=r(k4e," \u2014 "),nI=n(k4e,"A",{href:!0});var gQr=s(nI);qTo=r(gQr,"FSMTForConditionalGeneration"),gQr.forEach(t),OTo=r(k4e," (FairSeq Machine-Translation model)"),k4e.forEach(t),GTo=i(ie),k1=n(ie,"LI",{});var R4e=s(k1);Ore=n(R4e,"STRONG",{});var hQr=s(Ore);XTo=r(hQr,"led"),hQr.forEach(t),VTo=r(R4e," \u2014 "),sI=n(R4e,"A",{href:!0});var pQr=s(sI);zTo=r(pQr,"LEDForConditionalGeneration"),pQr.forEach(t),WTo=r(R4e," (LED model)"),R4e.forEach(t),QTo=i(ie),R1=n(ie,"LI",{});var S4e=s(R1);Gre=n(S4e,"STRONG",{});var _Qr=s(Gre);HTo=r(_Qr,"m2m_100"),_Qr.forEach(t),UTo=r(S4e," \u2014 "),lI=n(S4e,"A",{href:!0});var uQr=s(lI);JTo=r(uQr,"M2M100ForConditionalGeneration"),uQr.forEach(t),YTo=r(S4e," (M2M100 model)"),S4e.forEach(t),KTo=i(ie),S1=n(ie,"LI",{});var P4e=s(S1);Xre=n(P4e,"STRONG",{});var bQr=s(Xre);ZTo=r(bQr,"marian"),bQr.forEach(t),e8o=r(P4e," \u2014 "),iI=n(P4e,"A",{href:!0});var vQr=s(iI);o8o=r(vQr,"MarianMTModel"),vQr.forEach(t),r8o=r(P4e," (Marian model)"),P4e.forEach(t),t8o=i(ie),P1=n(ie,"LI",{});var $4e=s(P1);Vre=n($4e,"STRONG",{});var TQr=s(Vre);a8o=r(TQr,"mbart"),TQr.forEach(t),n8o=r($4e," \u2014 "),dI=n($4e,"A",{href:!0});var FQr=s(dI);s8o=r(FQr,"MBartForConditionalGeneration"),FQr.forEach(t),l8o=r($4e," (mBART model)"),$4e.forEach(t),i8o=i(ie),$1=n(ie,"LI",{});var I4e=s($1);zre=n(I4e,"STRONG",{});var CQr=s(zre);d8o=r(CQr,"mt5"),CQr.forEach(t),c8o=r(I4e," \u2014 "),cI=n(I4e,"A",{href:!0});var MQr=s(cI);f8o=r(MQr,"MT5ForConditionalGeneration"),MQr.forEach(t),m8o=r(I4e," (mT5 model)"),I4e.forEach(t),g8o=i(ie),I1=n(ie,"LI",{});var j4e=s(I1);Wre=n(j4e,"STRONG",{});var EQr=s(Wre);h8o=r(EQr,"pegasus"),EQr.forEach(t),p8o=r(j4e," \u2014 "),fI=n(j4e,"A",{href:!0});var yQr=s(fI);_8o=r(yQr,"PegasusForConditionalGeneration"),yQr.forEach(t),u8o=r(j4e," (Pegasus model)"),j4e.forEach(t),b8o=i(ie),j1=n(ie,"LI",{});var D4e=s(j1);Qre=n(D4e,"STRONG",{});var wQr=s(Qre);v8o=r(wQr,"plbart"),wQr.forEach(t),T8o=r(D4e," \u2014 "),mI=n(D4e,"A",{href:!0});var AQr=s(mI);F8o=r(AQr,"PLBartForConditionalGeneration"),AQr.forEach(t),C8o=r(D4e," (PLBart model)"),D4e.forEach(t),M8o=i(ie),D1=n(ie,"LI",{});var N4e=s(D1);Hre=n(N4e,"STRONG",{});var LQr=s(Hre);E8o=r(LQr,"prophetnet"),LQr.forEach(t),y8o=r(N4e," \u2014 "),gI=n(N4e,"A",{href:!0});var BQr=s(gI);w8o=r(BQr,"ProphetNetForConditionalGeneration"),BQr.forEach(t),A8o=r(N4e," (ProphetNet model)"),N4e.forEach(t),L8o=i(ie),N1=n(ie,"LI",{});var q4e=s(N1);Ure=n(q4e,"STRONG",{});var xQr=s(Ure);B8o=r(xQr,"t5"),xQr.forEach(t),x8o=r(q4e," \u2014 "),hI=n(q4e,"A",{href:!0});var kQr=s(hI);k8o=r(kQr,"T5ForConditionalGeneration"),kQr.forEach(t),R8o=r(q4e," (T5 model)"),q4e.forEach(t),S8o=i(ie),q1=n(ie,"LI",{});var O4e=s(q1);Jre=n(O4e,"STRONG",{});var RQr=s(Jre);P8o=r(RQr,"xlm-prophetnet"),RQr.forEach(t),$8o=r(O4e," \u2014 "),pI=n(O4e,"A",{href:!0});var SQr=s(pI);I8o=r(SQr,"XLMProphetNetForConditionalGeneration"),SQr.forEach(t),j8o=r(O4e," (XLMProphetNet model)"),O4e.forEach(t),ie.forEach(t),D8o=i(Vt),O1=n(Vt,"P",{});var G4e=s(O1);N8o=r(G4e,"The model is set in evaluation mode by default using "),Yre=n(G4e,"CODE",{});var PQr=s(Yre);q8o=r(PQr,"model.eval()"),PQr.forEach(t),O8o=r(G4e,` (so for instance, dropout modules are
deactivated). To train the model, you should first set it back in training mode with `),Kre=n(G4e,"CODE",{});var $Qr=s(Kre);G8o=r($Qr,"model.train()"),$Qr.forEach(t),G4e.forEach(t),X8o=i(Vt),Zre=n(Vt,"P",{});var IQr=s(Zre);V8o=r(IQr,"Examples:"),IQr.forEach(t),z8o=i(Vt),m(lE.$$.fragment,Vt),Vt.forEach(t),el.forEach(t),Bxe=i(d),md=n(d,"H2",{class:!0});var DRe=s(md);G1=n(DRe,"A",{id:!0,class:!0,href:!0});var jQr=s(G1);ete=n(jQr,"SPAN",{});var DQr=s(ete);m(iE.$$.fragment,DQr),DQr.forEach(t),jQr.forEach(t),W8o=i(DRe),ote=n(DRe,"SPAN",{});var NQr=s(ote);Q8o=r(NQr,"AutoModelForSequenceClassification"),NQr.forEach(t),DRe.forEach(t),xxe=i(d),or=n(d,"DIV",{class:!0});var rl=s(or);m(dE.$$.fragment,rl),H8o=i(rl),gd=n(rl,"P",{});var $z=s(gd);U8o=r($z,`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 `),rte=n($z,"CODE",{});var qQr=s(rte);J8o=r(qQr,"from_pretrained()"),qQr.forEach(t),Y8o=r($z,"class method or the "),tte=n($z,"CODE",{});var OQr=s(tte);K8o=r(OQr,"from_config()"),OQr.forEach(t),Z8o=r($z,`class
method.`),$z.forEach(t),eFo=i(rl),cE=n(rl,"P",{});var NRe=s(cE);oFo=r(NRe,"This class cannot be instantiated directly using "),ate=n(NRe,"CODE",{});var GQr=s(ate);rFo=r(GQr,"__init__()"),GQr.forEach(t),tFo=r(NRe," (throws an error)."),NRe.forEach(t),aFo=i(rl),Ur=n(rl,"DIV",{class:!0});var tl=s(Ur);m(fE.$$.fragment,tl),nFo=i(tl),nte=n(tl,"P",{});var XQr=s(nte);sFo=r(XQr,"Instantiates one of the model classes of the library (with a sequence classification head) from a configuration."),XQr.forEach(t),lFo=i(tl),hd=n(tl,"P",{});var Iz=s(hd);iFo=r(Iz,`Note:
Loading a model from its configuration file does `),ste=n(Iz,"STRONG",{});var VQr=s(ste);dFo=r(VQr,"not"),VQr.forEach(t),cFo=r(Iz,` load the model weights. It only affects the
model\u2019s configuration. Use `),lte=n(Iz,"CODE",{});var zQr=s(lte);fFo=r(zQr,"from_pretrained()"),zQr.forEach(t),mFo=r(Iz,"to load the model weights."),Iz.forEach(t),gFo=i(tl),ite=n(tl,"P",{});var WQr=s(ite);hFo=r(WQr,"Examples:"),WQr.forEach(t),pFo=i(tl),m(mE.$$.fragment,tl),tl.forEach(t),_Fo=i(rl),Ge=n(rl,"DIV",{class:!0});var zt=s(Ge);m(gE.$$.fragment,zt),uFo=i(zt),dte=n(zt,"P",{});var QQr=s(dte);bFo=r(QQr,"Instantiate one of the model classes of the library (with a sequence classification head) from a pretrained model."),QQr.forEach(t),vFo=i(zt),Ua=n(zt,"P",{});var rM=s(Ua);TFo=r(rM,"The model class to instantiate is selected based on the "),cte=n(rM,"CODE",{});var HQr=s(cte);FFo=r(HQr,"model_type"),HQr.forEach(t),CFo=r(rM,` property of the config object (either
passed as an argument or loaded from `),fte=n(rM,"CODE",{});var UQr=s(fte);MFo=r(UQr,"pretrained_model_name_or_path"),UQr.forEach(t),EFo=r(rM,` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),mte=n(rM,"CODE",{});var JQr=s(mte);yFo=r(JQr,"pretrained_model_name_or_path"),JQr.forEach(t),wFo=r(rM,":"),rM.forEach(t),AFo=i(zt),A=n(zt,"UL",{});var L=s(A);X1=n(L,"LI",{});var X4e=s(X1);gte=n(X4e,"STRONG",{});var YQr=s(gte);LFo=r(YQr,"albert"),YQr.forEach(t),BFo=r(X4e," \u2014 "),_I=n(X4e,"A",{href:!0});var KQr=s(_I);xFo=r(KQr,"AlbertForSequenceClassification"),KQr.forEach(t),kFo=r(X4e," (ALBERT model)"),X4e.forEach(t),RFo=i(L),V1=n(L,"LI",{});var V4e=s(V1);hte=n(V4e,"STRONG",{});var ZQr=s(hte);SFo=r(ZQr,"bart"),ZQr.forEach(t),PFo=r(V4e," \u2014 "),uI=n(V4e,"A",{href:!0});var eHr=s(uI);$Fo=r(eHr,"BartForSequenceClassification"),eHr.forEach(t),IFo=r(V4e," (BART model)"),V4e.forEach(t),jFo=i(L),z1=n(L,"LI",{});var z4e=s(z1);pte=n(z4e,"STRONG",{});var oHr=s(pte);DFo=r(oHr,"bert"),oHr.forEach(t),NFo=r(z4e," \u2014 "),bI=n(z4e,"A",{href:!0});var rHr=s(bI);qFo=r(rHr,"BertForSequenceClassification"),rHr.forEach(t),OFo=r(z4e," (BERT model)"),z4e.forEach(t),GFo=i(L),W1=n(L,"LI",{});var W4e=s(W1);_te=n(W4e,"STRONG",{});var tHr=s(_te);XFo=r(tHr,"big_bird"),tHr.forEach(t),VFo=r(W4e," \u2014 "),vI=n(W4e,"A",{href:!0});var aHr=s(vI);zFo=r(aHr,"BigBirdForSequenceClassification"),aHr.forEach(t),WFo=r(W4e," (BigBird model)"),W4e.forEach(t),QFo=i(L),Q1=n(L,"LI",{});var Q4e=s(Q1);ute=n(Q4e,"STRONG",{});var nHr=s(ute);HFo=r(nHr,"bigbird_pegasus"),nHr.forEach(t),UFo=r(Q4e," \u2014 "),TI=n(Q4e,"A",{href:!0});var sHr=s(TI);JFo=r(sHr,"BigBirdPegasusForSequenceClassification"),sHr.forEach(t),YFo=r(Q4e," (BigBirdPegasus model)"),Q4e.forEach(t),KFo=i(L),H1=n(L,"LI",{});var H4e=s(H1);bte=n(H4e,"STRONG",{});var lHr=s(bte);ZFo=r(lHr,"camembert"),lHr.forEach(t),e9o=r(H4e," \u2014 "),FI=n(H4e,"A",{href:!0});var iHr=s(FI);o9o=r(iHr,"CamembertForSequenceClassification"),iHr.forEach(t),r9o=r(H4e," (CamemBERT model)"),H4e.forEach(t),t9o=i(L),U1=n(L,"LI",{});var U4e=s(U1);vte=n(U4e,"STRONG",{});var dHr=s(vte);a9o=r(dHr,"canine"),dHr.forEach(t),n9o=r(U4e," \u2014 "),CI=n(U4e,"A",{href:!0});var cHr=s(CI);s9o=r(cHr,"CanineForSequenceClassification"),cHr.forEach(t),l9o=r(U4e," (Canine model)"),U4e.forEach(t),i9o=i(L),J1=n(L,"LI",{});var J4e=s(J1);Tte=n(J4e,"STRONG",{});var fHr=s(Tte);d9o=r(fHr,"convbert"),fHr.forEach(t),c9o=r(J4e," \u2014 "),MI=n(J4e,"A",{href:!0});var mHr=s(MI);f9o=r(mHr,"ConvBertForSequenceClassification"),mHr.forEach(t),m9o=r(J4e," (ConvBERT model)"),J4e.forEach(t),g9o=i(L),Y1=n(L,"LI",{});var Y4e=s(Y1);Fte=n(Y4e,"STRONG",{});var gHr=s(Fte);h9o=r(gHr,"ctrl"),gHr.forEach(t),p9o=r(Y4e," \u2014 "),EI=n(Y4e,"A",{href:!0});var hHr=s(EI);_9o=r(hHr,"CTRLForSequenceClassification"),hHr.forEach(t),u9o=r(Y4e," (CTRL model)"),Y4e.forEach(t),b9o=i(L),K1=n(L,"LI",{});var K4e=s(K1);Cte=n(K4e,"STRONG",{});var pHr=s(Cte);v9o=r(pHr,"data2vec-text"),pHr.forEach(t),T9o=r(K4e," \u2014 "),yI=n(K4e,"A",{href:!0});var _Hr=s(yI);F9o=r(_Hr,"Data2VecTextForSequenceClassification"),_Hr.forEach(t),C9o=r(K4e," (Data2VecText model)"),K4e.forEach(t),M9o=i(L),Z1=n(L,"LI",{});var Z4e=s(Z1);Mte=n(Z4e,"STRONG",{});var uHr=s(Mte);E9o=r(uHr,"deberta"),uHr.forEach(t),y9o=r(Z4e," \u2014 "),wI=n(Z4e,"A",{href:!0});var bHr=s(wI);w9o=r(bHr,"DebertaForSequenceClassification"),bHr.forEach(t),A9o=r(Z4e," (DeBERTa model)"),Z4e.forEach(t),L9o=i(L),eb=n(L,"LI",{});var eEe=s(eb);Ete=n(eEe,"STRONG",{});var vHr=s(Ete);B9o=r(vHr,"deberta-v2"),vHr.forEach(t),x9o=r(eEe," \u2014 "),AI=n(eEe,"A",{href:!0});var THr=s(AI);k9o=r(THr,"DebertaV2ForSequenceClassification"),THr.forEach(t),R9o=r(eEe," (DeBERTa-v2 model)"),eEe.forEach(t),S9o=i(L),ob=n(L,"LI",{});var oEe=s(ob);yte=n(oEe,"STRONG",{});var FHr=s(yte);P9o=r(FHr,"distilbert"),FHr.forEach(t),$9o=r(oEe," \u2014 "),LI=n(oEe,"A",{href:!0});var CHr=s(LI);I9o=r(CHr,"DistilBertForSequenceClassification"),CHr.forEach(t),j9o=r(oEe," (DistilBERT model)"),oEe.forEach(t),D9o=i(L),rb=n(L,"LI",{});var rEe=s(rb);wte=n(rEe,"STRONG",{});var MHr=s(wte);N9o=r(MHr,"electra"),MHr.forEach(t),q9o=r(rEe," \u2014 "),BI=n(rEe,"A",{href:!0});var EHr=s(BI);O9o=r(EHr,"ElectraForSequenceClassification"),EHr.forEach(t),G9o=r(rEe," (ELECTRA model)"),rEe.forEach(t),X9o=i(L),tb=n(L,"LI",{});var tEe=s(tb);Ate=n(tEe,"STRONG",{});var yHr=s(Ate);V9o=r(yHr,"flaubert"),yHr.forEach(t),z9o=r(tEe," \u2014 "),xI=n(tEe,"A",{href:!0});var wHr=s(xI);W9o=r(wHr,"FlaubertForSequenceClassification"),wHr.forEach(t),Q9o=r(tEe," (FlauBERT model)"),tEe.forEach(t),H9o=i(L),ab=n(L,"LI",{});var aEe=s(ab);Lte=n(aEe,"STRONG",{});var AHr=s(Lte);U9o=r(AHr,"fnet"),AHr.forEach(t),J9o=r(aEe," \u2014 "),kI=n(aEe,"A",{href:!0});var LHr=s(kI);Y9o=r(LHr,"FNetForSequenceClassification"),LHr.forEach(t),K9o=r(aEe," (FNet model)"),aEe.forEach(t),Z9o=i(L),nb=n(L,"LI",{});var nEe=s(nb);Bte=n(nEe,"STRONG",{});var BHr=s(Bte);eCo=r(BHr,"funnel"),BHr.forEach(t),oCo=r(nEe," \u2014 "),RI=n(nEe,"A",{href:!0});var xHr=s(RI);rCo=r(xHr,"FunnelForSequenceClassification"),xHr.forEach(t),tCo=r(nEe," (Funnel Transformer model)"),nEe.forEach(t),aCo=i(L),sb=n(L,"LI",{});var sEe=s(sb);xte=n(sEe,"STRONG",{});var kHr=s(xte);nCo=r(kHr,"gpt2"),kHr.forEach(t),sCo=r(sEe," \u2014 "),SI=n(sEe,"A",{href:!0});var RHr=s(SI);lCo=r(RHr,"GPT2ForSequenceClassification"),RHr.forEach(t),iCo=r(sEe," (OpenAI GPT-2 model)"),sEe.forEach(t),dCo=i(L),lb=n(L,"LI",{});var lEe=s(lb);kte=n(lEe,"STRONG",{});var SHr=s(kte);cCo=r(SHr,"gpt_neo"),SHr.forEach(t),fCo=r(lEe," \u2014 "),PI=n(lEe,"A",{href:!0});var PHr=s(PI);mCo=r(PHr,"GPTNeoForSequenceClassification"),PHr.forEach(t),gCo=r(lEe," (GPT Neo model)"),lEe.forEach(t),hCo=i(L),ib=n(L,"LI",{});var iEe=s(ib);Rte=n(iEe,"STRONG",{});var $Hr=s(Rte);pCo=r($Hr,"gptj"),$Hr.forEach(t),_Co=r(iEe," \u2014 "),$I=n(iEe,"A",{href:!0});var IHr=s($I);uCo=r(IHr,"GPTJForSequenceClassification"),IHr.forEach(t),bCo=r(iEe," (GPT-J 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"),NI=n(mEe,"A",{href:!0});var VHr=s(NI);jCo=r(VHr,"LEDForSequenceClassification"),VHr.forEach(t),DCo=r(mEe," (LED model)"),mEe.forEach(t),NCo=i(L),gb=n(L,"LI",{});var gEe=s(gb);jte=n(gEe,"STRONG",{});var zHr=s(jte);qCo=r(zHr,"longformer"),zHr.forEach(t),OCo=r(gEe," \u2014 "),qI=n(gEe,"A",{href:!0});var WHr=s(qI);GCo=r(WHr,"LongformerForSequenceClassification"),WHr.forEach(t),XCo=r(gEe," (Longformer model)"),gEe.forEach(t),VCo=i(L),hb=n(L,"LI",{});var hEe=s(hb);Dte=n(hEe,"STRONG",{});var QHr=s(Dte);zCo=r(QHr,"mbart"),QHr.forEach(t),WCo=r(hEe," \u2014 "),OI=n(hEe,"A",{href:!0});var HHr=s(OI);QCo=r(HHr,"MBartForSequenceClassification"),HHr.forEach(t),HCo=r(hEe," (mBART model)"),hEe.forEach(t),UCo=i(L),pb=n(L,"LI",{});var pEe=s(pb);Nte=n(pEe,"STRONG",{});var UHr=s(Nte);JCo=r(UHr,"megatron-bert"),UHr.forEach(t),YCo=r(pEe," \u2014 "),GI=n(pEe,"A",{href:!0});var JHr=s(GI);KCo=r(JHr,"MegatronBertForSequenceClassification"),JHr.forEach(t),ZCo=r(pEe," (MegatronBert model)"),pEe.forEach(t),eMo=i(L),_b=n(L,"LI",{});var _Ee=s(_b);qte=n(_Ee,"STRONG",{});var YHr=s(qte);oMo=r(YHr,"mobilebert"),YHr.forEach(t),rMo=r(_Ee," \u2014 "),XI=n(_Ee,"A",{href:!0});var KHr=s(XI);tMo=r(KHr,"MobileBertForSequenceClassification"),KHr.forEach(t),aMo=r(_Ee," (MobileBERT model)"),_Ee.forEach(t),nMo=i(L),ub=n(L,"LI",{});var uEe=s(ub);Ote=n(uEe,"STRONG",{});var ZHr=s(Ote);sMo=r(ZHr,"mpnet"),ZHr.forEach(t),lMo=r(uEe," \u2014 "),VI=n(uEe,"A",{href:!0});var eUr=s(VI);iMo=r(eUr,"MPNetForSequenceClassification"),eUr.forEach(t),dMo=r(uEe," (MPNet model)"),uEe.forEach(t),cMo=i(L),bb=n(L,"LI",{});var bEe=s(bb);Gte=n(bEe,"STRONG",{});var oUr=s(Gte);fMo=r(oUr,"nystromformer"),oUr.forEach(t),mMo=r(bEe," \u2014 "),zI=n(bEe,"A",{href:!0});var rUr=s(zI);gMo=r(rUr,"NystromformerForSequenceClassification"),rUr.forEach(t),hMo=r(bEe," (Nystromformer model)"),bEe.forEach(t),pMo=i(L),vb=n(L,"LI",{});var vEe=s(vb);Xte=n(vEe,"STRONG",{});var tUr=s(Xte);_Mo=r(tUr,"openai-gpt"),tUr.forEach(t),uMo=r(vEe," \u2014 "),WI=n(vEe,"A",{href:!0});var aUr=s(WI);bMo=r(aUr,"OpenAIGPTForSequenceClassification"),aUr.forEach(t),vMo=r(vEe," (OpenAI GPT model)"),vEe.forEach(t),TMo=i(L),Tb=n(L,"LI",{});var TEe=s(Tb);Vte=n(TEe,"STRONG",{});var nUr=s(Vte);FMo=r(nUr,"perceiver"),nUr.forEach(t),CMo=r(TEe," \u2014 "),QI=n(TEe,"A",{href:!0});var sUr=s(QI);MMo=r(sUr,"PerceiverForSequenceClassification"),sUr.forEach(t),EMo=r(TEe," (Perceiver model)"),TEe.forEach(t),yMo=i(L),Fb=n(L,"LI",{});var FEe=s(Fb);zte=n(FEe,"STRONG",{});var lUr=s(zte);wMo=r(lUr,"plbart"),lUr.forEach(t),AMo=r(FEe," \u2014 "),HI=n(FEe,"A",{href:!0});var iUr=s(HI);LMo=r(iUr,"PLBartForSequenceClassification"),iUr.forEach(t),BMo=r(FEe," (PLBart model)"),FEe.forEach(t),xMo=i(L),Cb=n(L,"LI",{});var CEe=s(Cb);Wte=n(CEe,"STRONG",{});var dUr=s(Wte);kMo=r(dUr,"qdqbert"),dUr.forEach(t),RMo=r(CEe," \u2014 "),UI=n(CEe,"A",{href:!0});var cUr=s(UI);SMo=r(cUr,"QDQBertForSequenceClassification"),cUr.forEach(t),PMo=r(CEe," (QDQBert model)"),CEe.forEach(t),$Mo=i(L),Mb=n(L,"LI",{});var MEe=s(Mb);Qte=n(MEe,"STRONG",{});var fUr=s(Qte);IMo=r(fUr,"reformer"),fUr.forEach(t),jMo=r(MEe," \u2014 "),JI=n(MEe,"A",{href:!0});var mUr=s(JI);DMo=r(mUr,"ReformerForSequenceClassification"),mUr.forEach(t),NMo=r(MEe," (Reformer model)"),MEe.forEach(t),qMo=i(L),Eb=n(L,"LI",{});var EEe=s(Eb);Hte=n(EEe,"STRONG",{});var gUr=s(Hte);OMo=r(gUr,"rembert"),gUr.forEach(t),GMo=r(EEe," \u2014 "),YI=n(EEe,"A",{href:!0});var hUr=s(YI);XMo=r(hUr,"RemBertForSequenceClassification"),hUr.forEach(t),VMo=r(EEe," (RemBERT model)"),EEe.forEach(t),zMo=i(L),yb=n(L,"LI",{});var yEe=s(yb);Ute=n(yEe,"STRONG",{});var pUr=s(Ute);WMo=r(pUr,"roberta"),pUr.forEach(t),QMo=r(yEe," \u2014 "),KI=n(yEe,"A",{href:!0});var _Ur=s(KI);HMo=r(_Ur,"RobertaForSequenceClassification"),_Ur.forEach(t),UMo=r(yEe," (RoBERTa model)"),yEe.forEach(t),JMo=i(L),wb=n(L,"LI",{});var wEe=s(wb);Jte=n(wEe,"STRONG",{});var uUr=s(Jte);YMo=r(uUr,"roformer"),uUr.forEach(t),KMo=r(wEe," \u2014 "),ZI=n(wEe,"A",{href:!0});var bUr=s(ZI);ZMo=r(bUr,"RoFormerForSequenceClassification"),bUr.forEach(t),e4o=r(wEe," (RoFormer model)"),wEe.forEach(t),o4o=i(L),Ab=n(L,"LI",{});var AEe=s(Ab);Yte=n(AEe,"STRONG",{});var vUr=s(Yte);r4o=r(vUr,"squeezebert"),vUr.forEach(t),t4o=r(AEe," \u2014 "),ej=n(AEe,"A",{href:!0});var TUr=s(ej);a4o=r(TUr,"SqueezeBertForSequenceClassification"),TUr.forEach(t),n4o=r(AEe," (SqueezeBERT model)"),AEe.forEach(t),s4o=i(L),Lb=n(L,"LI",{});var LEe=s(Lb);Kte=n(LEe,"STRONG",{});var FUr=s(Kte);l4o=r(FUr,"tapas"),FUr.forEach(t),i4o=r(LEe," \u2014 "),oj=n(LEe,"A",{href:!0});var CUr=s(oj);d4o=r(CUr,"TapasForSequenceClassification"),CUr.forEach(t),c4o=r(LEe," (TAPAS model)"),LEe.forEach(t),f4o=i(L),Bb=n(L,"LI",{});var BEe=s(Bb);Zte=n(BEe,"STRONG",{});var MUr=s(Zte);m4o=r(MUr,"transfo-xl"),MUr.forEach(t),g4o=r(BEe," \u2014 "),rj=n(BEe,"A",{href:!0});var EUr=s(rj);h4o=r(EUr,"TransfoXLForSequenceClassification"),EUr.forEach(t),p4o=r(BEe," (Transformer-XL model)"),BEe.forEach(t),_4o=i(L),xb=n(L,"LI",{});var xEe=s(xb);eae=n(xEe,"STRONG",{});var yUr=s(eae);u4o=r(yUr,"xlm"),yUr.forEach(t),b4o=r(xEe," \u2014 "),tj=n(xEe,"A",{href:!0});var wUr=s(tj);v4o=r(wUr,"XLMForSequenceClassification"),wUr.forEach(t),T4o=r(xEe," (XLM model)"),xEe.forEach(t),F4o=i(L),kb=n(L,"LI",{});var kEe=s(kb);oae=n(kEe,"STRONG",{});var AUr=s(oae);C4o=r(AUr,"xlm-roberta"),AUr.forEach(t),M4o=r(kEe," \u2014 "),aj=n(kEe,"A",{href:!0});var LUr=s(aj);E4o=r(LUr,"XLMRobertaForSequenceClassification"),LUr.forEach(t),y4o=r(kEe," (XLM-RoBERTa model)"),kEe.forEach(t),w4o=i(L),Rb=n(L,"LI",{});var REe=s(Rb);rae=n(REe,"STRONG",{});var BUr=s(rae);A4o=r(BUr,"xlm-roberta-xl"),BUr.forEach(t),L4o=r(REe," \u2014 "),nj=n(REe,"A",{href:!0});var xUr=s(nj);B4o=r(xUr,"XLMRobertaXLForSequenceClassification"),xUr.forEach(t),x4o=r(REe," (XLM-RoBERTa-XL model)"),REe.forEach(t),k4o=i(L),Sb=n(L,"LI",{});var SEe=s(Sb);tae=n(SEe,"STRONG",{});var kUr=s(tae);R4o=r(kUr,"xlnet"),kUr.forEach(t),S4o=r(SEe," \u2014 "),sj=n(SEe,"A",{href:!0});var RUr=s(sj);P4o=r(RUr,"XLNetForSequenceClassification"),RUr.forEach(t),$4o=r(SEe," (XLNet model)"),SEe.forEach(t),I4o=i(L),Pb=n(L,"LI",{});var PEe=s(Pb);aae=n(PEe,"STRONG",{});var SUr=s(aae);j4o=r(SUr,"yoso"),SUr.forEach(t),D4o=r(PEe," \u2014 "),lj=n(PEe,"A",{href:!0});var PUr=s(lj);N4o=r(PUr,"YosoForSequenceClassification"),PUr.forEach(t),q4o=r(PEe," (YOSO model)"),PEe.forEach(t),L.forEach(t),O4o=i(zt),$b=n(zt,"P",{});var $Ee=s($b);G4o=r($Ee,"The model is set in evaluation mode by default using "),nae=n($Ee,"CODE",{});var $Ur=s(nae);X4o=r($Ur,"model.eval()"),$Ur.forEach(t),V4o=r($Ee,` (so for instance, dropout modules are
deactivated). To train the model, you should first set it back in training mode with `),sae=n($Ee,"CODE",{});var IUr=s(sae);z4o=r(IUr,"model.train()"),IUr.forEach(t),$Ee.forEach(t),W4o=i(zt),lae=n(zt,"P",{});var jUr=s(lae);Q4o=r(jUr,"Examples:"),jUr.forEach(t),H4o=i(zt),m(hE.$$.fragment,zt),zt.forEach(t),rl.forEach(t),kxe=i(d),pd=n(d,"H2",{class:!0});var qRe=s(pd);Ib=n(qRe,"A",{id:!0,class:!0,href:!0});var DUr=s(Ib);iae=n(DUr,"SPAN",{});var NUr=s(iae);m(pE.$$.fragment,NUr),NUr.forEach(t),DUr.forEach(t),U4o=i(qRe),dae=n(qRe,"SPAN",{});var qUr=s(dae);J4o=r(qUr,"AutoModelForMultipleChoice"),qUr.forEach(t),qRe.forEach(t),Rxe=i(d),rr=n(d,"DIV",{class:!0});var al=s(rr);m(_E.$$.fragment,al),Y4o=i(al),_d=n(al,"P",{});var jz=s(_d);K4o=r(jz,`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 `),cae=n(jz,"CODE",{});var OUr=s(cae);Z4o=r(OUr,"from_pretrained()"),OUr.forEach(t),eEo=r(jz,"class method or the "),fae=n(jz,"CODE",{});var GUr=s(fae);oEo=r(GUr,"from_config()"),GUr.forEach(t),rEo=r(jz,`class
method.`),jz.forEach(t),tEo=i(al),uE=n(al,"P",{});var ORe=s(uE);aEo=r(ORe,"This class cannot be instantiated directly using "),mae=n(ORe,"CODE",{});var XUr=s(mae);nEo=r(XUr,"__init__()"),XUr.forEach(t),sEo=r(ORe," (throws an error)."),ORe.forEach(t),lEo=i(al),Jr=n(al,"DIV",{class:!0});var nl=s(Jr);m(bE.$$.fragment,nl),iEo=i(nl),gae=n(nl,"P",{});var VUr=s(gae);dEo=r(VUr,"Instantiates one of the model classes of the library (with a multiple choice head) from a configuration."),VUr.forEach(t),cEo=i(nl),ud=n(nl,"P",{});var Dz=s(ud);fEo=r(Dz,`Note:
Loading a model from its configuration file does `),hae=n(Dz,"STRONG",{});var zUr=s(hae);mEo=r(zUr,"not"),zUr.forEach(t),gEo=r(Dz,` load the model weights. It only affects the
model\u2019s configuration. Use `),pae=n(Dz,"CODE",{});var WUr=s(pae);hEo=r(WUr,"from_pretrained()"),WUr.forEach(t),pEo=r(Dz,"to load the model weights."),Dz.forEach(t),_Eo=i(nl),_ae=n(nl,"P",{});var QUr=s(_ae);uEo=r(QUr,"Examples:"),QUr.forEach(t),bEo=i(nl),m(vE.$$.fragment,nl),nl.forEach(t),vEo=i(al),Xe=n(al,"DIV",{class:!0});var Wt=s(Xe);m(TE.$$.fragment,Wt),TEo=i(Wt),uae=n(Wt,"P",{});var HUr=s(uae);FEo=r(HUr,"Instantiate one of the model classes of the library (with a multiple choice head) from a pretrained model."),HUr.forEach(t),CEo=i(Wt),Ja=n(Wt,"P",{});var tM=s(Ja);MEo=r(tM,"The model class to instantiate is selected based on the "),bae=n(tM,"CODE",{});var UUr=s(bae);EEo=r(UUr,"model_type"),UUr.forEach(t),yEo=r(tM,` property of the config object (either
passed as an argument or loaded from `),vae=n(tM,"CODE",{});var JUr=s(vae);wEo=r(JUr,"pretrained_model_name_or_path"),JUr.forEach(t),AEo=r(tM,` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),Tae=n(tM,"CODE",{});var YUr=s(Tae);LEo=r(YUr,"pretrained_model_name_or_path"),YUr.forEach(t),BEo=r(tM,":"),tM.forEach(t),xEo=i(Wt),O=n(Wt,"UL",{});var G=s(O);jb=n(G,"LI",{});var IEe=s(jb);Fae=n(IEe,"STRONG",{});var KUr=s(Fae);kEo=r(KUr,"albert"),KUr.forEach(t),REo=r(IEe," \u2014 "),ij=n(IEe,"A",{href:!0});var ZUr=s(ij);SEo=r(ZUr,"AlbertForMultipleChoice"),ZUr.forEach(t),PEo=r(IEe," (ALBERT model)"),IEe.forEach(t),$Eo=i(G),Db=n(G,"LI",{});var jEe=s(Db);Cae=n(jEe,"STRONG",{});var eJr=s(Cae);IEo=r(eJr,"bert"),eJr.forEach(t),jEo=r(jEe," \u2014 "),dj=n(jEe,"A",{href:!0});var oJr=s(dj);DEo=r(oJr,"BertForMultipleChoice"),oJr.forEach(t),NEo=r(jEe," (BERT model)"),jEe.forEach(t),qEo=i(G),Nb=n(G,"LI",{});var DEe=s(Nb);Mae=n(DEe,"STRONG",{});var rJr=s(Mae);OEo=r(rJr,"big_bird"),rJr.forEach(t),GEo=r(DEe," \u2014 "),cj=n(DEe,"A",{href:!0});var tJr=s(cj);XEo=r(tJr,"BigBirdForMultipleChoice"),tJr.forEach(t),VEo=r(DEe," (BigBird model)"),DEe.forEach(t),zEo=i(G),qb=n(G,"LI",{});var NEe=s(qb);Eae=n(NEe,"STRONG",{});var aJr=s(Eae);WEo=r(aJr,"camembert"),aJr.forEach(t),QEo=r(NEe," \u2014 "),fj=n(NEe,"A",{href:!0});var nJr=s(fj);HEo=r(nJr,"CamembertForMultipleChoice"),nJr.forEach(t),UEo=r(NEe," (CamemBERT model)"),NEe.forEach(t),JEo=i(G),Ob=n(G,"LI",{});var qEe=s(Ob);yae=n(qEe,"STRONG",{});var sJr=s(yae);YEo=r(sJr,"canine"),sJr.forEach(t),KEo=r(qEe," \u2014 "),mj=n(qEe,"A",{href:!0});var lJr=s(mj);ZEo=r(lJr,"CanineForMultipleChoice"),lJr.forEach(t),eyo=r(qEe," (Canine model)"),qEe.forEach(t),oyo=i(G),Gb=n(G,"LI",{});var OEe=s(Gb);wae=n(OEe,"STRONG",{});var iJr=s(wae);ryo=r(iJr,"convbert"),iJr.forEach(t),tyo=r(OEe," \u2014 "),gj=n(OEe,"A",{href:!0});var dJr=s(gj);ayo=r(dJr,"ConvBertForMultipleChoice"),dJr.forEach(t),nyo=r(OEe," (ConvBERT model)"),OEe.forEach(t),syo=i(G),Xb=n(G,"LI",{});var GEe=s(Xb);Aae=n(GEe,"STRONG",{});var cJr=s(Aae);lyo=r(cJr,"data2vec-text"),cJr.forEach(t),iyo=r(GEe," \u2014 "),hj=n(GEe,"A",{href:!0});var fJr=s(hj);dyo=r(fJr,"Data2VecTextForMultipleChoice"),fJr.forEach(t),cyo=r(GEe," (Data2VecText model)"),GEe.forEach(t),fyo=i(G),Vb=n(G,"LI",{});var XEe=s(Vb);Lae=n(XEe,"STRONG",{});var mJr=s(Lae);myo=r(mJr,"distilbert"),mJr.forEach(t),gyo=r(XEe," \u2014 "),pj=n(XEe,"A",{href:!0});var gJr=s(pj);hyo=r(gJr,"DistilBertForMultipleChoice"),gJr.forEach(t),pyo=r(XEe," (DistilBERT model)"),XEe.forEach(t),_yo=i(G),zb=n(G,"LI",{});var VEe=s(zb);Bae=n(VEe,"STRONG",{});var hJr=s(Bae);uyo=r(hJr,"electra"),hJr.forEach(t),byo=r(VEe," \u2014 "),_j=n(VEe,"A",{href:!0});var pJr=s(_j);vyo=r(pJr,"ElectraForMultipleChoice"),pJr.forEach(t),Tyo=r(VEe," (ELECTRA model)"),VEe.forEach(t),Fyo=i(G),Wb=n(G,"LI",{});var zEe=s(Wb);xae=n(zEe,"STRONG",{});var _Jr=s(xae);Cyo=r(_Jr,"flaubert"),_Jr.forEach(t),Myo=r(zEe," \u2014 "),uj=n(zEe,"A",{href:!0});var uJr=s(uj);Eyo=r(uJr,"FlaubertForMultipleChoice"),uJr.forEach(t),yyo=r(zEe," (FlauBERT model)"),zEe.forEach(t),wyo=i(G),Qb=n(G,"LI",{});var WEe=s(Qb);kae=n(WEe,"STRONG",{});var bJr=s(kae);Ayo=r(bJr,"fnet"),bJr.forEach(t),Lyo=r(WEe," \u2014 "),bj=n(WEe,"A",{href:!0});var vJr=s(bj);Byo=r(vJr,"FNetForMultipleChoice"),vJr.forEach(t),xyo=r(WEe," (FNet model)"),WEe.forEach(t),kyo=i(G),Hb=n(G,"LI",{});var QEe=s(Hb);Rae=n(QEe,"STRONG",{});var TJr=s(Rae);Ryo=r(TJr,"funnel"),TJr.forEach(t),Syo=r(QEe," \u2014 "),vj=n(QEe,"A",{href:!0});var FJr=s(vj);Pyo=r(FJr,"FunnelForMultipleChoice"),FJr.forEach(t),$yo=r(QEe," (Funnel Transformer model)"),QEe.forEach(t),Iyo=i(G),Ub=n(G,"LI",{});var HEe=s(Ub);Sae=n(HEe,"STRONG",{});var CJr=s(Sae);jyo=r(CJr,"ibert"),CJr.forEach(t),Dyo=r(HEe," \u2014 "),Tj=n(HEe,"A",{href:!0});var MJr=s(Tj);Nyo=r(MJr,"IBertForMultipleChoice"),MJr.forEach(t),qyo=r(HEe," (I-BERT model)"),HEe.forEach(t),Oyo=i(G),Jb=n(G,"LI",{});var UEe=s(Jb);Pae=n(UEe,"STRONG",{});var EJr=s(Pae);Gyo=r(EJr,"longformer"),EJr.forEach(t),Xyo=r(UEe," \u2014 "),Fj=n(UEe,"A",{href:!0});var yJr=s(Fj);Vyo=r(yJr,"LongformerForMultipleChoice"),yJr.forEach(t),zyo=r(UEe," (Longformer model)"),UEe.forEach(t),Wyo=i(G),Yb=n(G,"LI",{});var JEe=s(Yb);$ae=n(JEe,"STRONG",{});var wJr=s($ae);Qyo=r(wJr,"megatron-bert"),wJr.forEach(t),Hyo=r(JEe," \u2014 "),Cj=n(JEe,"A",{href:!0});var AJr=s(Cj);Uyo=r(AJr,"MegatronBertForMultipleChoice"),AJr.forEach(t),Jyo=r(JEe," (MegatronBert model)"),JEe.forEach(t),Yyo=i(G),Kb=n(G,"LI",{});var YEe=s(Kb);Iae=n(YEe,"STRONG",{});var LJr=s(Iae);Kyo=r(LJr,"mobilebert"),LJr.forEach(t),Zyo=r(YEe," \u2014 "),Mj=n(YEe,"A",{href:!0});var BJr=s(Mj);ewo=r(BJr,"MobileBertForMultipleChoice"),BJr.forEach(t),owo=r(YEe," (MobileBERT model)"),YEe.forEach(t),rwo=i(G),Zb=n(G,"LI",{});var KEe=s(Zb);jae=n(KEe,"STRONG",{});var xJr=s(jae);two=r(xJr,"mpnet"),xJr.forEach(t),awo=r(KEe," \u2014 "),Ej=n(KEe,"A",{href:!0});var kJr=s(Ej);nwo=r(kJr,"MPNetForMultipleChoice"),kJr.forEach(t),swo=r(KEe," (MPNet model)"),KEe.forEach(t),lwo=i(G),e3=n(G,"LI",{});var ZEe=s(e3);Dae=n(ZEe,"STRONG",{});var RJr=s(Dae);iwo=r(RJr,"nystromformer"),RJr.forEach(t),dwo=r(ZEe," \u2014 "),yj=n(ZEe,"A",{href:!0});var SJr=s(yj);cwo=r(SJr,"NystromformerForMultipleChoice"),SJr.forEach(t),fwo=r(ZEe," (Nystromformer model)"),ZEe.forEach(t),mwo=i(G),o3=n(G,"LI",{});var eye=s(o3);Nae=n(eye,"STRONG",{});var PJr=s(Nae);gwo=r(PJr,"qdqbert"),PJr.forEach(t),hwo=r(eye," \u2014 "),wj=n(eye,"A",{href:!0});var $Jr=s(wj);pwo=r($Jr,"QDQBertForMultipleChoice"),$Jr.forEach(t),_wo=r(eye," (QDQBert model)"),eye.forEach(t),uwo=i(G),r3=n(G,"LI",{});var oye=s(r3);qae=n(oye,"STRONG",{});var IJr=s(qae);bwo=r(IJr,"rembert"),IJr.forEach(t),vwo=r(oye," \u2014 "),Aj=n(oye,"A",{href:!0});var jJr=s(Aj);Two=r(jJr,"RemBertForMultipleChoice"),jJr.forEach(t),Fwo=r(oye," (RemBERT model)"),oye.forEach(t),Cwo=i(G),t3=n(G,"LI",{});var rye=s(t3);Oae=n(rye,"STRONG",{});var DJr=s(Oae);Mwo=r(DJr,"roberta"),DJr.forEach(t),Ewo=r(rye," \u2014 "),Lj=n(rye,"A",{href:!0});var NJr=s(Lj);ywo=r(NJr,"RobertaForMultipleChoice"),NJr.forEach(t),wwo=r(rye," (RoBERTa model)"),rye.forEach(t),Awo=i(G),a3=n(G,"LI",{});var tye=s(a3);Gae=n(tye,"STRONG",{});var qJr=s(Gae);Lwo=r(qJr,"roformer"),qJr.forEach(t),Bwo=r(tye," \u2014 "),Bj=n(tye,"A",{href:!0});var OJr=s(Bj);xwo=r(OJr,"RoFormerForMultipleChoice"),OJr.forEach(t),kwo=r(tye," (RoFormer model)"),tye.forEach(t),Rwo=i(G),n3=n(G,"LI",{});var aye=s(n3);Xae=n(aye,"STRONG",{});var GJr=s(Xae);Swo=r(GJr,"squeezebert"),GJr.forEach(t),Pwo=r(aye," \u2014 "),xj=n(aye,"A",{href:!0});var XJr=s(xj);$wo=r(XJr,"SqueezeBertForMultipleChoice"),XJr.forEach(t),Iwo=r(aye," (SqueezeBERT model)"),aye.forEach(t),jwo=i(G),s3=n(G,"LI",{});var nye=s(s3);Vae=n(nye,"STRONG",{});var VJr=s(Vae);Dwo=r(VJr,"xlm"),VJr.forEach(t),Nwo=r(nye," \u2014 "),kj=n(nye,"A",{href:!0});var zJr=s(kj);qwo=r(zJr,"XLMForMultipleChoice"),zJr.forEach(t),Owo=r(nye," (XLM model)"),nye.forEach(t),Gwo=i(G),l3=n(G,"LI",{});var sye=s(l3);zae=n(sye,"STRONG",{});var WJr=s(zae);Xwo=r(WJr,"xlm-roberta"),WJr.forEach(t),Vwo=r(sye," \u2014 "),Rj=n(sye,"A",{href:!0});var QJr=s(Rj);zwo=r(QJr,"XLMRobertaForMultipleChoice"),QJr.forEach(t),Wwo=r(sye," (XLM-RoBERTa model)"),sye.forEach(t),Qwo=i(G),i3=n(G,"LI",{});var lye=s(i3);Wae=n(lye,"STRONG",{});var HJr=s(Wae);Hwo=r(HJr,"xlm-roberta-xl"),HJr.forEach(t),Uwo=r(lye," \u2014 "),Sj=n(lye,"A",{href:!0});var UJr=s(Sj);Jwo=r(UJr,"XLMRobertaXLForMultipleChoice"),UJr.forEach(t),Ywo=r(lye," (XLM-RoBERTa-XL model)"),lye.forEach(t),Kwo=i(G),d3=n(G,"LI",{});var iye=s(d3);Qae=n(iye,"STRONG",{});var JJr=s(Qae);Zwo=r(JJr,"xlnet"),JJr.forEach(t),e6o=r(iye," \u2014 "),Pj=n(iye,"A",{href:!0});var YJr=s(Pj);o6o=r(YJr,"XLNetForMultipleChoice"),YJr.forEach(t),r6o=r(iye," (XLNet model)"),iye.forEach(t),t6o=i(G),c3=n(G,"LI",{});var dye=s(c3);Hae=n(dye,"STRONG",{});var KJr=s(Hae);a6o=r(KJr,"yoso"),KJr.forEach(t),n6o=r(dye," \u2014 "),$j=n(dye,"A",{href:!0});var ZJr=s($j);s6o=r(ZJr,"YosoForMultipleChoice"),ZJr.forEach(t),l6o=r(dye," (YOSO model)"),dye.forEach(t),G.forEach(t),i6o=i(Wt),f3=n(Wt,"P",{});var cye=s(f3);d6o=r(cye,"The model is set in evaluation mode by default using "),Uae=n(cye,"CODE",{});var eYr=s(Uae);c6o=r(eYr,"model.eval()"),eYr.forEach(t),f6o=r(cye,` (so for instance, dropout modules are
deactivated). To train the model, you should first set it back in training mode with `),Jae=n(cye,"CODE",{});var oYr=s(Jae);m6o=r(oYr,"model.train()"),oYr.forEach(t),cye.forEach(t),g6o=i(Wt),Yae=n(Wt,"P",{});var rYr=s(Yae);h6o=r(rYr,"Examples:"),rYr.forEach(t),p6o=i(Wt),m(FE.$$.fragment,Wt),Wt.forEach(t),al.forEach(t),Sxe=i(d),bd=n(d,"H2",{class:!0});var GRe=s(bd);m3=n(GRe,"A",{id:!0,class:!0,href:!0});var tYr=s(m3);Kae=n(tYr,"SPAN",{});var aYr=s(Kae);m(CE.$$.fragment,aYr),aYr.forEach(t),tYr.forEach(t),_6o=i(GRe),Zae=n(GRe,"SPAN",{});var nYr=s(Zae);u6o=r(nYr,"AutoModelForNextSentencePrediction"),nYr.forEach(t),GRe.forEach(t),Pxe=i(d),tr=n(d,"DIV",{class:!0});var sl=s(tr);m(ME.$$.fragment,sl),b6o=i(sl),vd=n(sl,"P",{});var Nz=s(vd);v6o=r(Nz,`This is a generic model class that will be instantiated as one of the model classes of the library (with a next sentence prediction head) when created
with the `),ene=n(Nz,"CODE",{});var sYr=s(ene);T6o=r(sYr,"from_pretrained()"),sYr.forEach(t),F6o=r(Nz,"class method or the "),one=n(Nz,"CODE",{});var lYr=s(one);C6o=r(lYr,"from_config()"),lYr.forEach(t),M6o=r(Nz,`class
method.`),Nz.forEach(t),E6o=i(sl),EE=n(sl,"P",{});var XRe=s(EE);y6o=r(XRe,"This class cannot be instantiated directly using "),rne=n(XRe,"CODE",{});var iYr=s(rne);w6o=r(iYr,"__init__()"),iYr.forEach(t),A6o=r(XRe," (throws an error)."),XRe.forEach(t),L6o=i(sl),Yr=n(sl,"DIV",{class:!0});var ll=s(Yr);m(yE.$$.fragment,ll),B6o=i(ll),tne=n(ll,"P",{});var dYr=s(tne);x6o=r(dYr,"Instantiates one of the model classes of the library (with a next sentence prediction head) from a configuration."),dYr.forEach(t),k6o=i(ll),Td=n(ll,"P",{});var qz=s(Td);R6o=r(qz,`Note:
Loading a model from its configuration file does `),ane=n(qz,"STRONG",{});var cYr=s(ane);S6o=r(cYr,"not"),cYr.forEach(t),P6o=r(qz,` load the model weights. It only affects the
model\u2019s configuration. Use `),nne=n(qz,"CODE",{});var fYr=s(nne);$6o=r(fYr,"from_pretrained()"),fYr.forEach(t),I6o=r(qz,"to load the model weights."),qz.forEach(t),j6o=i(ll),sne=n(ll,"P",{});var mYr=s(sne);D6o=r(mYr,"Examples:"),mYr.forEach(t),N6o=i(ll),m(wE.$$.fragment,ll),ll.forEach(t),q6o=i(sl),Ve=n(sl,"DIV",{class:!0});var Qt=s(Ve);m(AE.$$.fragment,Qt),O6o=i(Qt),lne=n(Qt,"P",{});var gYr=s(lne);G6o=r(gYr,"Instantiate one of the model classes of the library (with a next sentence prediction head) from a pretrained model."),gYr.forEach(t),X6o=i(Qt),Ya=n(Qt,"P",{});var aM=s(Ya);V6o=r(aM,"The model class to instantiate is selected based on the "),ine=n(aM,"CODE",{});var hYr=s(ine);z6o=r(hYr,"model_type"),hYr.forEach(t),W6o=r(aM,` property of the config object (either
passed as an argument or loaded from `),dne=n(aM,"CODE",{});var pYr=s(dne);Q6o=r(pYr,"pretrained_model_name_or_path"),pYr.forEach(t),H6o=r(aM,` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),cne=n(aM,"CODE",{});var _Yr=s(cne);U6o=r(_Yr,"pretrained_model_name_or_path"),_Yr.forEach(t),J6o=r(aM,":"),aM.forEach(t),Y6o=i(Qt),da=n(Qt,"UL",{});var il=s(da);g3=n(il,"LI",{});var fye=s(g3);fne=n(fye,"STRONG",{});var uYr=s(fne);K6o=r(uYr,"bert"),uYr.forEach(t),Z6o=r(fye," \u2014 "),Ij=n(fye,"A",{href:!0});var bYr=s(Ij);eAo=r(bYr,"BertForNextSentencePrediction"),bYr.forEach(t),oAo=r(fye," (BERT model)"),fye.forEach(t),rAo=i(il),h3=n(il,"LI",{});var mye=s(h3);mne=n(mye,"STRONG",{});var vYr=s(mne);tAo=r(vYr,"fnet"),vYr.forEach(t),aAo=r(mye," \u2014 "),jj=n(mye,"A",{href:!0});var TYr=s(jj);nAo=r(TYr,"FNetForNextSentencePrediction"),TYr.forEach(t),sAo=r(mye," (FNet model)"),mye.forEach(t),lAo=i(il),p3=n(il,"LI",{});var gye=s(p3);gne=n(gye,"STRONG",{});var FYr=s(gne);iAo=r(FYr,"megatron-bert"),FYr.forEach(t),dAo=r(gye," \u2014 "),Dj=n(gye,"A",{href:!0});var CYr=s(Dj);cAo=r(CYr,"MegatronBertForNextSentencePrediction"),CYr.forEach(t),fAo=r(gye," (MegatronBert model)"),gye.forEach(t),mAo=i(il),_3=n(il,"LI",{});var hye=s(_3);hne=n(hye,"STRONG",{});var MYr=s(hne);gAo=r(MYr,"mobilebert"),MYr.forEach(t),hAo=r(hye," \u2014 "),Nj=n(hye,"A",{href:!0});var EYr=s(Nj);pAo=r(EYr,"MobileBertForNextSentencePrediction"),EYr.forEach(t),_Ao=r(hye," (MobileBERT model)"),hye.forEach(t),uAo=i(il),u3=n(il,"LI",{});var pye=s(u3);pne=n(pye,"STRONG",{});var yYr=s(pne);bAo=r(yYr,"qdqbert"),yYr.forEach(t),vAo=r(pye," \u2014 "),qj=n(pye,"A",{href:!0});var wYr=s(qj);TAo=r(wYr,"QDQBertForNextSentencePrediction"),wYr.forEach(t),FAo=r(pye," (QDQBert model)"),pye.forEach(t),il.forEach(t),CAo=i(Qt),b3=n(Qt,"P",{});var _ye=s(b3);MAo=r(_ye,"The model is set in evaluation mode by default using "),_ne=n(_ye,"CODE",{});var AYr=s(_ne);EAo=r(AYr,"model.eval()"),AYr.forEach(t),yAo=r(_ye,` (so for instance, dropout modules are
deactivated). To train the model, you should first set it back in training mode with `),une=n(_ye,"CODE",{});var LYr=s(une);wAo=r(LYr,"model.train()"),LYr.forEach(t),_ye.forEach(t),AAo=i(Qt),bne=n(Qt,"P",{});var BYr=s(bne);LAo=r(BYr,"Examples:"),BYr.forEach(t),BAo=i(Qt),m(LE.$$.fragment,Qt),Qt.forEach(t),sl.forEach(t),$xe=i(d),Fd=n(d,"H2",{class:!0});var VRe=s(Fd);v3=n(VRe,"A",{id:!0,class:!0,href:!0});var xYr=s(v3);vne=n(xYr,"SPAN",{});var kYr=s(vne);m(BE.$$.fragment,kYr),kYr.forEach(t),xYr.forEach(t),xAo=i(VRe),Tne=n(VRe,"SPAN",{});var RYr=s(Tne);kAo=r(RYr,"AutoModelForTokenClassification"),RYr.forEach(t),VRe.forEach(t),Ixe=i(d),ar=n(d,"DIV",{class:!0});var dl=s(ar);m(xE.$$.fragment,dl),RAo=i(dl),Cd=n(dl,"P",{});var Oz=s(Cd);SAo=r(Oz,`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 `),Fne=n(Oz,"CODE",{});var SYr=s(Fne);PAo=r(SYr,"from_pretrained()"),SYr.forEach(t),$Ao=r(Oz,"class method or the "),Cne=n(Oz,"CODE",{});var PYr=s(Cne);IAo=r(PYr,"from_config()"),PYr.forEach(t),jAo=r(Oz,`class
method.`),Oz.forEach(t),DAo=i(dl),kE=n(dl,"P",{});var zRe=s(kE);NAo=r(zRe,"This class cannot be instantiated directly using "),Mne=n(zRe,"CODE",{});var $Yr=s(Mne);qAo=r($Yr,"__init__()"),$Yr.forEach(t),OAo=r(zRe," (throws an error)."),zRe.forEach(t),GAo=i(dl),Kr=n(dl,"DIV",{class:!0});var cl=s(Kr);m(RE.$$.fragment,cl),XAo=i(cl),Ene=n(cl,"P",{});var IYr=s(Ene);VAo=r(IYr,"Instantiates one of the model classes of the library (with a token classification head) from a configuration."),IYr.forEach(t),zAo=i(cl),Md=n(cl,"P",{});var Gz=s(Md);WAo=r(Gz,`Note:
Loading a model from its configuration file does `),yne=n(Gz,"STRONG",{});var jYr=s(yne);QAo=r(jYr,"not"),jYr.forEach(t),HAo=r(Gz,` load the model weights. It only affects the
model\u2019s configuration. Use `),wne=n(Gz,"CODE",{});var DYr=s(wne);UAo=r(DYr,"from_pretrained()"),DYr.forEach(t),JAo=r(Gz,"to load the model weights."),Gz.forEach(t),YAo=i(cl),Ane=n(cl,"P",{});var NYr=s(Ane);KAo=r(NYr,"Examples:"),NYr.forEach(t),ZAo=i(cl),m(SE.$$.fragment,cl),cl.forEach(t),e0o=i(dl),ze=n(dl,"DIV",{class:!0});var Ht=s(ze);m(PE.$$.fragment,Ht),o0o=i(Ht),Lne=n(Ht,"P",{});var qYr=s(Lne);r0o=r(qYr,"Instantiate one of the model classes of the library (with a token classification head) from a pretrained model."),qYr.forEach(t),t0o=i(Ht),Ka=n(Ht,"P",{});var nM=s(Ka);a0o=r(nM,"The model class to instantiate is selected based on the "),Bne=n(nM,"CODE",{});var OYr=s(Bne);n0o=r(OYr,"model_type"),OYr.forEach(t),s0o=r(nM,` property of the config object (either
passed as an argument or loaded from `),xne=n(nM,"CODE",{});var GYr=s(xne);l0o=r(GYr,"pretrained_model_name_or_path"),GYr.forEach(t),i0o=r(nM,` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),kne=n(nM,"CODE",{});var XYr=s(kne);d0o=r(XYr,"pretrained_model_name_or_path"),XYr.forEach(t),c0o=r(nM,":"),nM.forEach(t),f0o=i(Ht),N=n(Ht,"UL",{});var q=s(N);T3=n(q,"LI",{});var uye=s(T3);Rne=n(uye,"STRONG",{});var VYr=s(Rne);m0o=r(VYr,"albert"),VYr.forEach(t),g0o=r(uye," \u2014 "),Oj=n(uye,"A",{href:!0});var zYr=s(Oj);h0o=r(zYr,"AlbertForTokenClassification"),zYr.forEach(t),p0o=r(uye," (ALBERT model)"),uye.forEach(t),_0o=i(q),F3=n(q,"LI",{});var bye=s(F3);Sne=n(bye,"STRONG",{});var WYr=s(Sne);u0o=r(WYr,"bert"),WYr.forEach(t),b0o=r(bye," \u2014 "),Gj=n(bye,"A",{href:!0});var QYr=s(Gj);v0o=r(QYr,"BertForTokenClassification"),QYr.forEach(t),T0o=r(bye," (BERT model)"),bye.forEach(t),F0o=i(q),C3=n(q,"LI",{});var vye=s(C3);Pne=n(vye,"STRONG",{});var HYr=s(Pne);C0o=r(HYr,"big_bird"),HYr.forEach(t),M0o=r(vye," \u2014 "),Xj=n(vye,"A",{href:!0});var UYr=s(Xj);E0o=r(UYr,"BigBirdForTokenClassification"),UYr.forEach(t),y0o=r(vye," (BigBird model)"),vye.forEach(t),w0o=i(q),M3=n(q,"LI",{});var Tye=s(M3);$ne=n(Tye,"STRONG",{});var JYr=s($ne);A0o=r(JYr,"camembert"),JYr.forEach(t),L0o=r(Tye," \u2014 "),Vj=n(Tye,"A",{href:!0});var YYr=s(Vj);B0o=r(YYr,"CamembertForTokenClassification"),YYr.forEach(t),x0o=r(Tye," (CamemBERT model)"),Tye.forEach(t),k0o=i(q),E3=n(q,"LI",{});var Fye=s(E3);Ine=n(Fye,"STRONG",{});var KYr=s(Ine);R0o=r(KYr,"canine"),KYr.forEach(t),S0o=r(Fye," \u2014 "),zj=n(Fye,"A",{href:!0});var ZYr=s(zj);P0o=r(ZYr,"CanineForTokenClassification"),ZYr.forEach(t),$0o=r(Fye," (Canine model)"),Fye.forEach(t),I0o=i(q),y3=n(q,"LI",{});var Cye=s(y3);jne=n(Cye,"STRONG",{});var eKr=s(jne);j0o=r(eKr,"convbert"),eKr.forEach(t),D0o=r(Cye," \u2014 "),Wj=n(Cye,"A",{href:!0});var oKr=s(Wj);N0o=r(oKr,"ConvBertForTokenClassification"),oKr.forEach(t),q0o=r(Cye," (ConvBERT model)"),Cye.forEach(t),O0o=i(q),w3=n(q,"LI",{});var Mye=s(w3);Dne=n(Mye,"STRONG",{});var rKr=s(Dne);G0o=r(rKr,"data2vec-text"),rKr.forEach(t),X0o=r(Mye," \u2014 "),Qj=n(Mye,"A",{href:!0});var tKr=s(Qj);V0o=r(tKr,"Data2VecTextForTokenClassification"),tKr.forEach(t),z0o=r(Mye," (Data2VecText model)"),Mye.forEach(t),W0o=i(q),A3=n(q,"LI",{});var Eye=s(A3);Nne=n(Eye,"STRONG",{});var aKr=s(Nne);Q0o=r(aKr,"deberta"),aKr.forEach(t),H0o=r(Eye," \u2014 "),Hj=n(Eye,"A",{href:!0});var nKr=s(Hj);U0o=r(nKr,"DebertaForTokenClassification"),nKr.forEach(t),J0o=r(Eye," (DeBERTa model)"),Eye.forEach(t),Y0o=i(q),L3=n(q,"LI",{});var yye=s(L3);qne=n(yye,"STRONG",{});var sKr=s(qne);K0o=r(sKr,"deberta-v2"),sKr.forEach(t),Z0o=r(yye," \u2014 "),Uj=n(yye,"A",{href:!0});var lKr=s(Uj);eLo=r(lKr,"DebertaV2ForTokenClassification"),lKr.forEach(t),oLo=r(yye," (DeBERTa-v2 model)"),yye.forEach(t),rLo=i(q),B3=n(q,"LI",{});var wye=s(B3);One=n(wye,"STRONG",{});var iKr=s(One);tLo=r(iKr,"distilbert"),iKr.forEach(t),aLo=r(wye," \u2014 "),Jj=n(wye,"A",{href:!0});var dKr=s(Jj);nLo=r(dKr,"DistilBertForTokenClassification"),dKr.forEach(t),sLo=r(wye," (DistilBERT model)"),wye.forEach(t),lLo=i(q),x3=n(q,"LI",{});var Aye=s(x3);Gne=n(Aye,"STRONG",{});var cKr=s(Gne);iLo=r(cKr,"electra"),cKr.forEach(t),dLo=r(Aye," \u2014 "),Yj=n(Aye,"A",{href:!0});var fKr=s(Yj);cLo=r(fKr,"ElectraForTokenClassification"),fKr.forEach(t),fLo=r(Aye," (ELECTRA model)"),Aye.forEach(t),mLo=i(q),k3=n(q,"LI",{});var Lye=s(k3);Xne=n(Lye,"STRONG",{});var mKr=s(Xne);gLo=r(mKr,"flaubert"),mKr.forEach(t),hLo=r(Lye," \u2014 "),Kj=n(Lye,"A",{href:!0});var gKr=s(Kj);pLo=r(gKr,"FlaubertForTokenClassification"),gKr.forEach(t),_Lo=r(Lye," (FlauBERT model)"),Lye.forEach(t),uLo=i(q),R3=n(q,"LI",{});var Bye=s(R3);Vne=n(Bye,"STRONG",{});var hKr=s(Vne);bLo=r(hKr,"fnet"),hKr.forEach(t),vLo=r(Bye," \u2014 "),Zj=n(Bye,"A",{href:!0});var pKr=s(Zj);TLo=r(pKr,"FNetForTokenClassification"),pKr.forEach(t),FLo=r(Bye," (FNet model)"),Bye.forEach(t),CLo=i(q),S3=n(q,"LI",{});var xye=s(S3);zne=n(xye,"STRONG",{});var _Kr=s(zne);MLo=r(_Kr,"funnel"),_Kr.forEach(t),ELo=r(xye," \u2014 "),eD=n(xye,"A",{href:!0});var uKr=s(eD);yLo=r(uKr,"FunnelForTokenClassification"),uKr.forEach(t),wLo=r(xye," (Funnel Transformer model)"),xye.forEach(t),ALo=i(q),P3=n(q,"LI",{});var kye=s(P3);Wne=n(kye,"STRONG",{});var bKr=s(Wne);LLo=r(bKr,"gpt2"),bKr.forEach(t),BLo=r(kye," \u2014 "),oD=n(kye,"A",{href:!0});var vKr=s(oD);xLo=r(vKr,"GPT2ForTokenClassification"),vKr.forEach(t),kLo=r(kye," (OpenAI GPT-2 model)"),kye.forEach(t),RLo=i(q),$3=n(q,"LI",{});var Rye=s($3);Qne=n(Rye,"STRONG",{});var TKr=s(Qne);SLo=r(TKr,"ibert"),TKr.forEach(t),PLo=r(Rye," \u2014 "),rD=n(Rye,"A",{href:!0});var FKr=s(rD);$Lo=r(FKr,"IBertForTokenClassification"),FKr.forEach(t),ILo=r(Rye," (I-BERT model)"),Rye.forEach(t),jLo=i(q),I3=n(q,"LI",{});var Sye=s(I3);Hne=n(Sye,"STRONG",{});var CKr=s(Hne);DLo=r(CKr,"layoutlm"),CKr.forEach(t),NLo=r(Sye," \u2014 "),tD=n(Sye,"A",{href:!0});var MKr=s(tD);qLo=r(MKr,"LayoutLMForTokenClassification"),MKr.forEach(t),OLo=r(Sye," (LayoutLM model)"),Sye.forEach(t),GLo=i(q),j3=n(q,"LI",{});var Pye=s(j3);Une=n(Pye,"STRONG",{});var EKr=s(Une);XLo=r(EKr,"layoutlmv2"),EKr.forEach(t),VLo=r(Pye," \u2014 "),aD=n(Pye,"A",{href:!0});var yKr=s(aD);zLo=r(yKr,"LayoutLMv2ForTokenClassification"),yKr.forEach(t),WLo=r(Pye," (LayoutLMv2 model)"),Pye.forEach(t),QLo=i(q),D3=n(q,"LI",{});var $ye=s(D3);Jne=n($ye,"STRONG",{});var wKr=s(Jne);HLo=r(wKr,"longformer"),wKr.forEach(t),ULo=r($ye," \u2014 "),nD=n($ye,"A",{href:!0});var AKr=s(nD);JLo=r(AKr,"LongformerForTokenClassification"),AKr.forEach(t),YLo=r($ye," (Longformer model)"),$ye.forEach(t),KLo=i(q),N3=n(q,"LI",{});var Iye=s(N3);Yne=n(Iye,"STRONG",{});var LKr=s(Yne);ZLo=r(LKr,"megatron-bert"),LKr.forEach(t),e7o=r(Iye," \u2014 "),sD=n(Iye,"A",{href:!0});var BKr=s(sD);o7o=r(BKr,"MegatronBertForTokenClassification"),BKr.forEach(t),r7o=r(Iye," (MegatronBert model)"),Iye.forEach(t),t7o=i(q),q3=n(q,"LI",{});var jye=s(q3);Kne=n(jye,"STRONG",{});var xKr=s(Kne);a7o=r(xKr,"mobilebert"),xKr.forEach(t),n7o=r(jye," \u2014 "),lD=n(jye,"A",{href:!0});var kKr=s(lD);s7o=r(kKr,"MobileBertForTokenClassification"),kKr.forEach(t),l7o=r(jye," (MobileBERT model)"),jye.forEach(t),i7o=i(q),O3=n(q,"LI",{});var Dye=s(O3);Zne=n(Dye,"STRONG",{});var RKr=s(Zne);d7o=r(RKr,"mpnet"),RKr.forEach(t),c7o=r(Dye," \u2014 "),iD=n(Dye,"A",{href:!0});var SKr=s(iD);f7o=r(SKr,"MPNetForTokenClassification"),SKr.forEach(t),m7o=r(Dye," (MPNet model)"),Dye.forEach(t),g7o=i(q),G3=n(q,"LI",{});var Nye=s(G3);ese=n(Nye,"STRONG",{});var PKr=s(ese);h7o=r(PKr,"nystromformer"),PKr.forEach(t),p7o=r(Nye," \u2014 "),dD=n(Nye,"A",{href:!0});var $Kr=s(dD);_7o=r($Kr,"NystromformerForTokenClassification"),$Kr.forEach(t),u7o=r(Nye," (Nystromformer model)"),Nye.forEach(t),b7o=i(q),X3=n(q,"LI",{});var qye=s(X3);ose=n(qye,"STRONG",{});var IKr=s(ose);v7o=r(IKr,"qdqbert"),IKr.forEach(t),T7o=r(qye," \u2014 "),cD=n(qye,"A",{href:!0});var jKr=s(cD);F7o=r(jKr,"QDQBertForTokenClassification"),jKr.forEach(t),C7o=r(qye," (QDQBert model)"),qye.forEach(t),M7o=i(q),V3=n(q,"LI",{});var Oye=s(V3);rse=n(Oye,"STRONG",{});var DKr=s(rse);E7o=r(DKr,"rembert"),DKr.forEach(t),y7o=r(Oye," \u2014 "),fD=n(Oye,"A",{href:!0});var NKr=s(fD);w7o=r(NKr,"RemBertForTokenClassification"),NKr.forEach(t),A7o=r(Oye," (RemBERT model)"),Oye.forEach(t),L7o=i(q),z3=n(q,"LI",{});var Gye=s(z3);tse=n(Gye,"STRONG",{});var qKr=s(tse);B7o=r(qKr,"roberta"),qKr.forEach(t),x7o=r(Gye," \u2014 "),mD=n(Gye,"A",{href:!0});var OKr=s(mD);k7o=r(OKr,"RobertaForTokenClassification"),OKr.forEach(t),R7o=r(Gye," (RoBERTa model)"),Gye.forEach(t),S7o=i(q),W3=n(q,"LI",{});var Xye=s(W3);ase=n(Xye,"STRONG",{});var GKr=s(ase);P7o=r(GKr,"roformer"),GKr.forEach(t),$7o=r(Xye," \u2014 "),gD=n(Xye,"A",{href:!0});var XKr=s(gD);I7o=r(XKr,"RoFormerForTokenClassification"),XKr.forEach(t),j7o=r(Xye," (RoFormer model)"),Xye.forEach(t),D7o=i(q),Q3=n(q,"LI",{});var Vye=s(Q3);nse=n(Vye,"STRONG",{});var VKr=s(nse);N7o=r(VKr,"squeezebert"),VKr.forEach(t),q7o=r(Vye," \u2014 "),hD=n(Vye,"A",{href:!0});var zKr=s(hD);O7o=r(zKr,"SqueezeBertForTokenClassification"),zKr.forEach(t),G7o=r(Vye," (SqueezeBERT model)"),Vye.forEach(t),X7o=i(q),H3=n(q,"LI",{});var zye=s(H3);sse=n(zye,"STRONG",{});var WKr=s(sse);V7o=r(WKr,"xlm"),WKr.forEach(t),z7o=r(zye," \u2014 "),pD=n(zye,"A",{href:!0});var QKr=s(pD);W7o=r(QKr,"XLMForTokenClassification"),QKr.forEach(t),Q7o=r(zye," (XLM model)"),zye.forEach(t),H7o=i(q),U3=n(q,"LI",{});var Wye=s(U3);lse=n(Wye,"STRONG",{});var HKr=s(lse);U7o=r(HKr,"xlm-roberta"),HKr.forEach(t),J7o=r(Wye," \u2014 "),_D=n(Wye,"A",{href:!0});var UKr=s(_D);Y7o=r(UKr,"XLMRobertaForTokenClassification"),UKr.forEach(t),K7o=r(Wye," (XLM-RoBERTa model)"),Wye.forEach(t),Z7o=i(q),J3=n(q,"LI",{});var Qye=s(J3);ise=n(Qye,"STRONG",{});var JKr=s(ise);eBo=r(JKr,"xlm-roberta-xl"),JKr.forEach(t),oBo=r(Qye," \u2014 "),uD=n(Qye,"A",{href:!0});var YKr=s(uD);rBo=r(YKr,"XLMRobertaXLForTokenClassification"),YKr.forEach(t),tBo=r(Qye," (XLM-RoBERTa-XL model)"),Qye.forEach(t),aBo=i(q),Y3=n(q,"LI",{});var Hye=s(Y3);dse=n(Hye,"STRONG",{});var KKr=s(dse);nBo=r(KKr,"xlnet"),KKr.forEach(t),sBo=r(Hye," \u2014 "),bD=n(Hye,"A",{href:!0});var ZKr=s(bD);lBo=r(ZKr,"XLNetForTokenClassification"),ZKr.forEach(t),iBo=r(Hye," (XLNet model)"),Hye.forEach(t),dBo=i(q),K3=n(q,"LI",{});var Uye=s(K3);cse=n(Uye,"STRONG",{});var eZr=s(cse);cBo=r(eZr,"yoso"),eZr.forEach(t),fBo=r(Uye," \u2014 "),vD=n(Uye,"A",{href:!0});var oZr=s(vD);mBo=r(oZr,"YosoForTokenClassification"),oZr.forEach(t),gBo=r(Uye," (YOSO model)"),Uye.forEach(t),q.forEach(t),hBo=i(Ht),Z3=n(Ht,"P",{});var Jye=s(Z3);pBo=r(Jye,"The model is set in evaluation mode by default using "),fse=n(Jye,"CODE",{});var rZr=s(fse);_Bo=r(rZr,"model.eval()"),rZr.forEach(t),uBo=r(Jye,` (so for instance, dropout modules are
deactivated). To train the model, you should first set it back in training mode with `),mse=n(Jye,"CODE",{});var tZr=s(mse);bBo=r(tZr,"model.train()"),tZr.forEach(t),Jye.forEach(t),vBo=i(Ht),gse=n(Ht,"P",{});var aZr=s(gse);TBo=r(aZr,"Examples:"),aZr.forEach(t),FBo=i(Ht),m($E.$$.fragment,Ht),Ht.forEach(t),dl.forEach(t),jxe=i(d),Ed=n(d,"H2",{class:!0});var WRe=s(Ed);e5=n(WRe,"A",{id:!0,class:!0,href:!0});var nZr=s(e5);hse=n(nZr,"SPAN",{});var sZr=s(hse);m(IE.$$.fragment,sZr),sZr.forEach(t),nZr.forEach(t),CBo=i(WRe),pse=n(WRe,"SPAN",{});var lZr=s(pse);MBo=r(lZr,"AutoModelForQuestionAnswering"),lZr.forEach(t),WRe.forEach(t),Dxe=i(d),nr=n(d,"DIV",{class:!0});var fl=s(nr);m(jE.$$.fragment,fl),EBo=i(fl),yd=n(fl,"P",{});var Xz=s(yd);yBo=r(Xz,`This is a generic model class that will be instantiated as one of the model classes of the library (with a question answering head) when created
with the `),_se=n(Xz,"CODE",{});var iZr=s(_se);wBo=r(iZr,"from_pretrained()"),iZr.forEach(t),ABo=r(Xz,"class method or the "),use=n(Xz,"CODE",{});var dZr=s(use);LBo=r(dZr,"from_config()"),dZr.forEach(t),BBo=r(Xz,`class
method.`),Xz.forEach(t),xBo=i(fl),DE=n(fl,"P",{});var QRe=s(DE);kBo=r(QRe,"This class cannot be instantiated directly using "),bse=n(QRe,"CODE",{});var cZr=s(bse);RBo=r(cZr,"__init__()"),cZr.forEach(t),SBo=r(QRe," (throws an error)."),QRe.forEach(t),PBo=i(fl),Zr=n(fl,"DIV",{class:!0});var ml=s(Zr);m(NE.$$.fragment,ml),$Bo=i(ml),vse=n(ml,"P",{});var fZr=s(vse);IBo=r(fZr,"Instantiates one of the model classes of the library (with a question answering head) from a configuration."),fZr.forEach(t),jBo=i(ml),wd=n(ml,"P",{});var Vz=s(wd);DBo=r(Vz,`Note:
Loading a model from its configuration file does `),Tse=n(Vz,"STRONG",{});var mZr=s(Tse);NBo=r(mZr,"not"),mZr.forEach(t),qBo=r(Vz,` load the model weights. It only affects the
model\u2019s configuration. Use `),Fse=n(Vz,"CODE",{});var gZr=s(Fse);OBo=r(gZr,"from_pretrained()"),gZr.forEach(t),GBo=r(Vz,"to load the model weights."),Vz.forEach(t),XBo=i(ml),Cse=n(ml,"P",{});var hZr=s(Cse);VBo=r(hZr,"Examples:"),hZr.forEach(t),zBo=i(ml),m(qE.$$.fragment,ml),ml.forEach(t),WBo=i(fl),We=n(fl,"DIV",{class:!0});var Ut=s(We);m(OE.$$.fragment,Ut),QBo=i(Ut),Mse=n(Ut,"P",{});var pZr=s(Mse);HBo=r(pZr,"Instantiate one of the model classes of the library (with a question answering head) from a pretrained model."),pZr.forEach(t),UBo=i(Ut),Za=n(Ut,"P",{});var sM=s(Za);JBo=r(sM,"The model class to instantiate is selected based on the "),Ese=n(sM,"CODE",{});var _Zr=s(Ese);YBo=r(_Zr,"model_type"),_Zr.forEach(t),KBo=r(sM,` property of the config object (either
passed as an argument or loaded from `),yse=n(sM,"CODE",{});var uZr=s(yse);ZBo=r(uZr,"pretrained_model_name_or_path"),uZr.forEach(t),exo=r(sM,` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),wse=n(sM,"CODE",{});var bZr=s(wse);oxo=r(bZr,"pretrained_model_name_or_path"),bZr.forEach(t),rxo=r(sM,":"),sM.forEach(t),txo=i(Ut),R=n(Ut,"UL",{});var P=s(R);o5=n(P,"LI",{});var Yye=s(o5);Ase=n(Yye,"STRONG",{});var vZr=s(Ase);axo=r(vZr,"albert"),vZr.forEach(t),nxo=r(Yye," \u2014 "),TD=n(Yye,"A",{href:!0});var TZr=s(TD);sxo=r(TZr,"AlbertForQuestionAnswering"),TZr.forEach(t),lxo=r(Yye," (ALBERT model)"),Yye.forEach(t),ixo=i(P),r5=n(P,"LI",{});var Kye=s(r5);Lse=n(Kye,"STRONG",{});var FZr=s(Lse);dxo=r(FZr,"bart"),FZr.forEach(t),cxo=r(Kye," \u2014 "),FD=n(Kye,"A",{href:!0});var CZr=s(FD);fxo=r(CZr,"BartForQuestionAnswering"),CZr.forEach(t),mxo=r(Kye," (BART model)"),Kye.forEach(t),gxo=i(P),t5=n(P,"LI",{});var Zye=s(t5);Bse=n(Zye,"STRONG",{});var MZr=s(Bse);hxo=r(MZr,"bert"),MZr.forEach(t),pxo=r(Zye," \u2014 "),CD=n(Zye,"A",{href:!0});var EZr=s(CD);_xo=r(EZr,"BertForQuestionAnswering"),EZr.forEach(t),uxo=r(Zye," (BERT model)"),Zye.forEach(t),bxo=i(P),a5=n(P,"LI",{});var ewe=s(a5);xse=n(ewe,"STRONG",{});var yZr=s(xse);vxo=r(yZr,"big_bird"),yZr.forEach(t),Txo=r(ewe," \u2014 "),MD=n(ewe,"A",{href:!0});var wZr=s(MD);Fxo=r(wZr,"BigBirdForQuestionAnswering"),wZr.forEach(t),Cxo=r(ewe," (BigBird model)"),ewe.forEach(t),Mxo=i(P),n5=n(P,"LI",{});var owe=s(n5);kse=n(owe,"STRONG",{});var AZr=s(kse);Exo=r(AZr,"bigbird_pegasus"),AZr.forEach(t),yxo=r(owe," \u2014 "),ED=n(owe,"A",{href:!0});var LZr=s(ED);wxo=r(LZr,"BigBirdPegasusForQuestionAnswering"),LZr.forEach(t),Axo=r(owe," (BigBirdPegasus model)"),owe.forEach(t),Lxo=i(P),s5=n(P,"LI",{});var rwe=s(s5);Rse=n(rwe,"STRONG",{});var BZr=s(Rse);Bxo=r(BZr,"camembert"),BZr.forEach(t),xxo=r(rwe," \u2014 "),yD=n(rwe,"A",{href:!0});var xZr=s(yD);kxo=r(xZr,"CamembertForQuestionAnswering"),xZr.forEach(t),Rxo=r(rwe," (CamemBERT model)"),rwe.forEach(t),Sxo=i(P),l5=n(P,"LI",{});var twe=s(l5);Sse=n(twe,"STRONG",{});var kZr=s(Sse);Pxo=r(kZr,"canine"),kZr.forEach(t),$xo=r(twe," \u2014 "),wD=n(twe,"A",{href:!0});var RZr=s(wD);Ixo=r(RZr,"CanineForQuestionAnswering"),RZr.forEach(t),jxo=r(twe," (Canine model)"),twe.forEach(t),Dxo=i(P),i5=n(P,"LI",{});var awe=s(i5);Pse=n(awe,"STRONG",{});var SZr=s(Pse);Nxo=r(SZr,"convbert"),SZr.forEach(t),qxo=r(awe," \u2014 "),AD=n(awe,"A",{href:!0});var PZr=s(AD);Oxo=r(PZr,"ConvBertForQuestionAnswering"),PZr.forEach(t),Gxo=r(awe," (ConvBERT model)"),awe.forEach(t),Xxo=i(P),d5=n(P,"LI",{});var nwe=s(d5);$se=n(nwe,"STRONG",{});var $Zr=s($se);Vxo=r($Zr,"data2vec-text"),$Zr.forEach(t),zxo=r(nwe," \u2014 "),LD=n(nwe,"A",{href:!0});var IZr=s(LD);Wxo=r(IZr,"Data2VecTextForQuestionAnswering"),IZr.forEach(t),Qxo=r(nwe," (Data2VecText model)"),nwe.forEach(t),Hxo=i(P),c5=n(P,"LI",{});var swe=s(c5);Ise=n(swe,"STRONG",{});var jZr=s(Ise);Uxo=r(jZr,"deberta"),jZr.forEach(t),Jxo=r(swe," \u2014 "),BD=n(swe,"A",{href:!0});var DZr=s(BD);Yxo=r(DZr,"DebertaForQuestionAnswering"),DZr.forEach(t),Kxo=r(swe," (DeBERTa model)"),swe.forEach(t),Zxo=i(P),f5=n(P,"LI",{});var lwe=s(f5);jse=n(lwe,"STRONG",{});var NZr=s(jse);eko=r(NZr,"deberta-v2"),NZr.forEach(t),oko=r(lwe," \u2014 "),xD=n(lwe,"A",{href:!0});var qZr=s(xD);rko=r(qZr,"DebertaV2ForQuestionAnswering"),qZr.forEach(t),tko=r(lwe," (DeBERTa-v2 model)"),lwe.forEach(t),ako=i(P),m5=n(P,"LI",{});var iwe=s(m5);Dse=n(iwe,"STRONG",{});var OZr=s(Dse);nko=r(OZr,"distilbert"),OZr.forEach(t),sko=r(iwe," \u2014 "),kD=n(iwe,"A",{href:!0});var GZr=s(kD);lko=r(GZr,"DistilBertForQuestionAnswering"),GZr.forEach(t),iko=r(iwe," (DistilBERT model)"),iwe.forEach(t),dko=i(P),g5=n(P,"LI",{});var dwe=s(g5);Nse=n(dwe,"STRONG",{});var XZr=s(Nse);cko=r(XZr,"electra"),XZr.forEach(t),fko=r(dwe," \u2014 "),RD=n(dwe,"A",{href:!0});var VZr=s(RD);mko=r(VZr,"ElectraForQuestionAnswering"),VZr.forEach(t),gko=r(dwe," (ELECTRA model)"),dwe.forEach(t),hko=i(P),h5=n(P,"LI",{});var cwe=s(h5);qse=n(cwe,"STRONG",{});var zZr=s(qse);pko=r(zZr,"flaubert"),zZr.forEach(t),_ko=r(cwe," \u2014 "),SD=n(cwe,"A",{href:!0});var WZr=s(SD);uko=r(WZr,"FlaubertForQuestionAnsweringSimple"),WZr.forEach(t),bko=r(cwe," (FlauBERT model)"),cwe.forEach(t),vko=i(P),p5=n(P,"LI",{});var fwe=s(p5);Ose=n(fwe,"STRONG",{});var QZr=s(Ose);Tko=r(QZr,"fnet"),QZr.forEach(t),Fko=r(fwe," \u2014 "),PD=n(fwe,"A",{href:!0});var HZr=s(PD);Cko=r(HZr,"FNetForQuestionAnswering"),HZr.forEach(t),Mko=r(fwe," (FNet model)"),fwe.forEach(t),Eko=i(P),_5=n(P,"LI",{});var mwe=s(_5);Gse=n(mwe,"STRONG",{});var UZr=s(Gse);yko=r(UZr,"funnel"),UZr.forEach(t),wko=r(mwe," \u2014 "),$D=n(mwe,"A",{href:!0});var JZr=s($D);Ako=r(JZr,"FunnelForQuestionAnswering"),JZr.forEach(t),Lko=r(mwe," (Funnel Transformer model)"),mwe.forEach(t),Bko=i(P),u5=n(P,"LI",{});var gwe=s(u5);Xse=n(gwe,"STRONG",{});var YZr=s(Xse);xko=r(YZr,"gptj"),YZr.forEach(t),kko=r(gwe," \u2014 "),ID=n(gwe,"A",{href:!0});var KZr=s(ID);Rko=r(KZr,"GPTJForQuestionAnswering"),KZr.forEach(t),Sko=r(gwe," (GPT-J model)"),gwe.forEach(t),Pko=i(P),b5=n(P,"LI",{});var hwe=s(b5);Vse=n(hwe,"STRONG",{});var ZZr=s(Vse);$ko=r(ZZr,"ibert"),ZZr.forEach(t),Iko=r(hwe," \u2014 "),jD=n(hwe,"A",{href:!0});var eet=s(jD);jko=r(eet,"IBertForQuestionAnswering"),eet.forEach(t),Dko=r(hwe," (I-BERT model)"),hwe.forEach(t),Nko=i(P),v5=n(P,"LI",{});var pwe=s(v5);zse=n(pwe,"STRONG",{});var oet=s(zse);qko=r(oet,"layoutlmv2"),oet.forEach(t),Oko=r(pwe," \u2014 "),DD=n(pwe,"A",{href:!0});var ret=s(DD);Gko=r(ret,"LayoutLMv2ForQuestionAnswering"),ret.forEach(t),Xko=r(pwe," (LayoutLMv2 model)"),pwe.forEach(t),Vko=i(P),T5=n(P,"LI",{});var _we=s(T5);Wse=n(_we,"STRONG",{});var tet=s(Wse);zko=r(tet,"led"),tet.forEach(t),Wko=r(_we," \u2014 "),ND=n(_we,"A",{href:!0});var aet=s(ND);Qko=r(aet,"LEDForQuestionAnswering"),aet.forEach(t),Hko=r(_we," (LED model)"),_we.forEach(t),Uko=i(P),F5=n(P,"LI",{});var uwe=s(F5);Qse=n(uwe,"STRONG",{});var net=s(Qse);Jko=r(net,"longformer"),net.forEach(t),Yko=r(uwe," \u2014 "),qD=n(uwe,"A",{href:!0});var set=s(qD);Kko=r(set,"LongformerForQuestionAnswering"),set.forEach(t),Zko=r(uwe," (Longformer model)"),uwe.forEach(t),eRo=i(P),C5=n(P,"LI",{});var bwe=s(C5);Hse=n(bwe,"STRONG",{});var iet=s(Hse);oRo=r(iet,"lxmert"),iet.forEach(t),rRo=r(bwe," \u2014 "),OD=n(bwe,"A",{href:!0});var det=s(OD);tRo=r(det,"LxmertForQuestionAnswering"),det.forEach(t),aRo=r(bwe," (LXMERT model)"),bwe.forEach(t),nRo=i(P),M5=n(P,"LI",{});var vwe=s(M5);Use=n(vwe,"STRONG",{});var cet=s(Use);sRo=r(cet,"mbart"),cet.forEach(t),lRo=r(vwe," \u2014 "),GD=n(vwe,"A",{href:!0});var fet=s(GD);iRo=r(fet,"MBartForQuestionAnswering"),fet.forEach(t),dRo=r(vwe," (mBART model)"),vwe.forEach(t),cRo=i(P),E5=n(P,"LI",{});var Twe=s(E5);Jse=n(Twe,"STRONG",{});var met=s(Jse);fRo=r(met,"megatron-bert"),met.forEach(t),mRo=r(Twe," \u2014 "),XD=n(Twe,"A",{href:!0});var get=s(XD);gRo=r(get,"MegatronBertForQuestionAnswering"),get.forEach(t),hRo=r(Twe," (MegatronBert model)"),Twe.forEach(t),pRo=i(P),y5=n(P,"LI",{});var Fwe=s(y5);Yse=n(Fwe,"STRONG",{});var het=s(Yse);_Ro=r(het,"mobilebert"),het.forEach(t),uRo=r(Fwe," \u2014 "),VD=n(Fwe,"A",{href:!0});var pet=s(VD);bRo=r(pet,"MobileBertForQuestionAnswering"),pet.forEach(t),vRo=r(Fwe," (MobileBERT model)"),Fwe.forEach(t),TRo=i(P),w5=n(P,"LI",{});var Cwe=s(w5);Kse=n(Cwe,"STRONG",{});var _et=s(Kse);FRo=r(_et,"mpnet"),_et.forEach(t),CRo=r(Cwe," \u2014 "),zD=n(Cwe,"A",{href:!0});var uet=s(zD);MRo=r(uet,"MPNetForQuestionAnswering"),uet.forEach(t),ERo=r(Cwe," (MPNet model)"),Cwe.forEach(t),yRo=i(P),A5=n(P,"LI",{});var Mwe=s(A5);Zse=n(Mwe,"STRONG",{});var bet=s(Zse);wRo=r(bet,"nystromformer"),bet.forEach(t),ARo=r(Mwe," \u2014 "),WD=n(Mwe,"A",{href:!0});var vet=s(WD);LRo=r(vet,"NystromformerForQuestionAnswering"),vet.forEach(t),BRo=r(Mwe," (Nystromformer model)"),Mwe.forEach(t),xRo=i(P),L5=n(P,"LI",{});var Ewe=s(L5);ele=n(Ewe,"STRONG",{});var Tet=s(ele);kRo=r(Tet,"qdqbert"),Tet.forEach(t),RRo=r(Ewe," \u2014 "),QD=n(Ewe,"A",{href:!0});var Fet=s(QD);SRo=r(Fet,"QDQBertForQuestionAnswering"),Fet.forEach(t),PRo=r(Ewe," (QDQBert model)"),Ewe.forEach(t),$Ro=i(P),B5=n(P,"LI",{});var ywe=s(B5);ole=n(ywe,"STRONG",{});var Cet=s(ole);IRo=r(Cet,"reformer"),Cet.forEach(t),jRo=r(ywe," \u2014 "),HD=n(ywe,"A",{href:!0});var Met=s(HD);DRo=r(Met,"ReformerForQuestionAnswering"),Met.forEach(t),NRo=r(ywe," (Reformer model)"),ywe.forEach(t),qRo=i(P),x5=n(P,"LI",{});var wwe=s(x5);rle=n(wwe,"STRONG",{});var Eet=s(rle);ORo=r(Eet,"rembert"),Eet.forEach(t),GRo=r(wwe," \u2014 "),UD=n(wwe,"A",{href:!0});var yet=s(UD);XRo=r(yet,"RemBertForQuestionAnswering"),yet.forEach(t),VRo=r(wwe," (RemBERT model)"),wwe.forEach(t),zRo=i(P),k5=n(P,"LI",{});var Awe=s(k5);tle=n(Awe,"STRONG",{});var wet=s(tle);WRo=r(wet,"roberta"),wet.forEach(t),QRo=r(Awe," \u2014 "),JD=n(Awe,"A",{href:!0});var Aet=s(JD);HRo=r(Aet,"RobertaForQuestionAnswering"),Aet.forEach(t),URo=r(Awe," (RoBERTa model)"),Awe.forEach(t),JRo=i(P),R5=n(P,"LI",{});var Lwe=s(R5);ale=n(Lwe,"STRONG",{});var Let=s(ale);YRo=r(Let,"roformer"),Let.forEach(t),KRo=r(Lwe," \u2014 "),YD=n(Lwe,"A",{href:!0});var Bet=s(YD);ZRo=r(Bet,"RoFormerForQuestionAnswering"),Bet.forEach(t),eSo=r(Lwe," (RoFormer model)"),Lwe.forEach(t),oSo=i(P),S5=n(P,"LI",{});var Bwe=s(S5);nle=n(Bwe,"STRONG",{});var xet=s(nle);rSo=r(xet,"splinter"),xet.forEach(t),tSo=r(Bwe," \u2014 "),KD=n(Bwe,"A",{href:!0});var ket=s(KD);aSo=r(ket,"SplinterForQuestionAnswering"),ket.forEach(t),nSo=r(Bwe," (Splinter model)"),Bwe.forEach(t),sSo=i(P),P5=n(P,"LI",{});var xwe=s(P5);sle=n(xwe,"STRONG",{});var Ret=s(sle);lSo=r(Ret,"squeezebert"),Ret.forEach(t),iSo=r(xwe," \u2014 "),ZD=n(xwe,"A",{href:!0});var Set=s(ZD);dSo=r(Set,"SqueezeBertForQuestionAnswering"),Set.forEach(t),cSo=r(xwe," (SqueezeBERT model)"),xwe.forEach(t),fSo=i(P),$5=n(P,"LI",{});var kwe=s($5);lle=n(kwe,"STRONG",{});var Pet=s(lle);mSo=r(Pet,"xlm"),Pet.forEach(t),gSo=r(kwe," \u2014 "),eN=n(kwe,"A",{href:!0});var $et=s(eN);hSo=r($et,"XLMForQuestionAnsweringSimple"),$et.forEach(t),pSo=r(kwe," (XLM model)"),kwe.forEach(t),_So=i(P),I5=n(P,"LI",{});var Rwe=s(I5);ile=n(Rwe,"STRONG",{});var Iet=s(ile);uSo=r(Iet,"xlm-roberta"),Iet.forEach(t),bSo=r(Rwe," \u2014 "),oN=n(Rwe,"A",{href:!0});var jet=s(oN);vSo=r(jet,"XLMRobertaForQuestionAnswering"),jet.forEach(t),TSo=r(Rwe," (XLM-RoBERTa model)"),Rwe.forEach(t),FSo=i(P),j5=n(P,"LI",{});var Swe=s(j5);dle=n(Swe,"STRONG",{});var Det=s(dle);CSo=r(Det,"xlm-roberta-xl"),Det.forEach(t),MSo=r(Swe," \u2014 "),rN=n(Swe,"A",{href:!0});var Net=s(rN);ESo=r(Net,"XLMRobertaXLForQuestionAnswering"),Net.forEach(t),ySo=r(Swe," (XLM-RoBERTa-XL model)"),Swe.forEach(t),wSo=i(P),D5=n(P,"LI",{});var Pwe=s(D5);cle=n(Pwe,"STRONG",{});var qet=s(cle);ASo=r(qet,"xlnet"),qet.forEach(t),LSo=r(Pwe," \u2014 "),tN=n(Pwe,"A",{href:!0});var Oet=s(tN);BSo=r(Oet,"XLNetForQuestionAnsweringSimple"),Oet.forEach(t),xSo=r(Pwe," (XLNet model)"),Pwe.forEach(t),kSo=i(P),N5=n(P,"LI",{});var $we=s(N5);fle=n($we,"STRONG",{});var Get=s(fle);RSo=r(Get,"yoso"),Get.forEach(t),SSo=r($we," \u2014 "),aN=n($we,"A",{href:!0});var Xet=s(aN);PSo=r(Xet,"YosoForQuestionAnswering"),Xet.forEach(t),$So=r($we," (YOSO model)"),$we.forEach(t),P.forEach(t),ISo=i(Ut),q5=n(Ut,"P",{});var Iwe=s(q5);jSo=r(Iwe,"The model is set in evaluation mode by default using "),mle=n(Iwe,"CODE",{});var Vet=s(mle);DSo=r(Vet,"model.eval()"),Vet.forEach(t),NSo=r(Iwe,` (so for instance, dropout modules are
deactivated). To train the model, you should first set it back in training mode with `),gle=n(Iwe,"CODE",{});var zet=s(gle);qSo=r(zet,"model.train()"),zet.forEach(t),Iwe.forEach(t),OSo=i(Ut),hle=n(Ut,"P",{});var Wet=s(hle);GSo=r(Wet,"Examples:"),Wet.forEach(t),XSo=i(Ut),m(GE.$$.fragment,Ut),Ut.forEach(t),fl.forEach(t),Nxe=i(d),Ad=n(d,"H2",{class:!0});var HRe=s(Ad);O5=n(HRe,"A",{id:!0,class:!0,href:!0});var Qet=s(O5);ple=n(Qet,"SPAN",{});var Het=s(ple);m(XE.$$.fragment,Het),Het.forEach(t),Qet.forEach(t),VSo=i(HRe),_le=n(HRe,"SPAN",{});var Uet=s(_le);zSo=r(Uet,"AutoModelForTableQuestionAnswering"),Uet.forEach(t),HRe.forEach(t),qxe=i(d),sr=n(d,"DIV",{class:!0});var gl=s(sr);m(VE.$$.fragment,gl),WSo=i(gl),Ld=n(gl,"P",{});var zz=s(Ld);QSo=r(zz,`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 `),ule=n(zz,"CODE",{});var Jet=s(ule);HSo=r(Jet,"from_pretrained()"),Jet.forEach(t),USo=r(zz,"class method or the "),ble=n(zz,"CODE",{});var Yet=s(ble);JSo=r(Yet,"from_config()"),Yet.forEach(t),YSo=r(zz,`class
method.`),zz.forEach(t),KSo=i(gl),zE=n(gl,"P",{});var URe=s(zE);ZSo=r(URe,"This class cannot be instantiated directly using "),vle=n(URe,"CODE",{});var Ket=s(vle);ePo=r(Ket,"__init__()"),Ket.forEach(t),oPo=r(URe," (throws an error)."),URe.forEach(t),rPo=i(gl),et=n(gl,"DIV",{class:!0});var hl=s(et);m(WE.$$.fragment,hl),tPo=i(hl),Tle=n(hl,"P",{});var Zet=s(Tle);aPo=r(Zet,"Instantiates one of the model classes of the library (with a table question answering head) from a configuration."),Zet.forEach(t),nPo=i(hl),Bd=n(hl,"P",{});var Wz=s(Bd);sPo=r(Wz,`Note:
Loading a model from its configuration file does `),Fle=n(Wz,"STRONG",{});var eot=s(Fle);lPo=r(eot,"not"),eot.forEach(t),iPo=r(Wz,` load the model weights. It only affects the
model\u2019s configuration. Use `),Cle=n(Wz,"CODE",{});var oot=s(Cle);dPo=r(oot,"from_pretrained()"),oot.forEach(t),cPo=r(Wz,"to load the model weights."),Wz.forEach(t),fPo=i(hl),Mle=n(hl,"P",{});var rot=s(Mle);mPo=r(rot,"Examples:"),rot.forEach(t),gPo=i(hl),m(QE.$$.fragment,hl),hl.forEach(t),hPo=i(gl),Qe=n(gl,"DIV",{class:!0});var Jt=s(Qe);m(HE.$$.fragment,Jt),pPo=i(Jt),Ele=n(Jt,"P",{});var tot=s(Ele);_Po=r(tot,"Instantiate one of the model classes of the library (with a table question answering head) from a pretrained model."),tot.forEach(t),uPo=i(Jt),en=n(Jt,"P",{});var lM=s(en);bPo=r(lM,"The model class to instantiate is selected based on the "),yle=n(lM,"CODE",{});var aot=s(yle);vPo=r(aot,"model_type"),aot.forEach(t),TPo=r(lM,` property of the config object (either
passed as an argument or loaded from `),wle=n(lM,"CODE",{});var not=s(wle);FPo=r(not,"pretrained_model_name_or_path"),not.forEach(t),CPo=r(lM,` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),Ale=n(lM,"CODE",{});var sot=s(Ale);MPo=r(sot,"pretrained_model_name_or_path"),sot.forEach(t),EPo=r(lM,":"),lM.forEach(t),yPo=i(Jt),Lle=n(Jt,"UL",{});var lot=s(Lle);G5=n(lot,"LI",{});var jwe=s(G5);Ble=n(jwe,"STRONG",{});var iot=s(Ble);wPo=r(iot,"tapas"),iot.forEach(t),APo=r(jwe," \u2014 "),nN=n(jwe,"A",{href:!0});var dot=s(nN);LPo=r(dot,"TapasForQuestionAnswering"),dot.forEach(t),BPo=r(jwe," (TAPAS model)"),jwe.forEach(t),lot.forEach(t),xPo=i(Jt),X5=n(Jt,"P",{});var Dwe=s(X5);kPo=r(Dwe,"The model is set in evaluation mode by default using "),xle=n(Dwe,"CODE",{});var cot=s(xle);RPo=r(cot,"model.eval()"),cot.forEach(t),SPo=r(Dwe,` (so for instance, dropout modules are
deactivated). To train the model, you should first set it back in training mode with `),kle=n(Dwe,"CODE",{});var fot=s(kle);PPo=r(fot,"model.train()"),fot.forEach(t),Dwe.forEach(t),$Po=i(Jt),Rle=n(Jt,"P",{});var mot=s(Rle);IPo=r(mot,"Examples:"),mot.forEach(t),jPo=i(Jt),m(UE.$$.fragment,Jt),Jt.forEach(t),gl.forEach(t),Oxe=i(d),xd=n(d,"H2",{class:!0});var JRe=s(xd);V5=n(JRe,"A",{id:!0,class:!0,href:!0});var got=s(V5);Sle=n(got,"SPAN",{});var hot=s(Sle);m(JE.$$.fragment,hot),hot.forEach(t),got.forEach(t),DPo=i(JRe),Ple=n(JRe,"SPAN",{});var pot=s(Ple);NPo=r(pot,"AutoModelForImageClassification"),pot.forEach(t),JRe.forEach(t),Gxe=i(d),lr=n(d,"DIV",{class:!0});var pl=s(lr);m(YE.$$.fragment,pl),qPo=i(pl),kd=n(pl,"P",{});var Qz=s(kd);OPo=r(Qz,`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 `),$le=n(Qz,"CODE",{});var _ot=s($le);GPo=r(_ot,"from_pretrained()"),_ot.forEach(t),XPo=r(Qz,"class method or the "),Ile=n(Qz,"CODE",{});var uot=s(Ile);VPo=r(uot,"from_config()"),uot.forEach(t),zPo=r(Qz,`class
method.`),Qz.forEach(t),WPo=i(pl),KE=n(pl,"P",{});var YRe=s(KE);QPo=r(YRe,"This class cannot be instantiated directly using "),jle=n(YRe,"CODE",{});var bot=s(jle);HPo=r(bot,"__init__()"),bot.forEach(t),UPo=r(YRe," (throws an error)."),YRe.forEach(t),JPo=i(pl),ot=n(pl,"DIV",{class:!0});var _l=s(ot);m(ZE.$$.fragment,_l),YPo=i(_l),Dle=n(_l,"P",{});var vot=s(Dle);KPo=r(vot,"Instantiates one of the model classes of the library (with a image classification head) from a configuration."),vot.forEach(t),ZPo=i(_l),Rd=n(_l,"P",{});var Hz=s(Rd);e$o=r(Hz,`Note:
Loading a model from its configuration file does `),Nle=n(Hz,"STRONG",{});var Tot=s(Nle);o$o=r(Tot,"not"),Tot.forEach(t),r$o=r(Hz,` load the model weights. It only affects the
model\u2019s configuration. Use `),qle=n(Hz,"CODE",{});var Fot=s(qle);t$o=r(Fot,"from_pretrained()"),Fot.forEach(t),a$o=r(Hz,"to load the model weights."),Hz.forEach(t),n$o=i(_l),Ole=n(_l,"P",{});var Cot=s(Ole);s$o=r(Cot,"Examples:"),Cot.forEach(t),l$o=i(_l),m(ey.$$.fragment,_l),_l.forEach(t),i$o=i(pl),He=n(pl,"DIV",{class:!0});var Yt=s(He);m(oy.$$.fragment,Yt),d$o=i(Yt),Gle=n(Yt,"P",{});var Mot=s(Gle);c$o=r(Mot,"Instantiate one of the model classes of the library (with a image classification head) from a pretrained model."),Mot.forEach(t),f$o=i(Yt),on=n(Yt,"P",{});var iM=s(on);m$o=r(iM,"The model class to instantiate is selected based on the "),Xle=n(iM,"CODE",{});var Eot=s(Xle);g$o=r(Eot,"model_type"),Eot.forEach(t),h$o=r(iM,` property of the config object (either
passed as an argument or loaded from `),Vle=n(iM,"CODE",{});var yot=s(Vle);p$o=r(yot,"pretrained_model_name_or_path"),yot.forEach(t),_$o=r(iM,` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),zle=n(iM,"CODE",{});var wot=s(zle);u$o=r(wot,"pretrained_model_name_or_path"),wot.forEach(t),b$o=r(iM,":"),iM.forEach(t),v$o=i(Yt),Ce=n(Yt,"UL",{});var so=s(Ce);z5=n(so,"LI",{});var Nwe=s(z5);Wle=n(Nwe,"STRONG",{});var Aot=s(Wle);T$o=r(Aot,"beit"),Aot.forEach(t),F$o=r(Nwe," \u2014 "),sN=n(Nwe,"A",{href:!0});var Lot=s(sN);C$o=r(Lot,"BeitForImageClassification"),Lot.forEach(t),M$o=r(Nwe," (BEiT model)"),Nwe.forEach(t),E$o=i(so),W5=n(so,"LI",{});var qwe=s(W5);Qle=n(qwe,"STRONG",{});var Bot=s(Qle);y$o=r(Bot,"convnext"),Bot.forEach(t),w$o=r(qwe," \u2014 "),lN=n(qwe,"A",{href:!0});var xot=s(lN);A$o=r(xot,"ConvNextForImageClassification"),xot.forEach(t),L$o=r(qwe," (ConvNext model)"),qwe.forEach(t),B$o=i(so),qs=n(so,"LI",{});var DL=s(qs);Hle=n(DL,"STRONG",{});var kot=s(Hle);x$o=r(kot,"deit"),kot.forEach(t),k$o=r(DL," \u2014 "),iN=n(DL,"A",{href:!0});var Rot=s(iN);R$o=r(Rot,"DeiTForImageClassification"),Rot.forEach(t),S$o=r(DL," or "),dN=n(DL,"A",{href:!0});var Sot=s(dN);P$o=r(Sot,"DeiTForImageClassificationWithTeacher"),Sot.forEach(t),$$o=r(DL," (DeiT model)"),DL.forEach(t),I$o=i(so),Q5=n(so,"LI",{});var Owe=s(Q5);Ule=n(Owe,"STRONG",{});var Pot=s(Ule);j$o=r(Pot,"imagegpt"),Pot.forEach(t),D$o=r(Owe," \u2014 "),cN=n(Owe,"A",{href:!0});var $ot=s(cN);N$o=r($ot,"ImageGPTForImageClassification"),$ot.forEach(t),q$o=r(Owe," (ImageGPT model)"),Owe.forEach(t),O$o=i(so),ma=n(so,"LI",{});var $f=s(ma);Jle=n($f,"STRONG",{});var Iot=s(Jle);G$o=r(Iot,"perceiver"),Iot.forEach(t),X$o=r($f," \u2014 "),fN=n($f,"A",{href:!0});var jot=s(fN);V$o=r(jot,"PerceiverForImageClassificationLearned"),jot.forEach(t),z$o=r($f," or "),mN=n($f,"A",{href:!0});var Dot=s(mN);W$o=r(Dot,"PerceiverForImageClassificationFourier"),Dot.forEach(t),Q$o=r($f," or "),gN=n($f,"A",{href:!0});var Not=s(gN);H$o=r(Not,"PerceiverForImageClassificationConvProcessing"),Not.forEach(t),U$o=r($f," (Perceiver model)"),$f.forEach(t),J$o=i(so),H5=n(so,"LI",{});var Gwe=s(H5);Yle=n(Gwe,"STRONG",{});var qot=s(Yle);Y$o=r(qot,"poolformer"),qot.forEach(t),K$o=r(Gwe," \u2014 "),hN=n(Gwe,"A",{href:!0});var Oot=s(hN);Z$o=r(Oot,"PoolFormerForImageClassification"),Oot.forEach(t),eIo=r(Gwe," (PoolFormer model)"),Gwe.forEach(t),oIo=i(so),U5=n(so,"LI",{});var Xwe=s(U5);Kle=n(Xwe,"STRONG",{});var Got=s(Kle);rIo=r(Got,"segformer"),Got.forEach(t),tIo=r(Xwe," \u2014 "),pN=n(Xwe,"A",{href:!0});var Xot=s(pN);aIo=r(Xot,"SegformerForImageClassification"),Xot.forEach(t),nIo=r(Xwe," (SegFormer model)"),Xwe.forEach(t),sIo=i(so),J5=n(so,"LI",{});var Vwe=s(J5);Zle=n(Vwe,"STRONG",{});var Vot=s(Zle);lIo=r(Vot,"swin"),Vot.forEach(t),iIo=r(Vwe," \u2014 "),_N=n(Vwe,"A",{href:!0});var zot=s(_N);dIo=r(zot,"SwinForImageClassification"),zot.forEach(t),cIo=r(Vwe," (Swin model)"),Vwe.forEach(t),fIo=i(so),Y5=n(so,"LI",{});var zwe=s(Y5);eie=n(zwe,"STRONG",{});var Wot=s(eie);mIo=r(Wot,"vit"),Wot.forEach(t),gIo=r(zwe," \u2014 "),uN=n(zwe,"A",{href:!0});var Qot=s(uN);hIo=r(Qot,"ViTForImageClassification"),Qot.forEach(t),pIo=r(zwe," (ViT model)"),zwe.forEach(t),so.forEach(t),_Io=i(Yt),K5=n(Yt,"P",{});var Wwe=s(K5);uIo=r(Wwe,"The model is set in evaluation mode by default using "),oie=n(Wwe,"CODE",{});var Hot=s(oie);bIo=r(Hot,"model.eval()"),Hot.forEach(t),vIo=r(Wwe,` (so for instance, dropout modules are
deactivated). To train the model, you should first set it back in training mode with `),rie=n(Wwe,"CODE",{});var Uot=s(rie);TIo=r(Uot,"model.train()"),Uot.forEach(t),Wwe.forEach(t),FIo=i(Yt),tie=n(Yt,"P",{});var Jot=s(tie);CIo=r(Jot,"Examples:"),Jot.forEach(t),MIo=i(Yt),m(ry.$$.fragment,Yt),Yt.forEach(t),pl.forEach(t),Xxe=i(d),Sd=n(d,"H2",{class:!0});var KRe=s(Sd);Z5=n(KRe,"A",{id:!0,class:!0,href:!0});var Yot=s(Z5);aie=n(Yot,"SPAN",{});var Kot=s(aie);m(ty.$$.fragment,Kot),Kot.forEach(t),Yot.forEach(t),EIo=i(KRe),nie=n(KRe,"SPAN",{});var Zot=s(nie);yIo=r(Zot,"AutoModelForVision2Seq"),Zot.forEach(t),KRe.forEach(t),Vxe=i(d),ir=n(d,"DIV",{class:!0});var ul=s(ir);m(ay.$$.fragment,ul),wIo=i(ul),Pd=n(ul,"P",{});var Uz=s(Pd);AIo=r(Uz,`This is a generic model class that will be instantiated as one of the model classes of the library (with a vision-to-text modeling head) when created
with the `),sie=n(Uz,"CODE",{});var ert=s(sie);LIo=r(ert,"from_pretrained()"),ert.forEach(t),BIo=r(Uz,"class method or the "),lie=n(Uz,"CODE",{});var ort=s(lie);xIo=r(ort,"from_config()"),ort.forEach(t),kIo=r(Uz,`class
method.`),Uz.forEach(t),RIo=i(ul),ny=n(ul,"P",{});var ZRe=s(ny);SIo=r(ZRe,"This class cannot be instantiated directly using "),iie=n(ZRe,"CODE",{});var rrt=s(iie);PIo=r(rrt,"__init__()"),rrt.forEach(t),$Io=r(ZRe," (throws an error)."),ZRe.forEach(t),IIo=i(ul),rt=n(ul,"DIV",{class:!0});var bl=s(rt);m(sy.$$.fragment,bl),jIo=i(bl),die=n(bl,"P",{});var trt=s(die);DIo=r(trt,"Instantiates one of the model classes of the library (with a vision-to-text modeling head) from a configuration."),trt.forEach(t),NIo=i(bl),$d=n(bl,"P",{});var Jz=s($d);qIo=r(Jz,`Note:
Loading a model from its configuration file does `),cie=n(Jz,"STRONG",{});var art=s(cie);OIo=r(art,"not"),art.forEach(t),GIo=r(Jz,` load the model weights. It only affects the
model\u2019s configuration. Use `),fie=n(Jz,"CODE",{});var nrt=s(fie);XIo=r(nrt,"from_pretrained()"),nrt.forEach(t),VIo=r(Jz,"to load the model weights."),Jz.forEach(t),zIo=i(bl),mie=n(bl,"P",{});var srt=s(mie);WIo=r(srt,"Examples:"),srt.forEach(t),QIo=i(bl),m(ly.$$.fragment,bl),bl.forEach(t),HIo=i(ul),Ue=n(ul,"DIV",{class:!0});var Kt=s(Ue);m(iy.$$.fragment,Kt),UIo=i(Kt),gie=n(Kt,"P",{});var lrt=s(gie);JIo=r(lrt,"Instantiate one of the model classes of the library (with a vision-to-text modeling head) from a pretrained model."),lrt.forEach(t),YIo=i(Kt),rn=n(Kt,"P",{});var dM=s(rn);KIo=r(dM,"The model class to instantiate is selected based on the "),hie=n(dM,"CODE",{});var irt=s(hie);ZIo=r(irt,"model_type"),irt.forEach(t),ejo=r(dM,` property of the config object (either
passed as an argument or loaded from `),pie=n(dM,"CODE",{});var drt=s(pie);ojo=r(drt,"pretrained_model_name_or_path"),drt.forEach(t),rjo=r(dM,` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),_ie=n(dM,"CODE",{});var crt=s(_ie);tjo=r(crt,"pretrained_model_name_or_path"),crt.forEach(t),ajo=r(dM,":"),dM.forEach(t),njo=i(Kt),uie=n(Kt,"UL",{});var frt=s(uie);e2=n(frt,"LI",{});var Qwe=s(e2);bie=n(Qwe,"STRONG",{});var mrt=s(bie);sjo=r(mrt,"vision-encoder-decoder"),mrt.forEach(t),ljo=r(Qwe," \u2014 "),bN=n(Qwe,"A",{href:!0});var grt=s(bN);ijo=r(grt,"VisionEncoderDecoderModel"),grt.forEach(t),djo=r(Qwe," (Vision Encoder decoder model)"),Qwe.forEach(t),frt.forEach(t),cjo=i(Kt),o2=n(Kt,"P",{});var Hwe=s(o2);fjo=r(Hwe,"The model is set in evaluation mode by default using "),vie=n(Hwe,"CODE",{});var hrt=s(vie);mjo=r(hrt,"model.eval()"),hrt.forEach(t),gjo=r(Hwe,` (so for instance, dropout modules are
deactivated). To train the model, you should first set it back in training mode with `),Tie=n(Hwe,"CODE",{});var prt=s(Tie);hjo=r(prt,"model.train()"),prt.forEach(t),Hwe.forEach(t),pjo=i(Kt),Fie=n(Kt,"P",{});var _rt=s(Fie);_jo=r(_rt,"Examples:"),_rt.forEach(t),ujo=i(Kt),m(dy.$$.fragment,Kt),Kt.forEach(t),ul.forEach(t),zxe=i(d),Id=n(d,"H2",{class:!0});var eSe=s(Id);r2=n(eSe,"A",{id:!0,class:!0,href:!0});var urt=s(r2);Cie=n(urt,"SPAN",{});var brt=s(Cie);m(cy.$$.fragment,brt),brt.forEach(t),urt.forEach(t),bjo=i(eSe),Mie=n(eSe,"SPAN",{});var vrt=s(Mie);vjo=r(vrt,"AutoModelForAudioClassification"),vrt.forEach(t),eSe.forEach(t),Wxe=i(d),dr=n(d,"DIV",{class:!0});var vl=s(dr);m(fy.$$.fragment,vl),Tjo=i(vl),jd=n(vl,"P",{});var Yz=s(jd);Fjo=r(Yz,`This is a generic model class that will be instantiated as one of the model classes of the library (with a audio classification head) when created
with the `),Eie=n(Yz,"CODE",{});var Trt=s(Eie);Cjo=r(Trt,"from_pretrained()"),Trt.forEach(t),Mjo=r(Yz,"class method or the "),yie=n(Yz,"CODE",{});var Frt=s(yie);Ejo=r(Frt,"from_config()"),Frt.forEach(t),yjo=r(Yz,`class
method.`),Yz.forEach(t),wjo=i(vl),my=n(vl,"P",{});var oSe=s(my);Ajo=r(oSe,"This class cannot be instantiated directly using "),wie=n(oSe,"CODE",{});var Crt=s(wie);Ljo=r(Crt,"__init__()"),Crt.forEach(t),Bjo=r(oSe," (throws an error)."),oSe.forEach(t),xjo=i(vl),tt=n(vl,"DIV",{class:!0});var Tl=s(tt);m(gy.$$.fragment,Tl),kjo=i(Tl),Aie=n(Tl,"P",{});var Mrt=s(Aie);Rjo=r(Mrt,"Instantiates one of the model classes of the library (with a audio classification head) from a configuration."),Mrt.forEach(t),Sjo=i(Tl),Dd=n(Tl,"P",{});var Kz=s(Dd);Pjo=r(Kz,`Note:
Loading a model from its configuration file does `),Lie=n(Kz,"STRONG",{});var Ert=s(Lie);$jo=r(Ert,"not"),Ert.forEach(t),Ijo=r(Kz,` load the model weights. It only affects the
model\u2019s configuration. Use `),Bie=n(Kz,"CODE",{});var yrt=s(Bie);jjo=r(yrt,"from_pretrained()"),yrt.forEach(t),Djo=r(Kz,"to load the model weights."),Kz.forEach(t),Njo=i(Tl),xie=n(Tl,"P",{});var wrt=s(xie);qjo=r(wrt,"Examples:"),wrt.forEach(t),Ojo=i(Tl),m(hy.$$.fragment,Tl),Tl.forEach(t),Gjo=i(vl),Je=n(vl,"DIV",{class:!0});var Zt=s(Je);m(py.$$.fragment,Zt),Xjo=i(Zt),kie=n(Zt,"P",{});var Art=s(kie);Vjo=r(Art,"Instantiate one of the model classes of the library (with a audio classification head) from a pretrained model."),Art.forEach(t),zjo=i(Zt),tn=n(Zt,"P",{});var cM=s(tn);Wjo=r(cM,"The model class to instantiate is selected based on the "),Rie=n(cM,"CODE",{});var Lrt=s(Rie);Qjo=r(Lrt,"model_type"),Lrt.forEach(t),Hjo=r(cM,` property of the config object (either
passed as an argument or loaded from `),Sie=n(cM,"CODE",{});var Brt=s(Sie);Ujo=r(Brt,"pretrained_model_name_or_path"),Brt.forEach(t),Jjo=r(cM,` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),Pie=n(cM,"CODE",{});var xrt=s(Pie);Yjo=r(xrt,"pretrained_model_name_or_path"),xrt.forEach(t),Kjo=r(cM,":"),cM.forEach(t),Zjo=i(Zt),xe=n(Zt,"UL",{});var qo=s(xe);t2=n(qo,"LI",{});var Uwe=s(t2);$ie=n(Uwe,"STRONG",{});var krt=s($ie);eDo=r(krt,"data2vec-audio"),krt.forEach(t),oDo=r(Uwe," \u2014 "),vN=n(Uwe,"A",{href:!0});var Rrt=s(vN);rDo=r(Rrt,"Data2VecAudioForSequenceClassification"),Rrt.forEach(t),tDo=r(Uwe," (Data2VecAudio model)"),Uwe.forEach(t),aDo=i(qo),a2=n(qo,"LI",{});var Jwe=s(a2);Iie=n(Jwe,"STRONG",{});var Srt=s(Iie);nDo=r(Srt,"hubert"),Srt.forEach(t),sDo=r(Jwe," \u2014 "),TN=n(Jwe,"A",{href:!0});var Prt=s(TN);lDo=r(Prt,"HubertForSequenceClassification"),Prt.forEach(t),iDo=r(Jwe," (Hubert model)"),Jwe.forEach(t),dDo=i(qo),n2=n(qo,"LI",{});var Ywe=s(n2);jie=n(Ywe,"STRONG",{});var $rt=s(jie);cDo=r($rt,"sew"),$rt.forEach(t),fDo=r(Ywe," \u2014 "),FN=n(Ywe,"A",{href:!0});var Irt=s(FN);mDo=r(Irt,"SEWForSequenceClassification"),Irt.forEach(t),gDo=r(Ywe," (SEW model)"),Ywe.forEach(t),hDo=i(qo),s2=n(qo,"LI",{});var Kwe=s(s2);Die=n(Kwe,"STRONG",{});var jrt=s(Die);pDo=r(jrt,"sew-d"),jrt.forEach(t),_Do=r(Kwe," \u2014 "),CN=n(Kwe,"A",{href:!0});var Drt=s(CN);uDo=r(Drt,"SEWDForSequenceClassification"),Drt.forEach(t),bDo=r(Kwe," (SEW-D model)"),Kwe.forEach(t),vDo=i(qo),l2=n(qo,"LI",{});var Zwe=s(l2);Nie=n(Zwe,"STRONG",{});var Nrt=s(Nie);TDo=r(Nrt,"unispeech"),Nrt.forEach(t),FDo=r(Zwe," \u2014 "),MN=n(Zwe,"A",{href:!0});var qrt=s(MN);CDo=r(qrt,"UniSpeechForSequenceClassification"),qrt.forEach(t),MDo=r(Zwe," (UniSpeech model)"),Zwe.forEach(t),EDo=i(qo),i2=n(qo,"LI",{});var e6e=s(i2);qie=n(e6e,"STRONG",{});var Ort=s(qie);yDo=r(Ort,"unispeech-sat"),Ort.forEach(t),wDo=r(e6e," \u2014 "),EN=n(e6e,"A",{href:!0});var Grt=s(EN);ADo=r(Grt,"UniSpeechSatForSequenceClassification"),Grt.forEach(t),LDo=r(e6e," (UniSpeechSat model)"),e6e.forEach(t),BDo=i(qo),d2=n(qo,"LI",{});var o6e=s(d2);Oie=n(o6e,"STRONG",{});var Xrt=s(Oie);xDo=r(Xrt,"wav2vec2"),Xrt.forEach(t),kDo=r(o6e," \u2014 "),yN=n(o6e,"A",{href:!0});var Vrt=s(yN);RDo=r(Vrt,"Wav2Vec2ForSequenceClassification"),Vrt.forEach(t),SDo=r(o6e," (Wav2Vec2 model)"),o6e.forEach(t),PDo=i(qo),c2=n(qo,"LI",{});var r6e=s(c2);Gie=n(r6e,"STRONG",{});var zrt=s(Gie);$Do=r(zrt,"wavlm"),zrt.forEach(t),IDo=r(r6e," \u2014 "),wN=n(r6e,"A",{href:!0});var Wrt=s(wN);jDo=r(Wrt,"WavLMForSequenceClassification"),Wrt.forEach(t),DDo=r(r6e," (WavLM model)"),r6e.forEach(t),qo.forEach(t),NDo=i(Zt),f2=n(Zt,"P",{});var t6e=s(f2);qDo=r(t6e,"The model is set in evaluation mode by default using "),Xie=n(t6e,"CODE",{});var Qrt=s(Xie);ODo=r(Qrt,"model.eval()"),Qrt.forEach(t),GDo=r(t6e,` (so for instance, dropout modules are
deactivated). To train the model, you should first set it back in training mode with `),Vie=n(t6e,"CODE",{});var Hrt=s(Vie);XDo=r(Hrt,"model.train()"),Hrt.forEach(t),t6e.forEach(t),VDo=i(Zt),zie=n(Zt,"P",{});var Urt=s(zie);zDo=r(Urt,"Examples:"),Urt.forEach(t),WDo=i(Zt),m(_y.$$.fragment,Zt),Zt.forEach(t),vl.forEach(t),Qxe=i(d),Nd=n(d,"H2",{class:!0});var rSe=s(Nd);m2=n(rSe,"A",{id:!0,class:!0,href:!0});var Jrt=s(m2);Wie=n(Jrt,"SPAN",{});var Yrt=s(Wie);m(uy.$$.fragment,Yrt),Yrt.forEach(t),Jrt.forEach(t),QDo=i(rSe),Qie=n(rSe,"SPAN",{});var Krt=s(Qie);HDo=r(Krt,"AutoModelForAudioFrameClassification"),Krt.forEach(t),rSe.forEach(t),Hxe=i(d),cr=n(d,"DIV",{class:!0});var Fl=s(cr);m(by.$$.fragment,Fl),UDo=i(Fl),qd=n(Fl,"P",{});var Zz=s(qd);JDo=r(Zz,`This is a generic model class that will be instantiated as one of the model classes of the library (with a audio frame (token) classification head) when created
with the `),Hie=n(Zz,"CODE",{});var Zrt=s(Hie);YDo=r(Zrt,"from_pretrained()"),Zrt.forEach(t),KDo=r(Zz,"class method or the "),Uie=n(Zz,"CODE",{});var ett=s(Uie);ZDo=r(ett,"from_config()"),ett.forEach(t),eNo=r(Zz,`class
method.`),Zz.forEach(t),oNo=i(Fl),vy=n(Fl,"P",{});var tSe=s(vy);rNo=r(tSe,"This class cannot be instantiated directly using "),Jie=n(tSe,"CODE",{});var ott=s(Jie);tNo=r(ott,"__init__()"),ott.forEach(t),aNo=r(tSe," (throws an error)."),tSe.forEach(t),nNo=i(Fl),at=n(Fl,"DIV",{class:!0});var Cl=s(at);m(Ty.$$.fragment,Cl),sNo=i(Cl),Yie=n(Cl,"P",{});var rtt=s(Yie);lNo=r(rtt,"Instantiates one of the model classes of the library (with a audio frame (token) classification head) from a configuration."),rtt.forEach(t),iNo=i(Cl),Od=n(Cl,"P",{});var eW=s(Od);dNo=r(eW,`Note:
Loading a model from its configuration file does `),Kie=n(eW,"STRONG",{});var ttt=s(Kie);cNo=r(ttt,"not"),ttt.forEach(t),fNo=r(eW,` load the model weights. It only affects the
model\u2019s configuration. Use `),Zie=n(eW,"CODE",{});var att=s(Zie);mNo=r(att,"from_pretrained()"),att.forEach(t),gNo=r(eW,"to load the model weights."),eW.forEach(t),hNo=i(Cl),ede=n(Cl,"P",{});var ntt=s(ede);pNo=r(ntt,"Examples:"),ntt.forEach(t),_No=i(Cl),m(Fy.$$.fragment,Cl),Cl.forEach(t),uNo=i(Fl),Ye=n(Fl,"DIV",{class:!0});var ea=s(Ye);m(Cy.$$.fragment,ea),bNo=i(ea),ode=n(ea,"P",{});var stt=s(ode);vNo=r(stt,"Instantiate one of the model classes of the library (with a audio frame (token) classification head) from a pretrained model."),stt.forEach(t),TNo=i(ea),an=n(ea,"P",{});var fM=s(an);FNo=r(fM,"The model class to instantiate is selected based on the "),rde=n(fM,"CODE",{});var ltt=s(rde);CNo=r(ltt,"model_type"),ltt.forEach(t),MNo=r(fM,` property of the config object (either
passed as an argument or loaded from `),tde=n(fM,"CODE",{});var itt=s(tde);ENo=r(itt,"pretrained_model_name_or_path"),itt.forEach(t),yNo=r(fM,` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),ade=n(fM,"CODE",{});var dtt=s(ade);wNo=r(dtt,"pretrained_model_name_or_path"),dtt.forEach(t),ANo=r(fM,":"),fM.forEach(t),LNo=i(ea),nn=n(ea,"UL",{});var mM=s(nn);g2=n(mM,"LI",{});var a6e=s(g2);nde=n(a6e,"STRONG",{});var ctt=s(nde);BNo=r(ctt,"data2vec-audio"),ctt.forEach(t),xNo=r(a6e," \u2014 "),AN=n(a6e,"A",{href:!0});var ftt=s(AN);kNo=r(ftt,"Data2VecAudioForAudioFrameClassification"),ftt.forEach(t),RNo=r(a6e," (Data2VecAudio model)"),a6e.forEach(t),SNo=i(mM),h2=n(mM,"LI",{});var n6e=s(h2);sde=n(n6e,"STRONG",{});var mtt=s(sde);PNo=r(mtt,"unispeech-sat"),mtt.forEach(t),$No=r(n6e," \u2014 "),LN=n(n6e,"A",{href:!0});var gtt=s(LN);INo=r(gtt,"UniSpeechSatForAudioFrameClassification"),gtt.forEach(t),jNo=r(n6e," (UniSpeechSat model)"),n6e.forEach(t),DNo=i(mM),p2=n(mM,"LI",{});var s6e=s(p2);lde=n(s6e,"STRONG",{});var htt=s(lde);NNo=r(htt,"wav2vec2"),htt.forEach(t),qNo=r(s6e," \u2014 "),BN=n(s6e,"A",{href:!0});var ptt=s(BN);ONo=r(ptt,"Wav2Vec2ForAudioFrameClassification"),ptt.forEach(t),GNo=r(s6e," (Wav2Vec2 model)"),s6e.forEach(t),XNo=i(mM),_2=n(mM,"LI",{});var l6e=s(_2);ide=n(l6e,"STRONG",{});var _tt=s(ide);VNo=r(_tt,"wavlm"),_tt.forEach(t),zNo=r(l6e," \u2014 "),xN=n(l6e,"A",{href:!0});var utt=s(xN);WNo=r(utt,"WavLMForAudioFrameClassification"),utt.forEach(t),QNo=r(l6e," (WavLM model)"),l6e.forEach(t),mM.forEach(t),HNo=i(ea),u2=n(ea,"P",{});var i6e=s(u2);UNo=r(i6e,"The model is set in evaluation mode by default using "),dde=n(i6e,"CODE",{});var btt=s(dde);JNo=r(btt,"model.eval()"),btt.forEach(t),YNo=r(i6e,` (so for instance, dropout modules are
deactivated). To train the model, you should first set it back in training mode with `),cde=n(i6e,"CODE",{});var vtt=s(cde);KNo=r(vtt,"model.train()"),vtt.forEach(t),i6e.forEach(t),ZNo=i(ea),fde=n(ea,"P",{});var Ttt=s(fde);eqo=r(Ttt,"Examples:"),Ttt.forEach(t),oqo=i(ea),m(My.$$.fragment,ea),ea.forEach(t),Fl.forEach(t),Uxe=i(d),Gd=n(d,"H2",{class:!0});var aSe=s(Gd);b2=n(aSe,"A",{id:!0,class:!0,href:!0});var Ftt=s(b2);mde=n(Ftt,"SPAN",{});var Ctt=s(mde);m(Ey.$$.fragment,Ctt),Ctt.forEach(t),Ftt.forEach(t),rqo=i(aSe),gde=n(aSe,"SPAN",{});var Mtt=s(gde);tqo=r(Mtt,"AutoModelForCTC"),Mtt.forEach(t),aSe.forEach(t),Jxe=i(d),fr=n(d,"DIV",{class:!0});var Ml=s(fr);m(yy.$$.fragment,Ml),aqo=i(Ml),Xd=n(Ml,"P",{});var oW=s(Xd);nqo=r(oW,`This is a generic model class that will be instantiated as one of the model classes of the library (with a connectionist temporal classification head) when created
with the `),hde=n(oW,"CODE",{});var Ett=s(hde);sqo=r(Ett,"from_pretrained()"),Ett.forEach(t),lqo=r(oW,"class method or the "),pde=n(oW,"CODE",{});var ytt=s(pde);iqo=r(ytt,"from_config()"),ytt.forEach(t),dqo=r(oW,`class
method.`),oW.forEach(t),cqo=i(Ml),wy=n(Ml,"P",{});var nSe=s(wy);fqo=r(nSe,"This class cannot be instantiated directly using "),_de=n(nSe,"CODE",{});var wtt=s(_de);mqo=r(wtt,"__init__()"),wtt.forEach(t),gqo=r(nSe," (throws an error)."),nSe.forEach(t),hqo=i(Ml),nt=n(Ml,"DIV",{class:!0});var El=s(nt);m(Ay.$$.fragment,El),pqo=i(El),ude=n(El,"P",{});var Att=s(ude);_qo=r(Att,"Instantiates one of the model classes of the library (with a connectionist temporal classification head) from a configuration."),Att.forEach(t),uqo=i(El),Vd=n(El,"P",{});var rW=s(Vd);bqo=r(rW,`Note:
Loading a model from its configuration file does `),bde=n(rW,"STRONG",{});var Ltt=s(bde);vqo=r(Ltt,"not"),Ltt.forEach(t),Tqo=r(rW,` load the model weights. It only affects the
model\u2019s configuration. Use `),vde=n(rW,"CODE",{});var Btt=s(vde);Fqo=r(Btt,"from_pretrained()"),Btt.forEach(t),Cqo=r(rW,"to load the model weights."),rW.forEach(t),Mqo=i(El),Tde=n(El,"P",{});var xtt=s(Tde);Eqo=r(xtt,"Examples:"),xtt.forEach(t),yqo=i(El),m(Ly.$$.fragment,El),El.forEach(t),wqo=i(Ml),Ke=n(Ml,"DIV",{class:!0});var oa=s(Ke);m(By.$$.fragment,oa),Aqo=i(oa),Fde=n(oa,"P",{});var ktt=s(Fde);Lqo=r(ktt,"Instantiate one of the model classes of the library (with a connectionist temporal classification head) from a pretrained model."),ktt.forEach(t),Bqo=i(oa),sn=n(oa,"P",{});var gM=s(sn);xqo=r(gM,"The model class to instantiate is selected based on the "),Cde=n(gM,"CODE",{});var Rtt=s(Cde);kqo=r(Rtt,"model_type"),Rtt.forEach(t),Rqo=r(gM,` property of the config object (either
passed as an argument or loaded from `),Mde=n(gM,"CODE",{});var Stt=s(Mde);Sqo=r(Stt,"pretrained_model_name_or_path"),Stt.forEach(t),Pqo=r(gM,` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),Ede=n(gM,"CODE",{});var Ptt=s(Ede);$qo=r(Ptt,"pretrained_model_name_or_path"),Ptt.forEach(t),Iqo=r(gM,":"),gM.forEach(t),jqo=i(oa),ke=n(oa,"UL",{});var Oo=s(ke);v2=n(Oo,"LI",{});var d6e=s(v2);yde=n(d6e,"STRONG",{});var $tt=s(yde);Dqo=r($tt,"data2vec-audio"),$tt.forEach(t),Nqo=r(d6e," \u2014 "),kN=n(d6e,"A",{href:!0});var Itt=s(kN);qqo=r(Itt,"Data2VecAudioForCTC"),Itt.forEach(t),Oqo=r(d6e," (Data2VecAudio model)"),d6e.forEach(t),Gqo=i(Oo),T2=n(Oo,"LI",{});var c6e=s(T2);wde=n(c6e,"STRONG",{});var jtt=s(wde);Xqo=r(jtt,"hubert"),jtt.forEach(t),Vqo=r(c6e," \u2014 "),RN=n(c6e,"A",{href:!0});var Dtt=s(RN);zqo=r(Dtt,"HubertForCTC"),Dtt.forEach(t),Wqo=r(c6e," (Hubert model)"),c6e.forEach(t),Qqo=i(Oo),F2=n(Oo,"LI",{});var f6e=s(F2);Ade=n(f6e,"STRONG",{});var Ntt=s(Ade);Hqo=r(Ntt,"sew"),Ntt.forEach(t),Uqo=r(f6e," \u2014 "),SN=n(f6e,"A",{href:!0});var qtt=s(SN);Jqo=r(qtt,"SEWForCTC"),qtt.forEach(t),Yqo=r(f6e," (SEW model)"),f6e.forEach(t),Kqo=i(Oo),C2=n(Oo,"LI",{});var m6e=s(C2);Lde=n(m6e,"STRONG",{});var Ott=s(Lde);Zqo=r(Ott,"sew-d"),Ott.forEach(t),eOo=r(m6e," \u2014 "),PN=n(m6e,"A",{href:!0});var Gtt=s(PN);oOo=r(Gtt,"SEWDForCTC"),Gtt.forEach(t),rOo=r(m6e," (SEW-D model)"),m6e.forEach(t),tOo=i(Oo),M2=n(Oo,"LI",{});var g6e=s(M2);Bde=n(g6e,"STRONG",{});var Xtt=s(Bde);aOo=r(Xtt,"unispeech"),Xtt.forEach(t),nOo=r(g6e," \u2014 "),$N=n(g6e,"A",{href:!0});var Vtt=s($N);sOo=r(Vtt,"UniSpeechForCTC"),Vtt.forEach(t),lOo=r(g6e," (UniSpeech model)"),g6e.forEach(t),iOo=i(Oo),E2=n(Oo,"LI",{});var h6e=s(E2);xde=n(h6e,"STRONG",{});var ztt=s(xde);dOo=r(ztt,"unispeech-sat"),ztt.forEach(t),cOo=r(h6e," \u2014 "),IN=n(h6e,"A",{href:!0});var Wtt=s(IN);fOo=r(Wtt,"UniSpeechSatForCTC"),Wtt.forEach(t),mOo=r(h6e," (UniSpeechSat model)"),h6e.forEach(t),gOo=i(Oo),y2=n(Oo,"LI",{});var p6e=s(y2);kde=n(p6e,"STRONG",{});var Qtt=s(kde);hOo=r(Qtt,"wav2vec2"),Qtt.forEach(t),pOo=r(p6e," \u2014 "),jN=n(p6e,"A",{href:!0});var Htt=s(jN);_Oo=r(Htt,"Wav2Vec2ForCTC"),Htt.forEach(t),uOo=r(p6e," (Wav2Vec2 model)"),p6e.forEach(t),bOo=i(Oo),w2=n(Oo,"LI",{});var _6e=s(w2);Rde=n(_6e,"STRONG",{});var Utt=s(Rde);vOo=r(Utt,"wavlm"),Utt.forEach(t),TOo=r(_6e," \u2014 "),DN=n(_6e,"A",{href:!0});var Jtt=s(DN);FOo=r(Jtt,"WavLMForCTC"),Jtt.forEach(t),COo=r(_6e," (WavLM model)"),_6e.forEach(t),Oo.forEach(t),MOo=i(oa),A2=n(oa,"P",{});var u6e=s(A2);EOo=r(u6e,"The model is set in evaluation mode by default using "),Sde=n(u6e,"CODE",{});var Ytt=s(Sde);yOo=r(Ytt,"model.eval()"),Ytt.forEach(t),wOo=r(u6e,` (so for instance, dropout modules are
deactivated). To train the model, you should first set it back in training mode with `),Pde=n(u6e,"CODE",{});var Ktt=s(Pde);AOo=r(Ktt,"model.train()"),Ktt.forEach(t),u6e.forEach(t),LOo=i(oa),$de=n(oa,"P",{});var Ztt=s($de);BOo=r(Ztt,"Examples:"),Ztt.forEach(t),xOo=i(oa),m(xy.$$.fragment,oa),oa.forEach(t),Ml.forEach(t),Yxe=i(d),zd=n(d,"H2",{class:!0});var sSe=s(zd);L2=n(sSe,"A",{id:!0,class:!0,href:!0});var eat=s(L2);Ide=n(eat,"SPAN",{});var oat=s(Ide);m(ky.$$.fragment,oat),oat.forEach(t),eat.forEach(t),kOo=i(sSe),jde=n(sSe,"SPAN",{});var rat=s(jde);ROo=r(rat,"AutoModelForSpeechSeq2Seq"),rat.forEach(t),sSe.forEach(t),Kxe=i(d),mr=n(d,"DIV",{class:!0});var yl=s(mr);m(Ry.$$.fragment,yl),SOo=i(yl),Wd=n(yl,"P",{});var tW=s(Wd);POo=r(tW,`This is a generic model class that will be instantiated as one of the model classes of the library (with a sequence-to-sequence speech-to-text modeling head) when created
with the `),Dde=n(tW,"CODE",{});var tat=s(Dde);$Oo=r(tat,"from_pretrained()"),tat.forEach(t),IOo=r(tW,"class method or the "),Nde=n(tW,"CODE",{});var aat=s(Nde);jOo=r(aat,"from_config()"),aat.forEach(t),DOo=r(tW,`class
method.`),tW.forEach(t),NOo=i(yl),Sy=n(yl,"P",{});var lSe=s(Sy);qOo=r(lSe,"This class cannot be instantiated directly using "),qde=n(lSe,"CODE",{});var nat=s(qde);OOo=r(nat,"__init__()"),nat.forEach(t),GOo=r(lSe," (throws an error)."),lSe.forEach(t),XOo=i(yl),st=n(yl,"DIV",{class:!0});var wl=s(st);m(Py.$$.fragment,wl),VOo=i(wl),Ode=n(wl,"P",{});var sat=s(Ode);zOo=r(sat,"Instantiates one of the model classes of the library (with a sequence-to-sequence speech-to-text modeling head) from a configuration."),sat.forEach(t),WOo=i(wl),Qd=n(wl,"P",{});var aW=s(Qd);QOo=r(aW,`Note:
Loading a model from its configuration file does `),Gde=n(aW,"STRONG",{});var lat=s(Gde);HOo=r(lat,"not"),lat.forEach(t),UOo=r(aW,` load the model weights. It only affects the
model\u2019s configuration. Use `),Xde=n(aW,"CODE",{});var iat=s(Xde);JOo=r(iat,"from_pretrained()"),iat.forEach(t),YOo=r(aW,"to load the model weights."),aW.forEach(t),KOo=i(wl),Vde=n(wl,"P",{});var dat=s(Vde);ZOo=r(dat,"Examples:"),dat.forEach(t),eGo=i(wl),m($y.$$.fragment,wl),wl.forEach(t),oGo=i(yl),Ze=n(yl,"DIV",{class:!0});var ra=s(Ze);m(Iy.$$.fragment,ra),rGo=i(ra),zde=n(ra,"P",{});var cat=s(zde);tGo=r(cat,"Instantiate one of the model classes of the library (with a sequence-to-sequence speech-to-text modeling head) from a pretrained model."),cat.forEach(t),aGo=i(ra),ln=n(ra,"P",{});var hM=s(ln);nGo=r(hM,"The model class to instantiate is selected based on the "),Wde=n(hM,"CODE",{});var fat=s(Wde);sGo=r(fat,"model_type"),fat.forEach(t),lGo=r(hM,` property of the config object (either
passed as an argument or loaded from `),Qde=n(hM,"CODE",{});var mat=s(Qde);iGo=r(mat,"pretrained_model_name_or_path"),mat.forEach(t),dGo=r(hM,` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),Hde=n(hM,"CODE",{});var gat=s(Hde);cGo=r(gat,"pretrained_model_name_or_path"),gat.forEach(t),fGo=r(hM,":"),hM.forEach(t),mGo=i(ra),jy=n(ra,"UL",{});var iSe=s(jy);B2=n(iSe,"LI",{});var b6e=s(B2);Ude=n(b6e,"STRONG",{});var hat=s(Ude);gGo=r(hat,"speech-encoder-decoder"),hat.forEach(t),hGo=r(b6e," \u2014 "),NN=n(b6e,"A",{href:!0});var pat=s(NN);pGo=r(pat,"SpeechEncoderDecoderModel"),pat.forEach(t),_Go=r(b6e," (Speech Encoder decoder model)"),b6e.forEach(t),uGo=i(iSe),x2=n(iSe,"LI",{});var v6e=s(x2);Jde=n(v6e,"STRONG",{});var _at=s(Jde);bGo=r(_at,"speech_to_text"),_at.forEach(t),vGo=r(v6e," \u2014 "),qN=n(v6e,"A",{href:!0});var uat=s(qN);TGo=r(uat,"Speech2TextForConditionalGeneration"),uat.forEach(t),FGo=r(v6e," (Speech2Text model)"),v6e.forEach(t),iSe.forEach(t),CGo=i(ra),k2=n(ra,"P",{});var T6e=s(k2);MGo=r(T6e,"The model is set in evaluation mode by default using "),Yde=n(T6e,"CODE",{});var bat=s(Yde);EGo=r(bat,"model.eval()"),bat.forEach(t),yGo=r(T6e,` (so for instance, dropout modules are
deactivated). To train the model, you should first set it back in training mode with `),Kde=n(T6e,"CODE",{});var vat=s(Kde);wGo=r(vat,"model.train()"),vat.forEach(t),T6e.forEach(t),AGo=i(ra),Zde=n(ra,"P",{});var Tat=s(Zde);LGo=r(Tat,"Examples:"),Tat.forEach(t),BGo=i(ra),m(Dy.$$.fragment,ra),ra.forEach(t),yl.forEach(t),Zxe=i(d),Hd=n(d,"H2",{class:!0});var dSe=s(Hd);R2=n(dSe,"A",{id:!0,class:!0,href:!0});var Fat=s(R2);ece=n(Fat,"SPAN",{});var Cat=s(ece);m(Ny.$$.fragment,Cat),Cat.forEach(t),Fat.forEach(t),xGo=i(dSe),oce=n(dSe,"SPAN",{});var Mat=s(oce);kGo=r(Mat,"AutoModelForAudioXVector"),Mat.forEach(t),dSe.forEach(t),eke=i(d),gr=n(d,"DIV",{class:!0});var Al=s(gr);m(qy.$$.fragment,Al),RGo=i(Al),Ud=n(Al,"P",{});var nW=s(Ud);SGo=r(nW,`This is a generic model class that will be instantiated as one of the model classes of the library (with a audio retrieval via x-vector head) when created
with the `),rce=n(nW,"CODE",{});var Eat=s(rce);PGo=r(Eat,"from_pretrained()"),Eat.forEach(t),$Go=r(nW,"class method or the "),tce=n(nW,"CODE",{});var yat=s(tce);IGo=r(yat,"from_config()"),yat.forEach(t),jGo=r(nW,`class
method.`),nW.forEach(t),DGo=i(Al),Oy=n(Al,"P",{});var cSe=s(Oy);NGo=r(cSe,"This class cannot be instantiated directly using "),ace=n(cSe,"CODE",{});var wat=s(ace);qGo=r(wat,"__init__()"),wat.forEach(t),OGo=r(cSe," (throws an error)."),cSe.forEach(t),GGo=i(Al),lt=n(Al,"DIV",{class:!0});var Ll=s(lt);m(Gy.$$.fragment,Ll),XGo=i(Ll),nce=n(Ll,"P",{});var Aat=s(nce);VGo=r(Aat,"Instantiates one of the model classes of the library (with a audio retrieval via x-vector head) from a configuration."),Aat.forEach(t),zGo=i(Ll),Jd=n(Ll,"P",{});var sW=s(Jd);WGo=r(sW,`Note:
Loading a model from its configuration file does `),sce=n(sW,"STRONG",{});var Lat=s(sce);QGo=r(Lat,"not"),Lat.forEach(t),HGo=r(sW,` load the model weights. It only affects the
model\u2019s configuration. Use `),lce=n(sW,"CODE",{});var Bat=s(lce);UGo=r(Bat,"from_pretrained()"),Bat.forEach(t),JGo=r(sW,"to load the model weights."),sW.forEach(t),YGo=i(Ll),ice=n(Ll,"P",{});var xat=s(ice);KGo=r(xat,"Examples:"),xat.forEach(t),ZGo=i(Ll),m(Xy.$$.fragment,Ll),Ll.forEach(t),eXo=i(Al),eo=n(Al,"DIV",{class:!0});var ta=s(eo);m(Vy.$$.fragment,ta),oXo=i(ta),dce=n(ta,"P",{});var kat=s(dce);rXo=r(kat,"Instantiate one of the model classes of the library (with a audio retrieval via x-vector head) from a pretrained model."),kat.forEach(t),tXo=i(ta),dn=n(ta,"P",{});var pM=s(dn);aXo=r(pM,"The model class to instantiate is selected based on the "),cce=n(pM,"CODE",{});var Rat=s(cce);nXo=r(Rat,"model_type"),Rat.forEach(t),sXo=r(pM,` property of the config object (either
passed as an argument or loaded from `),fce=n(pM,"CODE",{});var Sat=s(fce);lXo=r(Sat,"pretrained_model_name_or_path"),Sat.forEach(t),iXo=r(pM,` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),mce=n(pM,"CODE",{});var Pat=s(mce);dXo=r(Pat,"pretrained_model_name_or_path"),Pat.forEach(t),cXo=r(pM,":"),pM.forEach(t),fXo=i(ta),cn=n(ta,"UL",{});var _M=s(cn);S2=n(_M,"LI",{});var F6e=s(S2);gce=n(F6e,"STRONG",{});var $at=s(gce);mXo=r($at,"data2vec-audio"),$at.forEach(t),gXo=r(F6e," \u2014 "),ON=n(F6e,"A",{href:!0});var Iat=s(ON);hXo=r(Iat,"Data2VecAudioForXVector"),Iat.forEach(t),pXo=r(F6e," (Data2VecAudio model)"),F6e.forEach(t),_Xo=i(_M),P2=n(_M,"LI",{});var C6e=s(P2);hce=n(C6e,"STRONG",{});var jat=s(hce);uXo=r(jat,"unispeech-sat"),jat.forEach(t),bXo=r(C6e," \u2014 "),GN=n(C6e,"A",{href:!0});var Dat=s(GN);vXo=r(Dat,"UniSpeechSatForXVector"),Dat.forEach(t),TXo=r(C6e," (UniSpeechSat model)"),C6e.forEach(t),FXo=i(_M),$2=n(_M,"LI",{});var M6e=s($2);pce=n(M6e,"STRONG",{});var Nat=s(pce);CXo=r(Nat,"wav2vec2"),Nat.forEach(t),MXo=r(M6e," \u2014 "),XN=n(M6e,"A",{href:!0});var qat=s(XN);EXo=r(qat,"Wav2Vec2ForXVector"),qat.forEach(t),yXo=r(M6e," (Wav2Vec2 model)"),M6e.forEach(t),wXo=i(_M),I2=n(_M,"LI",{});var E6e=s(I2);_ce=n(E6e,"STRONG",{});var Oat=s(_ce);AXo=r(Oat,"wavlm"),Oat.forEach(t),LXo=r(E6e," \u2014 "),VN=n(E6e,"A",{href:!0});var Gat=s(VN);BXo=r(Gat,"WavLMForXVector"),Gat.forEach(t),xXo=r(E6e," (WavLM model)"),E6e.forEach(t),_M.forEach(t),kXo=i(ta),j2=n(ta,"P",{});var y6e=s(j2);RXo=r(y6e,"The model is set in evaluation mode by default using "),uce=n(y6e,"CODE",{});var Xat=s(uce);SXo=r(Xat,"model.eval()"),Xat.forEach(t),PXo=r(y6e,` (so for instance, dropout modules are
deactivated). To train the model, you should first set it back in training mode with `),bce=n(y6e,"CODE",{});var Vat=s(bce);$Xo=r(Vat,"model.train()"),Vat.forEach(t),y6e.forEach(t),IXo=i(ta),vce=n(ta,"P",{});var zat=s(vce);jXo=r(zat,"Examples:"),zat.forEach(t),DXo=i(ta),m(zy.$$.fragment,ta),ta.forEach(t),Al.forEach(t),oke=i(d),Yd=n(d,"H2",{class:!0});var fSe=s(Yd);D2=n(fSe,"A",{id:!0,class:!0,href:!0});var Wat=s(D2);Tce=n(Wat,"SPAN",{});var Qat=s(Tce);m(Wy.$$.fragment,Qat),Qat.forEach(t),Wat.forEach(t),NXo=i(fSe),Fce=n(fSe,"SPAN",{});var Hat=s(Fce);qXo=r(Hat,"AutoModelForMaskedImageModeling"),Hat.forEach(t),fSe.forEach(t),rke=i(d),hr=n(d,"DIV",{class:!0});var Bl=s(hr);m(Qy.$$.fragment,Bl),OXo=i(Bl),Kd=n(Bl,"P",{});var lW=s(Kd);GXo=r(lW,`This is a generic model class that will be instantiated as one of the model classes of the library (with a masked image modeling head) when created
with the `),Cce=n(lW,"CODE",{});var Uat=s(Cce);XXo=r(Uat,"from_pretrained()"),Uat.forEach(t),VXo=r(lW,"class method or the "),Mce=n(lW,"CODE",{});var Jat=s(Mce);zXo=r(Jat,"from_config()"),Jat.forEach(t),WXo=r(lW,`class
method.`),lW.forEach(t),QXo=i(Bl),Hy=n(Bl,"P",{});var mSe=s(Hy);HXo=r(mSe,"This class cannot be instantiated directly using "),Ece=n(mSe,"CODE",{});var Yat=s(Ece);UXo=r(Yat,"__init__()"),Yat.forEach(t),JXo=r(mSe," (throws an error)."),mSe.forEach(t),YXo=i(Bl),it=n(Bl,"DIV",{class:!0});var xl=s(it);m(Uy.$$.fragment,xl),KXo=i(xl),yce=n(xl,"P",{});var Kat=s(yce);ZXo=r(Kat,"Instantiates one of the model classes of the library (with a masked image modeling head) from a configuration."),Kat.forEach(t),eVo=i(xl),Zd=n(xl,"P",{});var iW=s(Zd);oVo=r(iW,`Note:
Loading a model from its configuration file does `),wce=n(iW,"STRONG",{});var Zat=s(wce);rVo=r(Zat,"not"),Zat.forEach(t),tVo=r(iW,` load the model weights. It only affects the
model\u2019s configuration. Use `),Ace=n(iW,"CODE",{});var ent=s(Ace);aVo=r(ent,"from_pretrained()"),ent.forEach(t),nVo=r(iW,"to load the model weights."),iW.forEach(t),sVo=i(xl),Lce=n(xl,"P",{});var ont=s(Lce);lVo=r(ont,"Examples:"),ont.forEach(t),iVo=i(xl),m(Jy.$$.fragment,xl),xl.forEach(t),dVo=i(Bl),oo=n(Bl,"DIV",{class:!0});var aa=s(oo);m(Yy.$$.fragment,aa),cVo=i(aa),Bce=n(aa,"P",{});var rnt=s(Bce);fVo=r(rnt,"Instantiate one of the model classes of the library (with a masked image modeling head) from a pretrained model."),rnt.forEach(t),mVo=i(aa),fn=n(aa,"P",{});var uM=s(fn);gVo=r(uM,"The model class to instantiate is selected based on the "),xce=n(uM,"CODE",{});var tnt=s(xce);hVo=r(tnt,"model_type"),tnt.forEach(t),pVo=r(uM,` property of the config object (either
passed as an argument or loaded from `),kce=n(uM,"CODE",{});var ant=s(kce);_Vo=r(ant,"pretrained_model_name_or_path"),ant.forEach(t),uVo=r(uM,` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),Rce=n(uM,"CODE",{});var nnt=s(Rce);bVo=r(nnt,"pretrained_model_name_or_path"),nnt.forEach(t),vVo=r(uM,":"),uM.forEach(t),TVo=i(aa),ec=n(aa,"UL",{});var dW=s(ec);N2=n(dW,"LI",{});var w6e=s(N2);Sce=n(w6e,"STRONG",{});var snt=s(Sce);FVo=r(snt,"deit"),snt.forEach(t),CVo=r(w6e," \u2014 "),zN=n(w6e,"A",{href:!0});var lnt=s(zN);MVo=r(lnt,"DeiTForMaskedImageModeling"),lnt.forEach(t),EVo=r(w6e," (DeiT model)"),w6e.forEach(t),yVo=i(dW),q2=n(dW,"LI",{});var A6e=s(q2);Pce=n(A6e,"STRONG",{});var int=s(Pce);wVo=r(int,"swin"),int.forEach(t),AVo=r(A6e," \u2014 "),WN=n(A6e,"A",{href:!0});var dnt=s(WN);LVo=r(dnt,"SwinForMaskedImageModeling"),dnt.forEach(t),BVo=r(A6e," (Swin model)"),A6e.forEach(t),xVo=i(dW),O2=n(dW,"LI",{});var L6e=s(O2);$ce=n(L6e,"STRONG",{});var cnt=s($ce);kVo=r(cnt,"vit"),cnt.forEach(t),RVo=r(L6e," \u2014 "),QN=n(L6e,"A",{href:!0});var fnt=s(QN);SVo=r(fnt,"ViTForMaskedImageModeling"),fnt.forEach(t),PVo=r(L6e," (ViT model)"),L6e.forEach(t),dW.forEach(t),$Vo=i(aa),G2=n(aa,"P",{});var B6e=s(G2);IVo=r(B6e,"The model is set in evaluation mode by default using "),Ice=n(B6e,"CODE",{});var mnt=s(Ice);jVo=r(mnt,"model.eval()"),mnt.forEach(t),DVo=r(B6e,` (so for instance, dropout modules are
deactivated). To train the model, you should first set it back in training mode with `),jce=n(B6e,"CODE",{});var gnt=s(jce);NVo=r(gnt,"model.train()"),gnt.forEach(t),B6e.forEach(t),qVo=i(aa),Dce=n(aa,"P",{});var hnt=s(Dce);OVo=r(hnt,"Examples:"),hnt.forEach(t),GVo=i(aa),m(Ky.$$.fragment,aa),aa.forEach(t),Bl.forEach(t),tke=i(d),oc=n(d,"H2",{class:!0});var gSe=s(oc);X2=n(gSe,"A",{id:!0,class:!0,href:!0});var pnt=s(X2);Nce=n(pnt,"SPAN",{});var _nt=s(Nce);m(Zy.$$.fragment,_nt),_nt.forEach(t),pnt.forEach(t),XVo=i(gSe),qce=n(gSe,"SPAN",{});var unt=s(qce);VVo=r(unt,"AutoModelForObjectDetection"),unt.forEach(t),gSe.forEach(t),ake=i(d),pr=n(d,"DIV",{class:!0});var kl=s(pr);m(ew.$$.fragment,kl),zVo=i(kl),rc=n(kl,"P",{});var cW=s(rc);WVo=r(cW,`This is a generic model class that will be instantiated as one of the model classes of the library (with a object detection head) when created
with the `),Oce=n(cW,"CODE",{});var bnt=s(Oce);QVo=r(bnt,"from_pretrained()"),bnt.forEach(t),HVo=r(cW,"class method or the "),Gce=n(cW,"CODE",{});var vnt=s(Gce);UVo=r(vnt,"from_config()"),vnt.forEach(t),JVo=r(cW,`class
method.`),cW.forEach(t),YVo=i(kl),ow=n(kl,"P",{});var hSe=s(ow);KVo=r(hSe,"This class cannot be instantiated directly using "),Xce=n(hSe,"CODE",{});var Tnt=s(Xce);ZVo=r(Tnt,"__init__()"),Tnt.forEach(t),ezo=r(hSe," (throws an error)."),hSe.forEach(t),ozo=i(kl),dt=n(kl,"DIV",{class:!0});var Rl=s(dt);m(rw.$$.fragment,Rl),rzo=i(Rl),Vce=n(Rl,"P",{});var Fnt=s(Vce);tzo=r(Fnt,"Instantiates one of the model classes of the library (with a object detection head) from a configuration."),Fnt.forEach(t),azo=i(Rl),tc=n(Rl,"P",{});var fW=s(tc);nzo=r(fW,`Note:
Loading a model from its configuration file does `),zce=n(fW,"STRONG",{});var Cnt=s(zce);szo=r(Cnt,"not"),Cnt.forEach(t),lzo=r(fW,` load the model weights. It only affects the
model\u2019s configuration. Use `),Wce=n(fW,"CODE",{});var Mnt=s(Wce);izo=r(Mnt,"from_pretrained()"),Mnt.forEach(t),dzo=r(fW,"to load the model weights."),fW.forEach(t),czo=i(Rl),Qce=n(Rl,"P",{});var Ent=s(Qce);fzo=r(Ent,"Examples:"),Ent.forEach(t),mzo=i(Rl),m(tw.$$.fragment,Rl),Rl.forEach(t),gzo=i(kl),ro=n(kl,"DIV",{class:!0});var na=s(ro);m(aw.$$.fragment,na),hzo=i(na),Hce=n(na,"P",{});var ynt=s(Hce);pzo=r(ynt,"Instantiate one of the model classes of the library (with a object detection head) from a pretrained model."),ynt.forEach(t),_zo=i(na),mn=n(na,"P",{});var bM=s(mn);uzo=r(bM,"The model class to instantiate is selected based on the "),Uce=n(bM,"CODE",{});var wnt=s(Uce);bzo=r(wnt,"model_type"),wnt.forEach(t),vzo=r(bM,` property of the config object (either
passed as an argument or loaded from `),Jce=n(bM,"CODE",{});var Ant=s(Jce);Tzo=r(Ant,"pretrained_model_name_or_path"),Ant.forEach(t),Fzo=r(bM,` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),Yce=n(bM,"CODE",{});var Lnt=s(Yce);Czo=r(Lnt,"pretrained_model_name_or_path"),Lnt.forEach(t),Mzo=r(bM,":"),bM.forEach(t),Ezo=i(na),Kce=n(na,"UL",{});var Bnt=s(Kce);V2=n(Bnt,"LI",{});var x6e=s(V2);Zce=n(x6e,"STRONG",{});var xnt=s(Zce);yzo=r(xnt,"detr"),xnt.forEach(t),wzo=r(x6e," \u2014 "),HN=n(x6e,"A",{href:!0});var knt=s(HN);Azo=r(knt,"DetrForObjectDetection"),knt.forEach(t),Lzo=r(x6e," (DETR model)"),x6e.forEach(t),Bnt.forEach(t),Bzo=i(na),z2=n(na,"P",{});var k6e=s(z2);xzo=r(k6e,"The model is set in evaluation mode by default using "),efe=n(k6e,"CODE",{});var Rnt=s(efe);kzo=r(Rnt,"model.eval()"),Rnt.forEach(t),Rzo=r(k6e,` (so for instance, dropout modules are
deactivated). To train the model, you should first set it back in training mode with `),ofe=n(k6e,"CODE",{});var Snt=s(ofe);Szo=r(Snt,"model.train()"),Snt.forEach(t),k6e.forEach(t),Pzo=i(na),rfe=n(na,"P",{});var Pnt=s(rfe);$zo=r(Pnt,"Examples:"),Pnt.forEach(t),Izo=i(na),m(nw.$$.fragment,na),na.forEach(t),kl.forEach(t),nke=i(d),ac=n(d,"H2",{class:!0});var pSe=s(ac);W2=n(pSe,"A",{id:!0,class:!0,href:!0});var $nt=s(W2);tfe=n($nt,"SPAN",{});var Int=s(tfe);m(sw.$$.fragment,Int),Int.forEach(t),$nt.forEach(t),jzo=i(pSe),afe=n(pSe,"SPAN",{});var jnt=s(afe);Dzo=r(jnt,"AutoModelForImageSegmentation"),jnt.forEach(t),pSe.forEach(t),ske=i(d),_r=n(d,"DIV",{class:!0});var Sl=s(_r);m(lw.$$.fragment,Sl),Nzo=i(Sl),nc=n(Sl,"P",{});var mW=s(nc);qzo=r(mW,`This is a generic model class that will be instantiated as one of the model classes of the library (with a image segmentation head) when created
with the `),nfe=n(mW,"CODE",{});var Dnt=s(nfe);Ozo=r(Dnt,"from_pretrained()"),Dnt.forEach(t),Gzo=r(mW,"class method or the "),sfe=n(mW,"CODE",{});var Nnt=s(sfe);Xzo=r(Nnt,"from_config()"),Nnt.forEach(t),Vzo=r(mW,`class
method.`),mW.forEach(t),zzo=i(Sl),iw=n(Sl,"P",{});var _Se=s(iw);Wzo=r(_Se,"This class cannot be instantiated directly using "),lfe=n(_Se,"CODE",{});var qnt=s(lfe);Qzo=r(qnt,"__init__()"),qnt.forEach(t),Hzo=r(_Se," (throws an error)."),_Se.forEach(t),Uzo=i(Sl),ct=n(Sl,"DIV",{class:!0});var Pl=s(ct);m(dw.$$.fragment,Pl),Jzo=i(Pl),ife=n(Pl,"P",{});var Ont=s(ife);Yzo=r(Ont,"Instantiates one of the model classes of the library (with a image segmentation head) from a configuration."),Ont.forEach(t),Kzo=i(Pl),sc=n(Pl,"P",{});var gW=s(sc);Zzo=r(gW,`Note:
Loading a model from its configuration file does `),dfe=n(gW,"STRONG",{});var Gnt=s(dfe);eWo=r(Gnt,"not"),Gnt.forEach(t),oWo=r(gW,` load the model weights. It only affects the
model\u2019s configuration. Use `),cfe=n(gW,"CODE",{});var Xnt=s(cfe);rWo=r(Xnt,"from_pretrained()"),Xnt.forEach(t),tWo=r(gW,"to load the model weights."),gW.forEach(t),aWo=i(Pl),ffe=n(Pl,"P",{});var Vnt=s(ffe);nWo=r(Vnt,"Examples:"),Vnt.forEach(t),sWo=i(Pl),m(cw.$$.fragment,Pl),Pl.forEach(t),lWo=i(Sl),to=n(Sl,"DIV",{class:!0});var sa=s(to);m(fw.$$.fragment,sa),iWo=i(sa),mfe=n(sa,"P",{});var znt=s(mfe);dWo=r(znt,"Instantiate one of the model classes of the library (with a image segmentation head) from a pretrained model."),znt.forEach(t),cWo=i(sa),gn=n(sa,"P",{});var vM=s(gn);fWo=r(vM,"The model class to instantiate is selected based on the "),gfe=n(vM,"CODE",{});var Wnt=s(gfe);mWo=r(Wnt,"model_type"),Wnt.forEach(t),gWo=r(vM,` property of the config object (either
passed as an argument or loaded from `),hfe=n(vM,"CODE",{});var Qnt=s(hfe);hWo=r(Qnt,"pretrained_model_name_or_path"),Qnt.forEach(t),pWo=r(vM,` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),pfe=n(vM,"CODE",{});var Hnt=s(pfe);_Wo=r(Hnt,"pretrained_model_name_or_path"),Hnt.forEach(t),uWo=r(vM,":"),vM.forEach(t),bWo=i(sa),_fe=n(sa,"UL",{});var Unt=s(_fe);Q2=n(Unt,"LI",{});var R6e=s(Q2);ufe=n(R6e,"STRONG",{});var Jnt=s(ufe);vWo=r(Jnt,"detr"),Jnt.forEach(t),TWo=r(R6e," \u2014 "),UN=n(R6e,"A",{href:!0});var Ynt=s(UN);FWo=r(Ynt,"DetrForSegmentation"),Ynt.forEach(t),CWo=r(R6e," (DETR model)"),R6e.forEach(t),Unt.forEach(t),MWo=i(sa),H2=n(sa,"P",{});var S6e=s(H2);EWo=r(S6e,"The model is set in evaluation mode by default using "),bfe=n(S6e,"CODE",{});var Knt=s(bfe);yWo=r(Knt,"model.eval()"),Knt.forEach(t),wWo=r(S6e,` (so for instance, dropout modules are
deactivated). To train the model, you should first set it back in training mode with `),vfe=n(S6e,"CODE",{});var Znt=s(vfe);AWo=r(Znt,"model.train()"),Znt.forEach(t),S6e.forEach(t),LWo=i(sa),Tfe=n(sa,"P",{});var est=s(Tfe);BWo=r(est,"Examples:"),est.forEach(t),xWo=i(sa),m(mw.$$.fragment,sa),sa.forEach(t),Sl.forEach(t),lke=i(d),lc=n(d,"H2",{class:!0});var uSe=s(lc);U2=n(uSe,"A",{id:!0,class:!0,href:!0});var ost=s(U2);Ffe=n(ost,"SPAN",{});var rst=s(Ffe);m(gw.$$.fragment,rst),rst.forEach(t),ost.forEach(t),kWo=i(uSe),Cfe=n(uSe,"SPAN",{});var tst=s(Cfe);RWo=r(tst,"AutoModelForSemanticSegmentation"),tst.forEach(t),uSe.forEach(t),ike=i(d),ur=n(d,"DIV",{class:!0});var $l=s(ur);m(hw.$$.fragment,$l),SWo=i($l),ic=n($l,"P",{});var hW=s(ic);PWo=r(hW,`This is a generic model class that will be instantiated as one of the model classes of the library (with a semantic segmentation head) when created
with the `),Mfe=n(hW,"CODE",{});var ast=s(Mfe);$Wo=r(ast,"from_pretrained()"),ast.forEach(t),IWo=r(hW,"class method or the "),Efe=n(hW,"CODE",{});var nst=s(Efe);jWo=r(nst,"from_config()"),nst.forEach(t),DWo=r(hW,`class
method.`),hW.forEach(t),NWo=i($l),pw=n($l,"P",{});var bSe=s(pw);qWo=r(bSe,"This class cannot be instantiated directly using "),yfe=n(bSe,"CODE",{});var sst=s(yfe);OWo=r(sst,"__init__()"),sst.forEach(t),GWo=r(bSe," (throws an error)."),bSe.forEach(t),XWo=i($l),ft=n($l,"DIV",{class:!0});var Il=s(ft);m(_w.$$.fragment,Il),VWo=i(Il),wfe=n(Il,"P",{});var lst=s(wfe);zWo=r(lst,"Instantiates one of the model classes of the library (with a semantic segmentation head) from a configuration."),lst.forEach(t),WWo=i(Il),dc=n(Il,"P",{});var pW=s(dc);QWo=r(pW,`Note:
Loading a model from its configuration file does `),Afe=n(pW,"STRONG",{});var ist=s(Afe);HWo=r(ist,"not"),ist.forEach(t),UWo=r(pW,` load the model weights. It only affects the
model\u2019s configuration. Use `),Lfe=n(pW,"CODE",{});var dst=s(Lfe);JWo=r(dst,"from_pretrained()"),dst.forEach(t),YWo=r(pW,"to load the model weights."),pW.forEach(t),KWo=i(Il),Bfe=n(Il,"P",{});var cst=s(Bfe);ZWo=r(cst,"Examples:"),cst.forEach(t),eQo=i(Il),m(uw.$$.fragment,Il),Il.forEach(t),oQo=i($l),ao=n($l,"DIV",{class:!0});var la=s(ao);m(bw.$$.fragment,la),rQo=i(la),xfe=n(la,"P",{});var fst=s(xfe);tQo=r(fst,"Instantiate one of the model classes of the library (with a semantic segmentation head) from a pretrained model."),fst.forEach(t),aQo=i(la),hn=n(la,"P",{});var TM=s(hn);nQo=r(TM,"The model class to instantiate is selected based on the "),kfe=n(TM,"CODE",{});var mst=s(kfe);sQo=r(mst,"model_type"),mst.forEach(t),lQo=r(TM,` property of the config object (either
passed as an argument or loaded from `),Rfe=n(TM,"CODE",{});var gst=s(Rfe);iQo=r(gst,"pretrained_model_name_or_path"),gst.forEach(t),dQo=r(TM,` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),Sfe=n(TM,"CODE",{});var hst=s(Sfe);cQo=r(hst,"pretrained_model_name_or_path"),hst.forEach(t),fQo=r(TM,":"),TM.forEach(t),mQo=i(la),vw=n(la,"UL",{});var vSe=s(vw);J2=n(vSe,"LI",{});var P6e=s(J2);Pfe=n(P6e,"STRONG",{});var pst=s(Pfe);gQo=r(pst,"beit"),pst.forEach(t),hQo=r(P6e," \u2014 "),JN=n(P6e,"A",{href:!0});var _st=s(JN);pQo=r(_st,"BeitForSemanticSegmentation"),_st.forEach(t),_Qo=r(P6e," (BEiT model)"),P6e.forEach(t),uQo=i(vSe),Y2=n(vSe,"LI",{});var $6e=s(Y2);$fe=n($6e,"STRONG",{});var ust=s($fe);bQo=r(ust,"segformer"),ust.forEach(t),vQo=r($6e," \u2014 "),YN=n($6e,"A",{href:!0});var bst=s(YN);TQo=r(bst,"SegformerForSemanticSegmentation"),bst.forEach(t),FQo=r($6e," (SegFormer model)"),$6e.forEach(t),vSe.forEach(t),CQo=i(la),K2=n(la,"P",{});var I6e=s(K2);MQo=r(I6e,"The model is set in evaluation mode by default using "),Ife=n(I6e,"CODE",{});var vst=s(Ife);EQo=r(vst,"model.eval()"),vst.forEach(t),yQo=r(I6e,` (so for instance, dropout modules are
deactivated). To train the model, you should first set it back in training mode with `),jfe=n(I6e,"CODE",{});var Tst=s(jfe);wQo=r(Tst,"model.train()"),Tst.forEach(t),I6e.forEach(t),AQo=i(la),Dfe=n(la,"P",{});var Fst=s(Dfe);LQo=r(Fst,"Examples:"),Fst.forEach(t),BQo=i(la),m(Tw.$$.fragment,la),la.forEach(t),$l.forEach(t),dke=i(d),cc=n(d,"H2",{class:!0});var TSe=s(cc);Z2=n(TSe,"A",{id:!0,class:!0,href:!0});var Cst=s(Z2);Nfe=n(Cst,"SPAN",{});var Mst=s(Nfe);m(Fw.$$.fragment,Mst),Mst.forEach(t),Cst.forEach(t),xQo=i(TSe),qfe=n(TSe,"SPAN",{});var Est=s(qfe);kQo=r(Est,"AutoModelForInstanceSegmentation"),Est.forEach(t),TSe.forEach(t),cke=i(d),br=n(d,"DIV",{class:!0});var jl=s(br);m(Cw.$$.fragment,jl),RQo=i(jl),fc=n(jl,"P",{});var _W=s(fc);SQo=r(_W,`This is a generic model class that will be instantiated as one of the model classes of the library (with a instance segmentation head) when created
with the `),Ofe=n(_W,"CODE",{});var yst=s(Ofe);PQo=r(yst,"from_pretrained()"),yst.forEach(t),$Qo=r(_W,"class method or the "),Gfe=n(_W,"CODE",{});var wst=s(Gfe);IQo=r(wst,"from_config()"),wst.forEach(t),jQo=r(_W,`class
method.`),_W.forEach(t),DQo=i(jl),Mw=n(jl,"P",{});var FSe=s(Mw);NQo=r(FSe,"This class cannot be instantiated directly using "),Xfe=n(FSe,"CODE",{});var Ast=s(Xfe);qQo=r(Ast,"__init__()"),Ast.forEach(t),OQo=r(FSe," (throws an error)."),FSe.forEach(t),GQo=i(jl),mt=n(jl,"DIV",{class:!0});var Dl=s(mt);m(Ew.$$.fragment,Dl),XQo=i(Dl),Vfe=n(Dl,"P",{});var Lst=s(Vfe);VQo=r(Lst,"Instantiates one of the model classes of the library (with a instance segmentation head) from a configuration."),Lst.forEach(t),zQo=i(Dl),mc=n(Dl,"P",{});var uW=s(mc);WQo=r(uW,`Note:
Loading a model from its configuration file does `),zfe=n(uW,"STRONG",{});var Bst=s(zfe);QQo=r(Bst,"not"),Bst.forEach(t),HQo=r(uW,` load the model weights. It only affects the
model\u2019s configuration. Use `),Wfe=n(uW,"CODE",{});var xst=s(Wfe);UQo=r(xst,"from_pretrained()"),xst.forEach(t),JQo=r(uW,"to load the model weights."),uW.forEach(t),YQo=i(Dl),Qfe=n(Dl,"P",{});var kst=s(Qfe);KQo=r(kst,"Examples:"),kst.forEach(t),ZQo=i(Dl),m(yw.$$.fragment,Dl),Dl.forEach(t),eHo=i(jl),no=n(jl,"DIV",{class:!0});var ia=s(no);m(ww.$$.fragment,ia),oHo=i(ia),Hfe=n(ia,"P",{});var Rst=s(Hfe);rHo=r(Rst,"Instantiate one of the model classes of the library (with a instance segmentation head) from a pretrained model."),Rst.forEach(t),tHo=i(ia),pn=n(ia,"P",{});var FM=s(pn);aHo=r(FM,"The model class to instantiate is selected based on the "),Ufe=n(FM,"CODE",{});var Sst=s(Ufe);nHo=r(Sst,"model_type"),Sst.forEach(t),sHo=r(FM,` property of the config object (either
passed as an argument or loaded from `),Jfe=n(FM,"CODE",{});var Pst=s(Jfe);lHo=r(Pst,"pretrained_model_name_or_path"),Pst.forEach(t),iHo=r(FM,` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),Yfe=n(FM,"CODE",{});var $st=s(Yfe);dHo=r($st,"pretrained_model_name_or_path"),$st.forEach(t),cHo=r(FM,":"),FM.forEach(t),fHo=i(ia),Kfe=n(ia,"UL",{});var Ist=s(Kfe);ev=n(Ist,"LI",{});var j6e=s(ev);Zfe=n(j6e,"STRONG",{});var jst=s(Zfe);mHo=r(jst,"maskformer"),jst.forEach(t),gHo=r(j6e," \u2014 "),KN=n(j6e,"A",{href:!0});var Dst=s(KN);hHo=r(Dst,"MaskFormerForInstanceSegmentation"),Dst.forEach(t),pHo=r(j6e," (MaskFormer model)"),j6e.forEach(t),Ist.forEach(t),_Ho=i(ia),ov=n(ia,"P",{});var D6e=s(ov);uHo=r(D6e,"The model is set in evaluation mode by default using "),eme=n(D6e,"CODE",{});var Nst=s(eme);bHo=r(Nst,"model.eval()"),Nst.forEach(t),vHo=r(D6e,` (so for instance, dropout modules are
deactivated). To train the model, you should first set it back in training mode with `),ome=n(D6e,"CODE",{});var qst=s(ome);THo=r(qst,"model.train()"),qst.forEach(t),D6e.forEach(t),FHo=i(ia),rme=n(ia,"P",{});var Ost=s(rme);CHo=r(Ost,"Examples:"),Ost.forEach(t),MHo=i(ia),m(Aw.$$.fragment,ia),ia.forEach(t),jl.forEach(t),fke=i(d),gc=n(d,"H2",{class:!0});var CSe=s(gc);rv=n(CSe,"A",{id:!0,class:!0,href:!0});var Gst=s(rv);tme=n(Gst,"SPAN",{});var Xst=s(tme);m(Lw.$$.fragment,Xst),Xst.forEach(t),Gst.forEach(t),EHo=i(CSe),ame=n(CSe,"SPAN",{});var Vst=s(ame);yHo=r(Vst,"TFAutoModel"),Vst.forEach(t),CSe.forEach(t),mke=i(d),vr=n(d,"DIV",{class:!0});var Nl=s(vr);m(Bw.$$.fragment,Nl),wHo=i(Nl),hc=n(Nl,"P",{});var bW=s(hc);AHo=r(bW,`This is a generic model class that will be instantiated as one of the base model classes of the library when created
with the `),nme=n(bW,"CODE",{});var zst=s(nme);LHo=r(zst,"from_pretrained()"),zst.forEach(t),BHo=r(bW,"class method or the "),sme=n(bW,"CODE",{});var Wst=s(sme);xHo=r(Wst,"from_config()"),Wst.forEach(t),kHo=r(bW,`class
method.`),bW.forEach(t),RHo=i(Nl),xw=n(Nl,"P",{});var MSe=s(xw);SHo=r(MSe,"This class cannot be instantiated directly using "),lme=n(MSe,"CODE",{});var Qst=s(lme);PHo=r(Qst,"__init__()"),Qst.forEach(t),$Ho=r(MSe," (throws an error)."),MSe.forEach(t),IHo=i(Nl),gt=n(Nl,"DIV",{class:!0});var ql=s(gt);m(kw.$$.fragment,ql),jHo=i(ql),ime=n(ql,"P",{});var Hst=s(ime);DHo=r(Hst,"Instantiates one of the base model classes of the library from a configuration."),Hst.forEach(t),NHo=i(ql),pc=n(ql,"P",{});var vW=s(pc);qHo=r(vW,`Note:
Loading a model from its configuration file does `),dme=n(vW,"STRONG",{});var Ust=s(dme);OHo=r(Ust,"not"),Ust.forEach(t),GHo=r(vW,` load the model weights. It only affects the
model\u2019s configuration. Use `),cme=n(vW,"CODE",{});var Jst=s(cme);XHo=r(Jst,"from_pretrained()"),Jst.forEach(t),VHo=r(vW,"to load the model weights."),vW.forEach(t),zHo=i(ql),fme=n(ql,"P",{});var Yst=s(fme);WHo=r(Yst,"Examples:"),Yst.forEach(t),QHo=i(ql),m(Rw.$$.fragment,ql),ql.forEach(t),HHo=i(Nl),ho=n(Nl,"DIV",{class:!0});var pa=s(ho);m(Sw.$$.fragment,pa),UHo=i(pa),mme=n(pa,"P",{});var Kst=s(mme);JHo=r(Kst,"Instantiate one of the base model classes of the library from a pretrained model."),Kst.forEach(t),YHo=i(pa),_n=n(pa,"P",{});var CM=s(_n);KHo=r(CM,"The model class to instantiate is selected based on the "),gme=n(CM,"CODE",{});var Zst=s(gme);ZHo=r(Zst,"model_type"),Zst.forEach(t),eUo=r(CM,` property of the config object (either
passed as an argument or loaded from `),hme=n(CM,"CODE",{});var elt=s(hme);oUo=r(elt,"pretrained_model_name_or_path"),elt.forEach(t),rUo=r(CM,` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),pme=n(CM,"CODE",{});var olt=s(pme);tUo=r(olt,"pretrained_model_name_or_path"),olt.forEach(t),aUo=r(CM,":"),CM.forEach(t),nUo=i(pa),B=n(pa,"UL",{});var x=s(B);tv=n(x,"LI",{});var N6e=s(tv);_me=n(N6e,"STRONG",{});var rlt=s(_me);sUo=r(rlt,"albert"),rlt.forEach(t),lUo=r(N6e," \u2014 "),ZN=n(N6e,"A",{href:!0});var tlt=s(ZN);iUo=r(tlt,"TFAlbertModel"),tlt.forEach(t),dUo=r(N6e," (ALBERT model)"),N6e.forEach(t),cUo=i(x),av=n(x,"LI",{});var q6e=s(av);ume=n(q6e,"STRONG",{});var alt=s(ume);fUo=r(alt,"bart"),alt.forEach(t),mUo=r(q6e," \u2014 "),eq=n(q6e,"A",{href:!0});var nlt=s(eq);gUo=r(nlt,"TFBartModel"),nlt.forEach(t),hUo=r(q6e," (BART model)"),q6e.forEach(t),pUo=i(x),nv=n(x,"LI",{});var O6e=s(nv);bme=n(O6e,"STRONG",{});var slt=s(bme);_Uo=r(slt,"bert"),slt.forEach(t),uUo=r(O6e," \u2014 "),oq=n(O6e,"A",{href:!0});var llt=s(oq);bUo=r(llt,"TFBertModel"),llt.forEach(t),vUo=r(O6e," (BERT model)"),O6e.forEach(t),TUo=i(x),sv=n(x,"LI",{});var G6e=s(sv);vme=n(G6e,"STRONG",{});var ilt=s(vme);FUo=r(ilt,"blenderbot"),ilt.forEach(t),CUo=r(G6e," \u2014 "),rq=n(G6e,"A",{href:!0});var dlt=s(rq);MUo=r(dlt,"TFBlenderbotModel"),dlt.forEach(t),EUo=r(G6e," (Blenderbot model)"),G6e.forEach(t),yUo=i(x),lv=n(x,"LI",{});var X6e=s(lv);Tme=n(X6e,"STRONG",{});var clt=s(Tme);wUo=r(clt,"blenderbot-small"),clt.forEach(t),AUo=r(X6e," \u2014 "),tq=n(X6e,"A",{href:!0});var flt=s(tq);LUo=r(flt,"TFBlenderbotSmallModel"),flt.forEach(t),BUo=r(X6e," (BlenderbotSmall model)"),X6e.forEach(t),xUo=i(x),iv=n(x,"LI",{});var V6e=s(iv);Fme=n(V6e,"STRONG",{});var mlt=s(Fme);kUo=r(mlt,"camembert"),mlt.forEach(t),RUo=r(V6e," \u2014 "),aq=n(V6e,"A",{href:!0});var glt=s(aq);SUo=r(glt,"TFCamembertModel"),glt.forEach(t),PUo=r(V6e," (CamemBERT model)"),V6e.forEach(t),$Uo=i(x),dv=n(x,"LI",{});var z6e=s(dv);Cme=n(z6e,"STRONG",{});var hlt=s(Cme);IUo=r(hlt,"clip"),hlt.forEach(t),jUo=r(z6e," \u2014 "),nq=n(z6e,"A",{href:!0});var plt=s(nq);DUo=r(plt,"TFCLIPModel"),plt.forEach(t),NUo=r(z6e," (CLIP model)"),z6e.forEach(t),qUo=i(x),cv=n(x,"LI",{});var W6e=s(cv);Mme=n(W6e,"STRONG",{});var _lt=s(Mme);OUo=r(_lt,"convbert"),_lt.forEach(t),GUo=r(W6e," \u2014 "),sq=n(W6e,"A",{href:!0});var ult=s(sq);XUo=r(ult,"TFConvBertModel"),ult.forEach(t),VUo=r(W6e," (ConvBERT model)"),W6e.forEach(t),zUo=i(x),fv=n(x,"LI",{});var Q6e=s(fv);Eme=n(Q6e,"STRONG",{});var blt=s(Eme);WUo=r(blt,"convnext"),blt.forEach(t),QUo=r(Q6e," \u2014 "),lq=n(Q6e,"A",{href:!0});var vlt=s(lq);HUo=r(vlt,"TFConvNextModel"),vlt.forEach(t),UUo=r(Q6e," (ConvNext model)"),Q6e.forEach(t),JUo=i(x),mv=n(x,"LI",{});var H6e=s(mv);yme=n(H6e,"STRONG",{});var Tlt=s(yme);YUo=r(Tlt,"ctrl"),Tlt.forEach(t),KUo=r(H6e," \u2014 "),iq=n(H6e,"A",{href:!0});var Flt=s(iq);ZUo=r(Flt,"TFCTRLModel"),Flt.forEach(t),eJo=r(H6e," (CTRL model)"),H6e.forEach(t),oJo=i(x),gv=n(x,"LI",{});var U6e=s(gv);wme=n(U6e,"STRONG",{});var Clt=s(wme);rJo=r(Clt,"deberta"),Clt.forEach(t),tJo=r(U6e," \u2014 "),dq=n(U6e,"A",{href:!0});var Mlt=s(dq);aJo=r(Mlt,"TFDebertaModel"),Mlt.forEach(t),nJo=r(U6e," (DeBERTa model)"),U6e.forEach(t),sJo=i(x),hv=n(x,"LI",{});var J6e=s(hv);Ame=n(J6e,"STRONG",{});var Elt=s(Ame);lJo=r(Elt,"deberta-v2"),Elt.forEach(t),iJo=r(J6e," \u2014 "),cq=n(J6e,"A",{href:!0});var ylt=s(cq);dJo=r(ylt,"TFDebertaV2Model"),ylt.forEach(t),cJo=r(J6e," (DeBERTa-v2 model)"),J6e.forEach(t),fJo=i(x),pv=n(x,"LI",{});var Y6e=s(pv);Lme=n(Y6e,"STRONG",{});var wlt=s(Lme);mJo=r(wlt,"distilbert"),wlt.forEach(t),gJo=r(Y6e," \u2014 "),fq=n(Y6e,"A",{href:!0});var Alt=s(fq);hJo=r(Alt,"TFDistilBertModel"),Alt.forEach(t),pJo=r(Y6e," (DistilBERT model)"),Y6e.forEach(t),_Jo=i(x),_v=n(x,"LI",{});var K6e=s(_v);Bme=n(K6e,"STRONG",{});var Llt=s(Bme);uJo=r(Llt,"dpr"),Llt.forEach(t),bJo=r(K6e," \u2014 "),mq=n(K6e,"A",{href:!0});var Blt=s(mq);vJo=r(Blt,"TFDPRQuestionEncoder"),Blt.forEach(t),TJo=r(K6e," (DPR model)"),K6e.forEach(t),FJo=i(x),uv=n(x,"LI",{});var Z6e=s(uv);xme=n(Z6e,"STRONG",{});var xlt=s(xme);CJo=r(xlt,"electra"),xlt.forEach(t),MJo=r(Z6e," \u2014 "),gq=n(Z6e,"A",{href:!0});var klt=s(gq);EJo=r(klt,"TFElectraModel"),klt.forEach(t),yJo=r(Z6e," (ELECTRA model)"),Z6e.forEach(t),wJo=i(x),bv=n(x,"LI",{});var eAe=s(bv);kme=n(eAe,"STRONG",{});var Rlt=s(kme);AJo=r(Rlt,"flaubert"),Rlt.forEach(t),LJo=r(eAe," \u2014 "),hq=n(eAe,"A",{href:!0});var Slt=s(hq);BJo=r(Slt,"TFFlaubertModel"),Slt.forEach(t),xJo=r(eAe," (FlauBERT model)"),eAe.forEach(t),kJo=i(x),Os=n(x,"LI",{});var NL=s(Os);Rme=n(NL,"STRONG",{});var Plt=s(Rme);RJo=r(Plt,"funnel"),Plt.forEach(t),SJo=r(NL," \u2014 "),pq=n(NL,"A",{href:!0});var $lt=s(pq);PJo=r($lt,"TFFunnelModel"),$lt.forEach(t),$Jo=r(NL," or "),_q=n(NL,"A",{href:!0});var Ilt=s(_q);IJo=r(Ilt,"TFFunnelBaseModel"),Ilt.forEach(t),jJo=r(NL," (Funnel Transformer model)"),NL.forEach(t),DJo=i(x),vv=n(x,"LI",{});var oAe=s(vv);Sme=n(oAe,"STRONG",{});var jlt=s(Sme);NJo=r(jlt,"gpt2"),jlt.forEach(t),qJo=r(oAe," \u2014 "),uq=n(oAe,"A",{href:!0});var Dlt=s(uq);OJo=r(Dlt,"TFGPT2Model"),Dlt.forEach(t),GJo=r(oAe," (OpenAI GPT-2 model)"),oAe.forEach(t),XJo=i(x),Tv=n(x,"LI",{});var rAe=s(Tv);Pme=n(rAe,"STRONG",{});var Nlt=s(Pme);VJo=r(Nlt,"hubert"),Nlt.forEach(t),zJo=r(rAe," \u2014 "),bq=n(rAe,"A",{href:!0});var qlt=s(bq);WJo=r(qlt,"TFHubertModel"),qlt.forEach(t),QJo=r(rAe," (Hubert model)"),rAe.forEach(t),HJo=i(x),Fv=n(x,"LI",{});var tAe=s(Fv);$me=n(tAe,"STRONG",{});var Olt=s($me);UJo=r(Olt,"layoutlm"),Olt.forEach(t),JJo=r(tAe," \u2014 "),vq=n(tAe,"A",{href:!0});var Glt=s(vq);YJo=r(Glt,"TFLayoutLMModel"),Glt.forEach(t),KJo=r(tAe," (LayoutLM model)"),tAe.forEach(t),ZJo=i(x),Cv=n(x,"LI",{});var aAe=s(Cv);Ime=n(aAe,"STRONG",{});var Xlt=s(Ime);eYo=r(Xlt,"led"),Xlt.forEach(t),oYo=r(aAe," \u2014 "),Tq=n(aAe,"A",{href:!0});var Vlt=s(Tq);rYo=r(Vlt,"TFLEDModel"),Vlt.forEach(t),tYo=r(aAe," (LED model)"),aAe.forEach(t),aYo=i(x),Mv=n(x,"LI",{});var nAe=s(Mv);jme=n(nAe,"STRONG",{});var zlt=s(jme);nYo=r(zlt,"longformer"),zlt.forEach(t),sYo=r(nAe," 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Zlt=s(Ome);yYo=r(Zlt,"mobilebert"),Zlt.forEach(t),wYo=r(dAe," \u2014 "),yq=n(dAe,"A",{href:!0});var eit=s(yq);AYo=r(eit,"TFMobileBertModel"),eit.forEach(t),LYo=r(dAe," (MobileBERT model)"),dAe.forEach(t),BYo=i(x),Lv=n(x,"LI",{});var cAe=s(Lv);Gme=n(cAe,"STRONG",{});var oit=s(Gme);xYo=r(oit,"mpnet"),oit.forEach(t),kYo=r(cAe," \u2014 "),wq=n(cAe,"A",{href:!0});var rit=s(wq);RYo=r(rit,"TFMPNetModel"),rit.forEach(t),SYo=r(cAe," (MPNet model)"),cAe.forEach(t),PYo=i(x),Bv=n(x,"LI",{});var fAe=s(Bv);Xme=n(fAe,"STRONG",{});var tit=s(Xme);$Yo=r(tit,"mt5"),tit.forEach(t),IYo=r(fAe," \u2014 "),Aq=n(fAe,"A",{href:!0});var ait=s(Aq);jYo=r(ait,"TFMT5Model"),ait.forEach(t),DYo=r(fAe," (mT5 model)"),fAe.forEach(t),NYo=i(x),xv=n(x,"LI",{});var mAe=s(xv);Vme=n(mAe,"STRONG",{});var nit=s(Vme);qYo=r(nit,"openai-gpt"),nit.forEach(t),OYo=r(mAe," \u2014 "),Lq=n(mAe,"A",{href:!0});var sit=s(Lq);GYo=r(sit,"TFOpenAIGPTModel"),sit.forEach(t),XYo=r(mAe," (OpenAI GPT 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hit=s(Rq);iKo=r(hit,"TFRoFormerModel"),hit.forEach(t),dKo=r(_Ae," (RoFormer model)"),_Ae.forEach(t),cKo=i(x),$v=n(x,"LI",{});var uAe=s($v);Ume=n(uAe,"STRONG",{});var pit=s(Ume);fKo=r(pit,"speech_to_text"),pit.forEach(t),mKo=r(uAe," \u2014 "),Sq=n(uAe,"A",{href:!0});var _it=s(Sq);gKo=r(_it,"TFSpeech2TextModel"),_it.forEach(t),hKo=r(uAe," (Speech2Text model)"),uAe.forEach(t),pKo=i(x),Iv=n(x,"LI",{});var bAe=s(Iv);Jme=n(bAe,"STRONG",{});var uit=s(Jme);_Ko=r(uit,"t5"),uit.forEach(t),uKo=r(bAe," \u2014 "),Pq=n(bAe,"A",{href:!0});var bit=s(Pq);bKo=r(bit,"TFT5Model"),bit.forEach(t),vKo=r(bAe," (T5 model)"),bAe.forEach(t),TKo=i(x),jv=n(x,"LI",{});var vAe=s(jv);Yme=n(vAe,"STRONG",{});var vit=s(Yme);FKo=r(vit,"tapas"),vit.forEach(t),CKo=r(vAe," \u2014 "),$q=n(vAe,"A",{href:!0});var Tit=s($q);MKo=r(Tit,"TFTapasModel"),Tit.forEach(t),EKo=r(vAe," (TAPAS model)"),vAe.forEach(t),yKo=i(x),Dv=n(x,"LI",{});var TAe=s(Dv);Kme=n(TAe,"STRONG",{});var Fit=s(Kme);wKo=r(Fit,"transfo-xl"),Fit.forEach(t),AKo=r(TAe," \u2014 "),Iq=n(TAe,"A",{href:!0});var Cit=s(Iq);LKo=r(Cit,"TFTransfoXLModel"),Cit.forEach(t),BKo=r(TAe," (Transformer-XL model)"),TAe.forEach(t),xKo=i(x),Nv=n(x,"LI",{});var FAe=s(Nv);Zme=n(FAe,"STRONG",{});var Mit=s(Zme);kKo=r(Mit,"vit"),Mit.forEach(t),RKo=r(FAe," \u2014 "),jq=n(FAe,"A",{href:!0});var Eit=s(jq);SKo=r(Eit,"TFViTModel"),Eit.forEach(t),PKo=r(FAe," (ViT model)"),FAe.forEach(t),$Ko=i(x),qv=n(x,"LI",{});var CAe=s(qv);ege=n(CAe,"STRONG",{});var yit=s(ege);IKo=r(yit,"wav2vec2"),yit.forEach(t),jKo=r(CAe," \u2014 "),Dq=n(CAe,"A",{href:!0});var wit=s(Dq);DKo=r(wit,"TFWav2Vec2Model"),wit.forEach(t),NKo=r(CAe," (Wav2Vec2 model)"),CAe.forEach(t),qKo=i(x),Ov=n(x,"LI",{});var MAe=s(Ov);oge=n(MAe,"STRONG",{});var Ait=s(oge);OKo=r(Ait,"xlm"),Ait.forEach(t),GKo=r(MAe," \u2014 "),Nq=n(MAe,"A",{href:!0});var Lit=s(Nq);XKo=r(Lit,"TFXLMModel"),Lit.forEach(t),VKo=r(MAe," (XLM model)"),MAe.forEach(t),zKo=i(x),Gv=n(x,"LI",{});var EAe=s(Gv);rge=n(EAe,"STRONG",{});var Bit=s(rge);WKo=r(Bit,"xlm-roberta"),Bit.forEach(t),QKo=r(EAe," \u2014 "),qq=n(EAe,"A",{href:!0});var xit=s(qq);HKo=r(xit,"TFXLMRobertaModel"),xit.forEach(t),UKo=r(EAe," (XLM-RoBERTa model)"),EAe.forEach(t),JKo=i(x),Xv=n(x,"LI",{});var yAe=s(Xv);tge=n(yAe,"STRONG",{});var kit=s(tge);YKo=r(kit,"xlnet"),kit.forEach(t),KKo=r(yAe," \u2014 "),Oq=n(yAe,"A",{href:!0});var Rit=s(Oq);ZKo=r(Rit,"TFXLNetModel"),Rit.forEach(t),eZo=r(yAe," (XLNet model)"),yAe.forEach(t),x.forEach(t),oZo=i(pa),age=n(pa,"P",{});var Sit=s(age);rZo=r(Sit,"Examples:"),Sit.forEach(t),tZo=i(pa),m(Pw.$$.fragment,pa),pa.forEach(t),Nl.forEach(t),gke=i(d),_c=n(d,"H2",{class:!0});var ESe=s(_c);Vv=n(ESe,"A",{id:!0,class:!0,href:!0});var Pit=s(Vv);nge=n(Pit,"SPAN",{});var $it=s(nge);m($w.$$.fragment,$it),$it.forEach(t),Pit.forEach(t),aZo=i(ESe),sge=n(ESe,"SPAN",{});var Iit=s(sge);nZo=r(Iit,"TFAutoModelForPreTraining"),Iit.forEach(t),ESe.forEach(t),hke=i(d),Tr=n(d,"DIV",{class:!0});var Ol=s(Tr);m(Iw.$$.fragment,Ol),sZo=i(Ol),uc=n(Ol,"P",{});var TW=s(uc);lZo=r(TW,`This is a generic model class that will be instantiated as one of the model classes of the library (with a pretraining head) when created
with the `),lge=n(TW,"CODE",{});var jit=s(lge);iZo=r(jit,"from_pretrained()"),jit.forEach(t),dZo=r(TW,"class method or the "),ige=n(TW,"CODE",{});var Dit=s(ige);cZo=r(Dit,"from_config()"),Dit.forEach(t),fZo=r(TW,`class
method.`),TW.forEach(t),mZo=i(Ol),jw=n(Ol,"P",{});var ySe=s(jw);gZo=r(ySe,"This class cannot be instantiated directly using "),dge=n(ySe,"CODE",{});var Nit=s(dge);hZo=r(Nit,"__init__()"),Nit.forEach(t),pZo=r(ySe," (throws an error)."),ySe.forEach(t),_Zo=i(Ol),ht=n(Ol,"DIV",{class:!0});var Gl=s(ht);m(Dw.$$.fragment,Gl),uZo=i(Gl),cge=n(Gl,"P",{});var qit=s(cge);bZo=r(qit,"Instantiates one of the model classes of the library (with a pretraining head) from a configuration."),qit.forEach(t),vZo=i(Gl),bc=n(Gl,"P",{});var FW=s(bc);TZo=r(FW,`Note:
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model\u2019s configuration. Use `),mge=n(FW,"CODE",{});var Git=s(mge);MZo=r(Git,"from_pretrained()"),Git.forEach(t),EZo=r(FW,"to load the model weights."),FW.forEach(t),yZo=i(Gl),gge=n(Gl,"P",{});var Xit=s(gge);wZo=r(Xit,"Examples:"),Xit.forEach(t),AZo=i(Gl),m(Nw.$$.fragment,Gl),Gl.forEach(t),LZo=i(Ol),po=n(Ol,"DIV",{class:!0});var _a=s(po);m(qw.$$.fragment,_a),BZo=i(_a),hge=n(_a,"P",{});var Vit=s(hge);xZo=r(Vit,"Instantiate one of the model classes of the library (with a pretraining head) from a pretrained model."),Vit.forEach(t),kZo=i(_a),un=n(_a,"P",{});var MM=s(un);RZo=r(MM,"The model class to instantiate is selected based on the "),pge=n(MM,"CODE",{});var zit=s(pge);SZo=r(zit,"model_type"),zit.forEach(t),PZo=r(MM,` property of the config object (either
passed as an argument or loaded from `),_ge=n(MM,"CODE",{});var Wit=s(_ge);$Zo=r(Wit,"pretrained_model_name_or_path"),Wit.forEach(t),IZo=r(MM,` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),uge=n(MM,"CODE",{});var Qit=s(uge);jZo=r(Qit,"pretrained_model_name_or_path"),Qit.forEach(t),DZo=r(MM,":"),MM.forEach(t),NZo=i(_a),H=n(_a,"UL",{});var U=s(H);zv=n(U,"LI",{});var wAe=s(zv);bge=n(wAe,"STRONG",{});var Hit=s(bge);qZo=r(Hit,"albert"),Hit.forEach(t),OZo=r(wAe," \u2014 "),Gq=n(wAe,"A",{href:!0});var Uit=s(Gq);GZo=r(Uit,"TFAlbertForPreTraining"),Uit.forEach(t),XZo=r(wAe," (ALBERT model)"),wAe.forEach(t),VZo=i(U),Wv=n(U,"LI",{});var AAe=s(Wv);vge=n(AAe,"STRONG",{});var Jit=s(vge);zZo=r(Jit,"bart"),Jit.forEach(t),WZo=r(AAe," \u2014 "),Xq=n(AAe,"A",{href:!0});var Yit=s(Xq);QZo=r(Yit,"TFBartForConditionalGeneration"),Yit.forEach(t),HZo=r(AAe," (BART model)"),AAe.forEach(t),UZo=i(U),Qv=n(U,"LI",{});var LAe=s(Qv);Tge=n(LAe,"STRONG",{});var Kit=s(Tge);JZo=r(Kit,"bert"),Kit.forEach(t),YZo=r(LAe," \u2014 "),Vq=n(LAe,"A",{href:!0});var Zit=s(Vq);KZo=r(Zit,"TFBertForPreTraining"),Zit.forEach(t),ZZo=r(LAe," (BERT model)"),LAe.forEach(t),eer=i(U),Hv=n(U,"LI",{});var BAe=s(Hv);Fge=n(BAe,"STRONG",{});var edt=s(Fge);oer=r(edt,"camembert"),edt.forEach(t),rer=r(BAe," \u2014 "),zq=n(BAe,"A",{href:!0});var odt=s(zq);ter=r(odt,"TFCamembertForMaskedLM"),odt.forEach(t),aer=r(BAe," (CamemBERT model)"),BAe.forEach(t),ner=i(U),Uv=n(U,"LI",{});var xAe=s(Uv);Cge=n(xAe,"STRONG",{});var rdt=s(Cge);ser=r(rdt,"ctrl"),rdt.forEach(t),ler=r(xAe," \u2014 "),Wq=n(xAe,"A",{href:!0});var tdt=s(Wq);ier=r(tdt,"TFCTRLLMHeadModel"),tdt.forEach(t),der=r(xAe," (CTRL model)"),xAe.forEach(t),cer=i(U),Jv=n(U,"LI",{});var kAe=s(Jv);Mge=n(kAe,"STRONG",{});var adt=s(Mge);fer=r(adt,"distilbert"),adt.forEach(t),mer=r(kAe," \u2014 "),Qq=n(kAe,"A",{href:!0});var ndt=s(Qq);ger=r(ndt,"TFDistilBertForMaskedLM"),ndt.forEach(t),her=r(kAe," (DistilBERT model)"),kAe.forEach(t),per=i(U),Yv=n(U,"LI",{});var RAe=s(Yv);Ege=n(RAe,"STRONG",{});var sdt=s(Ege);_er=r(sdt,"electra"),sdt.forEach(t),uer=r(RAe," \u2014 "),Hq=n(RAe,"A",{href:!0});var ldt=s(Hq);ber=r(ldt,"TFElectraForPreTraining"),ldt.forEach(t),ver=r(RAe," (ELECTRA model)"),RAe.forEach(t),Ter=i(U),Kv=n(U,"LI",{});var SAe=s(Kv);yge=n(SAe,"STRONG",{});var idt=s(yge);Fer=r(idt,"flaubert"),idt.forEach(t),Cer=r(SAe," \u2014 "),Uq=n(SAe,"A",{href:!0});var ddt=s(Uq);Mer=r(ddt,"TFFlaubertWithLMHeadModel"),ddt.forEach(t),Eer=r(SAe," (FlauBERT model)"),SAe.forEach(t),yer=i(U),Zv=n(U,"LI",{});var PAe=s(Zv);wge=n(PAe,"STRONG",{});var cdt=s(wge);wer=r(cdt,"funnel"),cdt.forEach(t),Aer=r(PAe," \u2014 "),Jq=n(PAe,"A",{href:!0});var fdt=s(Jq);Ler=r(fdt,"TFFunnelForPreTraining"),fdt.forEach(t),Ber=r(PAe," (Funnel Transformer model)"),PAe.forEach(t),xer=i(U),eT=n(U,"LI",{});var $Ae=s(eT);Age=n($Ae,"STRONG",{});var mdt=s(Age);ker=r(mdt,"gpt2"),mdt.forEach(t),Rer=r($Ae," \u2014 "),Yq=n($Ae,"A",{href:!0});var gdt=s(Yq);Ser=r(gdt,"TFGPT2LMHeadModel"),gdt.forEach(t),Per=r($Ae," (OpenAI GPT-2 model)"),$Ae.forEach(t),$er=i(U),oT=n(U,"LI",{});var IAe=s(oT);Lge=n(IAe,"STRONG",{});var hdt=s(Lge);Ier=r(hdt,"layoutlm"),hdt.forEach(t),jer=r(IAe," \u2014 "),Kq=n(IAe,"A",{href:!0});var pdt=s(Kq);Der=r(pdt,"TFLayoutLMForMaskedLM"),pdt.forEach(t),Ner=r(IAe," (LayoutLM model)"),IAe.forEach(t),qer=i(U),rT=n(U,"LI",{});var jAe=s(rT);Bge=n(jAe,"STRONG",{});var _dt=s(Bge);Oer=r(_dt,"lxmert"),_dt.forEach(t),Ger=r(jAe," \u2014 "),Zq=n(jAe,"A",{href:!0});var udt=s(Zq);Xer=r(udt,"TFLxmertForPreTraining"),udt.forEach(t),Ver=r(jAe," (LXMERT model)"),jAe.forEach(t),zer=i(U),tT=n(U,"LI",{});var DAe=s(tT);xge=n(DAe,"STRONG",{});var bdt=s(xge);Wer=r(bdt,"mobilebert"),bdt.forEach(t),Qer=r(DAe," \u2014 "),eO=n(DAe,"A",{href:!0});var vdt=s(eO);Her=r(vdt,"TFMobileBertForPreTraining"),vdt.forEach(t),Uer=r(DAe," (MobileBERT model)"),DAe.forEach(t),Jer=i(U),aT=n(U,"LI",{});var NAe=s(aT);kge=n(NAe,"STRONG",{});var Tdt=s(kge);Yer=r(Tdt,"mpnet"),Tdt.forEach(t),Ker=r(NAe," \u2014 "),oO=n(NAe,"A",{href:!0});var Fdt=s(oO);Zer=r(Fdt,"TFMPNetForMaskedLM"),Fdt.forEach(t),eor=r(NAe," (MPNet model)"),NAe.forEach(t),oor=i(U),nT=n(U,"LI",{});var qAe=s(nT);Rge=n(qAe,"STRONG",{});var Cdt=s(Rge);ror=r(Cdt,"openai-gpt"),Cdt.forEach(t),tor=r(qAe," \u2014 "),rO=n(qAe,"A",{href:!0});var Mdt=s(rO);aor=r(Mdt,"TFOpenAIGPTLMHeadModel"),Mdt.forEach(t),nor=r(qAe," (OpenAI GPT model)"),qAe.forEach(t),sor=i(U),sT=n(U,"LI",{});var OAe=s(sT);Sge=n(OAe,"STRONG",{});var Edt=s(Sge);lor=r(Edt,"roberta"),Edt.forEach(t),ior=r(OAe," \u2014 "),tO=n(OAe,"A",{href:!0});var ydt=s(tO);dor=r(ydt,"TFRobertaForMaskedLM"),ydt.forEach(t),cor=r(OAe," (RoBERTa model)"),OAe.forEach(t),mor=i(U),lT=n(U,"LI",{});var GAe=s(lT);Pge=n(GAe,"STRONG",{});var wdt=s(Pge);gor=r(wdt,"t5"),wdt.forEach(t),hor=r(GAe," \u2014 "),aO=n(GAe,"A",{href:!0});var Adt=s(aO);por=r(Adt,"TFT5ForConditionalGeneration"),Adt.forEach(t),_or=r(GAe," (T5 model)"),GAe.forEach(t),uor=i(U),iT=n(U,"LI",{});var XAe=s(iT);$ge=n(XAe,"STRONG",{});var Ldt=s($ge);bor=r(Ldt,"tapas"),Ldt.forEach(t),vor=r(XAe," \u2014 "),nO=n(XAe,"A",{href:!0});var Bdt=s(nO);Tor=r(Bdt,"TFTapasForMaskedLM"),Bdt.forEach(t),For=r(XAe," (TAPAS model)"),XAe.forEach(t),Cor=i(U),dT=n(U,"LI",{});var VAe=s(dT);Ige=n(VAe,"STRONG",{});var xdt=s(Ige);Mor=r(xdt,"transfo-xl"),xdt.forEach(t),Eor=r(VAe," \u2014 "),sO=n(VAe,"A",{href:!0});var kdt=s(sO);yor=r(kdt,"TFTransfoXLLMHeadModel"),kdt.forEach(t),wor=r(VAe," (Transformer-XL model)"),VAe.forEach(t),Aor=i(U),cT=n(U,"LI",{});var zAe=s(cT);jge=n(zAe,"STRONG",{});var Rdt=s(jge);Lor=r(Rdt,"xlm"),Rdt.forEach(t),Bor=r(zAe," \u2014 "),lO=n(zAe,"A",{href:!0});var Sdt=s(lO);xor=r(Sdt,"TFXLMWithLMHeadModel"),Sdt.forEach(t),kor=r(zAe," (XLM model)"),zAe.forEach(t),Ror=i(U),fT=n(U,"LI",{});var WAe=s(fT);Dge=n(WAe,"STRONG",{});var Pdt=s(Dge);Sor=r(Pdt,"xlm-roberta"),Pdt.forEach(t),Por=r(WAe," \u2014 "),iO=n(WAe,"A",{href:!0});var $dt=s(iO);$or=r($dt,"TFXLMRobertaForMaskedLM"),$dt.forEach(t),Ior=r(WAe," (XLM-RoBERTa model)"),WAe.forEach(t),jor=i(U),mT=n(U,"LI",{});var QAe=s(mT);Nge=n(QAe,"STRONG",{});var Idt=s(Nge);Dor=r(Idt,"xlnet"),Idt.forEach(t),Nor=r(QAe," \u2014 "),dO=n(QAe,"A",{href:!0});var jdt=s(dO);qor=r(jdt,"TFXLNetLMHeadModel"),jdt.forEach(t),Oor=r(QAe," (XLNet model)"),QAe.forEach(t),U.forEach(t),Gor=i(_a),qge=n(_a,"P",{});var Ddt=s(qge);Xor=r(Ddt,"Examples:"),Ddt.forEach(t),Vor=i(_a),m(Ow.$$.fragment,_a),_a.forEach(t),Ol.forEach(t),pke=i(d),vc=n(d,"H2",{class:!0});var wSe=s(vc);gT=n(wSe,"A",{id:!0,class:!0,href:!0});var Ndt=s(gT);Oge=n(Ndt,"SPAN",{});var qdt=s(Oge);m(Gw.$$.fragment,qdt),qdt.forEach(t),Ndt.forEach(t),zor=i(wSe),Gge=n(wSe,"SPAN",{});var Odt=s(Gge);Wor=r(Odt,"TFAutoModelForCausalLM"),Odt.forEach(t),wSe.forEach(t),_ke=i(d),Fr=n(d,"DIV",{class:!0});var Xl=s(Fr);m(Xw.$$.fragment,Xl),Qor=i(Xl),Tc=n(Xl,"P",{});var CW=s(Tc);Hor=r(CW,`This is a generic model class that will be instantiated as one of the model classes of the library (with a causal language modeling head) when created
with the `),Xge=n(CW,"CODE",{});var Gdt=s(Xge);Uor=r(Gdt,"from_pretrained()"),Gdt.forEach(t),Jor=r(CW,"class method or the "),Vge=n(CW,"CODE",{});var Xdt=s(Vge);Yor=r(Xdt,"from_config()"),Xdt.forEach(t),Kor=r(CW,`class
method.`),CW.forEach(t),Zor=i(Xl),Vw=n(Xl,"P",{});var ASe=s(Vw);err=r(ASe,"This class cannot be instantiated directly using "),zge=n(ASe,"CODE",{});var Vdt=s(zge);orr=r(Vdt,"__init__()"),Vdt.forEach(t),rrr=r(ASe," (throws an error)."),ASe.forEach(t),trr=i(Xl),pt=n(Xl,"DIV",{class:!0});var Vl=s(pt);m(zw.$$.fragment,Vl),arr=i(Vl),Wge=n(Vl,"P",{});var zdt=s(Wge);nrr=r(zdt,"Instantiates one of the model classes of the library (with a causal language modeling head) from a configuration."),zdt.forEach(t),srr=i(Vl),Fc=n(Vl,"P",{});var MW=s(Fc);lrr=r(MW,`Note:
Loading a model from its configuration file does `),Qge=n(MW,"STRONG",{});var Wdt=s(Qge);irr=r(Wdt,"not"),Wdt.forEach(t),drr=r(MW,` load the model weights. It only affects the
model\u2019s configuration. Use `),Hge=n(MW,"CODE",{});var Qdt=s(Hge);crr=r(Qdt,"from_pretrained()"),Qdt.forEach(t),frr=r(MW,"to load the model weights."),MW.forEach(t),mrr=i(Vl),Uge=n(Vl,"P",{});var Hdt=s(Uge);grr=r(Hdt,"Examples:"),Hdt.forEach(t),hrr=i(Vl),m(Ww.$$.fragment,Vl),Vl.forEach(t),prr=i(Xl),_o=n(Xl,"DIV",{class:!0});var ua=s(_o);m(Qw.$$.fragment,ua),_rr=i(ua),Jge=n(ua,"P",{});var Udt=s(Jge);urr=r(Udt,"Instantiate one of the model classes of the library (with a causal language modeling head) from a pretrained model."),Udt.forEach(t),brr=i(ua),bn=n(ua,"P",{});var EM=s(bn);vrr=r(EM,"The model class to instantiate is selected based on the "),Yge=n(EM,"CODE",{});var Jdt=s(Yge);Trr=r(Jdt,"model_type"),Jdt.forEach(t),Frr=r(EM,` property of the config object (either
passed as an argument or loaded from `),Kge=n(EM,"CODE",{});var Ydt=s(Kge);Crr=r(Ydt,"pretrained_model_name_or_path"),Ydt.forEach(t),Mrr=r(EM,` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),Zge=n(EM,"CODE",{});var Kdt=s(Zge);Err=r(Kdt,"pretrained_model_name_or_path"),Kdt.forEach(t),yrr=r(EM,":"),EM.forEach(t),wrr=i(ua),me=n(ua,"UL",{});var Te=s(me);hT=n(Te,"LI",{});var HAe=s(hT);ehe=n(HAe,"STRONG",{});var Zdt=s(ehe);Arr=r(Zdt,"bert"),Zdt.forEach(t),Lrr=r(HAe," \u2014 "),cO=n(HAe,"A",{href:!0});var ect=s(cO);Brr=r(ect,"TFBertLMHeadModel"),ect.forEach(t),xrr=r(HAe," (BERT model)"),HAe.forEach(t),krr=i(Te),pT=n(Te,"LI",{});var UAe=s(pT);ohe=n(UAe,"STRONG",{});var oct=s(ohe);Rrr=r(oct,"camembert"),oct.forEach(t),Srr=r(UAe," \u2014 "),fO=n(UAe,"A",{href:!0});var rct=s(fO);Prr=r(rct,"TFCamembertForCausalLM"),rct.forEach(t),$rr=r(UAe," (CamemBERT model)"),UAe.forEach(t),Irr=i(Te),_T=n(Te,"LI",{});var JAe=s(_T);rhe=n(JAe,"STRONG",{});var tct=s(rhe);jrr=r(tct,"ctrl"),tct.forEach(t),Drr=r(JAe," \u2014 "),mO=n(JAe,"A",{href:!0});var act=s(mO);Nrr=r(act,"TFCTRLLMHeadModel"),act.forEach(t),qrr=r(JAe," (CTRL model)"),JAe.forEach(t),Orr=i(Te),uT=n(Te,"LI",{});var YAe=s(uT);the=n(YAe,"STRONG",{});var nct=s(the);Grr=r(nct,"gpt2"),nct.forEach(t),Xrr=r(YAe," \u2014 "),gO=n(YAe,"A",{href:!0});var sct=s(gO);Vrr=r(sct,"TFGPT2LMHeadModel"),sct.forEach(t),zrr=r(YAe," (OpenAI GPT-2 model)"),YAe.forEach(t),Wrr=i(Te),bT=n(Te,"LI",{});var KAe=s(bT);ahe=n(KAe,"STRONG",{});var lct=s(ahe);Qrr=r(lct,"openai-gpt"),lct.forEach(t),Hrr=r(KAe," \u2014 "),hO=n(KAe,"A",{href:!0});var ict=s(hO);Urr=r(ict,"TFOpenAIGPTLMHeadModel"),ict.forEach(t),Jrr=r(KAe," (OpenAI GPT model)"),KAe.forEach(t),Yrr=i(Te),vT=n(Te,"LI",{});var ZAe=s(vT);nhe=n(ZAe,"STRONG",{});var dct=s(nhe);Krr=r(dct,"rembert"),dct.forEach(t),Zrr=r(ZAe," \u2014 "),pO=n(ZAe,"A",{href:!0});var cct=s(pO);etr=r(cct,"TFRemBertForCausalLM"),cct.forEach(t),otr=r(ZAe," (RemBERT model)"),ZAe.forEach(t),rtr=i(Te),TT=n(Te,"LI",{});var e0e=s(TT);she=n(e0e,"STRONG",{});var fct=s(she);ttr=r(fct,"roberta"),fct.forEach(t),atr=r(e0e," \u2014 "),_O=n(e0e,"A",{href:!0});var mct=s(_O);ntr=r(mct,"TFRobertaForCausalLM"),mct.forEach(t),str=r(e0e," (RoBERTa model)"),e0e.forEach(t),ltr=i(Te),FT=n(Te,"LI",{});var o0e=s(FT);lhe=n(o0e,"STRONG",{});var gct=s(lhe);itr=r(gct,"roformer"),gct.forEach(t),dtr=r(o0e," \u2014 "),uO=n(o0e,"A",{href:!0});var hct=s(uO);ctr=r(hct,"TFRoFormerForCausalLM"),hct.forEach(t),ftr=r(o0e," (RoFormer model)"),o0e.forEach(t),mtr=i(Te),CT=n(Te,"LI",{});var r0e=s(CT);ihe=n(r0e,"STRONG",{});var pct=s(ihe);gtr=r(pct,"transfo-xl"),pct.forEach(t),htr=r(r0e," \u2014 "),bO=n(r0e,"A",{href:!0});var _ct=s(bO);ptr=r(_ct,"TFTransfoXLLMHeadModel"),_ct.forEach(t),_tr=r(r0e," (Transformer-XL model)"),r0e.forEach(t),utr=i(Te),MT=n(Te,"LI",{});var t0e=s(MT);dhe=n(t0e,"STRONG",{});var uct=s(dhe);btr=r(uct,"xlm"),uct.forEach(t),vtr=r(t0e," \u2014 "),vO=n(t0e,"A",{href:!0});var bct=s(vO);Ttr=r(bct,"TFXLMWithLMHeadModel"),bct.forEach(t),Ftr=r(t0e," (XLM model)"),t0e.forEach(t),Ctr=i(Te),ET=n(Te,"LI",{});var a0e=s(ET);che=n(a0e,"STRONG",{});var vct=s(che);Mtr=r(vct,"xlnet"),vct.forEach(t),Etr=r(a0e," \u2014 "),TO=n(a0e,"A",{href:!0});var Tct=s(TO);ytr=r(Tct,"TFXLNetLMHeadModel"),Tct.forEach(t),wtr=r(a0e," (XLNet model)"),a0e.forEach(t),Te.forEach(t),Atr=i(ua),fhe=n(ua,"P",{});var Fct=s(fhe);Ltr=r(Fct,"Examples:"),Fct.forEach(t),Btr=i(ua),m(Hw.$$.fragment,ua),ua.forEach(t),Xl.forEach(t),uke=i(d),Cc=n(d,"H2",{class:!0});var LSe=s(Cc);yT=n(LSe,"A",{id:!0,class:!0,href:!0});var Cct=s(yT);mhe=n(Cct,"SPAN",{});var Mct=s(mhe);m(Uw.$$.fragment,Mct),Mct.forEach(t),Cct.forEach(t),xtr=i(LSe),ghe=n(LSe,"SPAN",{});var Ect=s(ghe);ktr=r(Ect,"TFAutoModelForImageClassification"),Ect.forEach(t),LSe.forEach(t),bke=i(d),Cr=n(d,"DIV",{class:!0});var zl=s(Cr);m(Jw.$$.fragment,zl),Rtr=i(zl),Mc=n(zl,"P",{});var EW=s(Mc);Str=r(EW,`This is a generic model class that will be instantiated as one of the model classes of the library (with a image classification head) when created
with the `),hhe=n(EW,"CODE",{});var yct=s(hhe);Ptr=r(yct,"from_pretrained()"),yct.forEach(t),$tr=r(EW,"class method or the "),phe=n(EW,"CODE",{});var wct=s(phe);Itr=r(wct,"from_config()"),wct.forEach(t),jtr=r(EW,`class
method.`),EW.forEach(t),Dtr=i(zl),Yw=n(zl,"P",{});var BSe=s(Yw);Ntr=r(BSe,"This class cannot be instantiated directly using "),_he=n(BSe,"CODE",{});var Act=s(_he);qtr=r(Act,"__init__()"),Act.forEach(t),Otr=r(BSe," (throws an error)."),BSe.forEach(t),Gtr=i(zl),_t=n(zl,"DIV",{class:!0});var Wl=s(_t);m(Kw.$$.fragment,Wl),Xtr=i(Wl),uhe=n(Wl,"P",{});var Lct=s(uhe);Vtr=r(Lct,"Instantiates one of the model classes of the library (with a image classification head) from a configuration."),Lct.forEach(t),ztr=i(Wl),Ec=n(Wl,"P",{});var yW=s(Ec);Wtr=r(yW,`Note:
Loading a model from its configuration file does `),bhe=n(yW,"STRONG",{});var Bct=s(bhe);Qtr=r(Bct,"not"),Bct.forEach(t),Htr=r(yW,` load the model weights. It only affects the
model\u2019s configuration. Use `),vhe=n(yW,"CODE",{});var xct=s(vhe);Utr=r(xct,"from_pretrained()"),xct.forEach(t),Jtr=r(yW,"to load the model weights."),yW.forEach(t),Ytr=i(Wl),The=n(Wl,"P",{});var kct=s(The);Ktr=r(kct,"Examples:"),kct.forEach(t),Ztr=i(Wl),m(Zw.$$.fragment,Wl),Wl.forEach(t),ear=i(zl),uo=n(zl,"DIV",{class:!0});var ba=s(uo);m(e6.$$.fragment,ba),oar=i(ba),Fhe=n(ba,"P",{});var Rct=s(Fhe);rar=r(Rct,"Instantiate one of the model classes of the library (with a image classification head) from a pretrained model."),Rct.forEach(t),tar=i(ba),vn=n(ba,"P",{});var yM=s(vn);aar=r(yM,"The model class to instantiate is selected based on the "),Che=n(yM,"CODE",{});var Sct=s(Che);nar=r(Sct,"model_type"),Sct.forEach(t),sar=r(yM,` property of the config object (either
passed as an argument or loaded from `),Mhe=n(yM,"CODE",{});var Pct=s(Mhe);lar=r(Pct,"pretrained_model_name_or_path"),Pct.forEach(t),iar=r(yM,` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),Ehe=n(yM,"CODE",{});var $ct=s(Ehe);dar=r($ct,"pretrained_model_name_or_path"),$ct.forEach(t),car=r(yM,":"),yM.forEach(t),far=i(ba),o6=n(ba,"UL",{});var xSe=s(o6);wT=n(xSe,"LI",{});var n0e=s(wT);yhe=n(n0e,"STRONG",{});var Ict=s(yhe);mar=r(Ict,"convnext"),Ict.forEach(t),gar=r(n0e," \u2014 "),FO=n(n0e,"A",{href:!0});var jct=s(FO);har=r(jct,"TFConvNextForImageClassification"),jct.forEach(t),par=r(n0e," (ConvNext model)"),n0e.forEach(t),_ar=i(xSe),AT=n(xSe,"LI",{});var s0e=s(AT);whe=n(s0e,"STRONG",{});var Dct=s(whe);uar=r(Dct,"vit"),Dct.forEach(t),bar=r(s0e," \u2014 "),CO=n(s0e,"A",{href:!0});var Nct=s(CO);Tar=r(Nct,"TFViTForImageClassification"),Nct.forEach(t),Far=r(s0e," (ViT model)"),s0e.forEach(t),xSe.forEach(t),Car=i(ba),Ahe=n(ba,"P",{});var qct=s(Ahe);Mar=r(qct,"Examples:"),qct.forEach(t),Ear=i(ba),m(r6.$$.fragment,ba),ba.forEach(t),zl.forEach(t),vke=i(d),yc=n(d,"H2",{class:!0});var kSe=s(yc);LT=n(kSe,"A",{id:!0,class:!0,href:!0});var Oct=s(LT);Lhe=n(Oct,"SPAN",{});var Gct=s(Lhe);m(t6.$$.fragment,Gct),Gct.forEach(t),Oct.forEach(t),yar=i(kSe),Bhe=n(kSe,"SPAN",{});var Xct=s(Bhe);war=r(Xct,"TFAutoModelForMaskedLM"),Xct.forEach(t),kSe.forEach(t),Tke=i(d),Mr=n(d,"DIV",{class:!0});var Ql=s(Mr);m(a6.$$.fragment,Ql),Aar=i(Ql),wc=n(Ql,"P",{});var wW=s(wc);Lar=r(wW,`This is a generic model class that will be instantiated as one of the model classes of the library (with a masked language modeling head) when created
with the `),xhe=n(wW,"CODE",{});var Vct=s(xhe);Bar=r(Vct,"from_pretrained()"),Vct.forEach(t),xar=r(wW,"class method or the "),khe=n(wW,"CODE",{});var zct=s(khe);kar=r(zct,"from_config()"),zct.forEach(t),Rar=r(wW,`class
method.`),wW.forEach(t),Sar=i(Ql),n6=n(Ql,"P",{});var RSe=s(n6);Par=r(RSe,"This class cannot be instantiated directly using "),Rhe=n(RSe,"CODE",{});var Wct=s(Rhe);$ar=r(Wct,"__init__()"),Wct.forEach(t),Iar=r(RSe," (throws an error)."),RSe.forEach(t),jar=i(Ql),ut=n(Ql,"DIV",{class:!0});var Hl=s(ut);m(s6.$$.fragment,Hl),Dar=i(Hl),She=n(Hl,"P",{});var Qct=s(She);Nar=r(Qct,"Instantiates one of the model classes of the library (with a masked language modeling head) from a configuration."),Qct.forEach(t),qar=i(Hl),Ac=n(Hl,"P",{});var AW=s(Ac);Oar=r(AW,`Note:
Loading a model from its configuration file does `),Phe=n(AW,"STRONG",{});var Hct=s(Phe);Gar=r(Hct,"not"),Hct.forEach(t),Xar=r(AW,` load the model weights. It only affects the
model\u2019s configuration. Use `),$he=n(AW,"CODE",{});var Uct=s($he);Var=r(Uct,"from_pretrained()"),Uct.forEach(t),zar=r(AW,"to load the model weights."),AW.forEach(t),War=i(Hl),Ihe=n(Hl,"P",{});var Jct=s(Ihe);Qar=r(Jct,"Examples:"),Jct.forEach(t),Har=i(Hl),m(l6.$$.fragment,Hl),Hl.forEach(t),Uar=i(Ql),bo=n(Ql,"DIV",{class:!0});var va=s(bo);m(i6.$$.fragment,va),Jar=i(va),jhe=n(va,"P",{});var Yct=s(jhe);Yar=r(Yct,"Instantiate one of the model classes of the library (with a masked language modeling head) from a pretrained model."),Yct.forEach(t),Kar=i(va),Tn=n(va,"P",{});var wM=s(Tn);Zar=r(wM,"The model class to instantiate is selected based on the "),Dhe=n(wM,"CODE",{});var Kct=s(Dhe);enr=r(Kct,"model_type"),Kct.forEach(t),onr=r(wM,` property of the config object (either
passed as an argument or loaded from `),Nhe=n(wM,"CODE",{});var Zct=s(Nhe);rnr=r(Zct,"pretrained_model_name_or_path"),Zct.forEach(t),tnr=r(wM,` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),qhe=n(wM,"CODE",{});var eft=s(qhe);anr=r(eft,"pretrained_model_name_or_path"),eft.forEach(t),nnr=r(wM,":"),wM.forEach(t),snr=i(va),Y=n(va,"UL",{});var ee=s(Y);BT=n(ee,"LI",{});var l0e=s(BT);Ohe=n(l0e,"STRONG",{});var oft=s(Ohe);lnr=r(oft,"albert"),oft.forEach(t),inr=r(l0e," \u2014 "),MO=n(l0e,"A",{href:!0});var rft=s(MO);dnr=r(rft,"TFAlbertForMaskedLM"),rft.forEach(t),cnr=r(l0e," (ALBERT model)"),l0e.forEach(t),fnr=i(ee),xT=n(ee,"LI",{});var i0e=s(xT);Ghe=n(i0e,"STRONG",{});var tft=s(Ghe);mnr=r(tft,"bert"),tft.forEach(t),gnr=r(i0e," \u2014 "),EO=n(i0e,"A",{href:!0});var aft=s(EO);hnr=r(aft,"TFBertForMaskedLM"),aft.forEach(t),pnr=r(i0e," (BERT model)"),i0e.forEach(t),_nr=i(ee),kT=n(ee,"LI",{});var d0e=s(kT);Xhe=n(d0e,"STRONG",{});var nft=s(Xhe);unr=r(nft,"camembert"),nft.forEach(t),bnr=r(d0e," \u2014 "),yO=n(d0e,"A",{href:!0});var sft=s(yO);vnr=r(sft,"TFCamembertForMaskedLM"),sft.forEach(t),Tnr=r(d0e," (CamemBERT model)"),d0e.forEach(t),Fnr=i(ee),RT=n(ee,"LI",{});var c0e=s(RT);Vhe=n(c0e,"STRONG",{});var lft=s(Vhe);Cnr=r(lft,"convbert"),lft.forEach(t),Mnr=r(c0e," \u2014 "),wO=n(c0e,"A",{href:!0});var ift=s(wO);Enr=r(ift,"TFConvBertForMaskedLM"),ift.forEach(t),ynr=r(c0e," (ConvBERT model)"),c0e.forEach(t),wnr=i(ee),ST=n(ee,"LI",{});var f0e=s(ST);zhe=n(f0e,"STRONG",{});var dft=s(zhe);Anr=r(dft,"deberta"),dft.forEach(t),Lnr=r(f0e," \u2014 "),AO=n(f0e,"A",{href:!0});var cft=s(AO);Bnr=r(cft,"TFDebertaForMaskedLM"),cft.forEach(t),xnr=r(f0e," (DeBERTa model)"),f0e.forEach(t),knr=i(ee),PT=n(ee,"LI",{});var m0e=s(PT);Whe=n(m0e,"STRONG",{});var fft=s(Whe);Rnr=r(fft,"deberta-v2"),fft.forEach(t),Snr=r(m0e," \u2014 "),LO=n(m0e,"A",{href:!0});var mft=s(LO);Pnr=r(mft,"TFDebertaV2ForMaskedLM"),mft.forEach(t),$nr=r(m0e," (DeBERTa-v2 model)"),m0e.forEach(t),Inr=i(ee),$T=n(ee,"LI",{});var g0e=s($T);Qhe=n(g0e,"STRONG",{});var gft=s(Qhe);jnr=r(gft,"distilbert"),gft.forEach(t),Dnr=r(g0e," \u2014 "),BO=n(g0e,"A",{href:!0});var hft=s(BO);Nnr=r(hft,"TFDistilBertForMaskedLM"),hft.forEach(t),qnr=r(g0e," (DistilBERT model)"),g0e.forEach(t),Onr=i(ee),IT=n(ee,"LI",{});var h0e=s(IT);Hhe=n(h0e,"STRONG",{});var pft=s(Hhe);Gnr=r(pft,"electra"),pft.forEach(t),Xnr=r(h0e," \u2014 "),xO=n(h0e,"A",{href:!0});var _ft=s(xO);Vnr=r(_ft,"TFElectraForMaskedLM"),_ft.forEach(t),znr=r(h0e," (ELECTRA model)"),h0e.forEach(t),Wnr=i(ee),jT=n(ee,"LI",{});var p0e=s(jT);Uhe=n(p0e,"STRONG",{});var uft=s(Uhe);Qnr=r(uft,"flaubert"),uft.forEach(t),Hnr=r(p0e," \u2014 "),kO=n(p0e,"A",{href:!0});var bft=s(kO);Unr=r(bft,"TFFlaubertWithLMHeadModel"),bft.forEach(t),Jnr=r(p0e," (FlauBERT model)"),p0e.forEach(t),Ynr=i(ee),DT=n(ee,"LI",{});var _0e=s(DT);Jhe=n(_0e,"STRONG",{});var vft=s(Jhe);Knr=r(vft,"funnel"),vft.forEach(t),Znr=r(_0e," \u2014 "),RO=n(_0e,"A",{href:!0});var Tft=s(RO);esr=r(Tft,"TFFunnelForMaskedLM"),Tft.forEach(t),osr=r(_0e," (Funnel Transformer model)"),_0e.forEach(t),rsr=i(ee),NT=n(ee,"LI",{});var u0e=s(NT);Yhe=n(u0e,"STRONG",{});var Fft=s(Yhe);tsr=r(Fft,"layoutlm"),Fft.forEach(t),asr=r(u0e," \u2014 "),SO=n(u0e,"A",{href:!0});var Cft=s(SO);nsr=r(Cft,"TFLayoutLMForMaskedLM"),Cft.forEach(t),ssr=r(u0e," (LayoutLM model)"),u0e.forEach(t),lsr=i(ee),qT=n(ee,"LI",{});var b0e=s(qT);Khe=n(b0e,"STRONG",{});var Mft=s(Khe);isr=r(Mft,"longformer"),Mft.forEach(t),dsr=r(b0e," \u2014 "),PO=n(b0e,"A",{href:!0});var Eft=s(PO);csr=r(Eft,"TFLongformerForMaskedLM"),Eft.forEach(t),fsr=r(b0e," (Longformer model)"),b0e.forEach(t),msr=i(ee),OT=n(ee,"LI",{});var v0e=s(OT);Zhe=n(v0e,"STRONG",{});var yft=s(Zhe);gsr=r(yft,"mobilebert"),yft.forEach(t),hsr=r(v0e," \u2014 "),$O=n(v0e,"A",{href:!0});var wft=s($O);psr=r(wft,"TFMobileBertForMaskedLM"),wft.forEach(t),_sr=r(v0e," (MobileBERT model)"),v0e.forEach(t),usr=i(ee),GT=n(ee,"LI",{});var T0e=s(GT);epe=n(T0e,"STRONG",{});var Aft=s(epe);bsr=r(Aft,"mpnet"),Aft.forEach(t),vsr=r(T0e," \u2014 "),IO=n(T0e,"A",{href:!0});var Lft=s(IO);Tsr=r(Lft,"TFMPNetForMaskedLM"),Lft.forEach(t),Fsr=r(T0e," (MPNet model)"),T0e.forEach(t),Csr=i(ee),XT=n(ee,"LI",{});var F0e=s(XT);ope=n(F0e,"STRONG",{});var Bft=s(ope);Msr=r(Bft,"rembert"),Bft.forEach(t),Esr=r(F0e," \u2014 "),jO=n(F0e,"A",{href:!0});var xft=s(jO);ysr=r(xft,"TFRemBertForMaskedLM"),xft.forEach(t),wsr=r(F0e," (RemBERT model)"),F0e.forEach(t),Asr=i(ee),VT=n(ee,"LI",{});var C0e=s(VT);rpe=n(C0e,"STRONG",{});var kft=s(rpe);Lsr=r(kft,"roberta"),kft.forEach(t),Bsr=r(C0e," \u2014 "),DO=n(C0e,"A",{href:!0});var Rft=s(DO);xsr=r(Rft,"TFRobertaForMaskedLM"),Rft.forEach(t),ksr=r(C0e," (RoBERTa model)"),C0e.forEach(t),Rsr=i(ee),zT=n(ee,"LI",{});var M0e=s(zT);tpe=n(M0e,"STRONG",{});var Sft=s(tpe);Ssr=r(Sft,"roformer"),Sft.forEach(t),Psr=r(M0e," \u2014 "),NO=n(M0e,"A",{href:!0});var Pft=s(NO);$sr=r(Pft,"TFRoFormerForMaskedLM"),Pft.forEach(t),Isr=r(M0e," (RoFormer model)"),M0e.forEach(t),jsr=i(ee),WT=n(ee,"LI",{});var E0e=s(WT);ape=n(E0e,"STRONG",{});var $ft=s(ape);Dsr=r($ft,"tapas"),$ft.forEach(t),Nsr=r(E0e," \u2014 "),qO=n(E0e,"A",{href:!0});var Ift=s(qO);qsr=r(Ift,"TFTapasForMaskedLM"),Ift.forEach(t),Osr=r(E0e," (TAPAS model)"),E0e.forEach(t),Gsr=i(ee),QT=n(ee,"LI",{});var y0e=s(QT);npe=n(y0e,"STRONG",{});var jft=s(npe);Xsr=r(jft,"xlm"),jft.forEach(t),Vsr=r(y0e," \u2014 "),OO=n(y0e,"A",{href:!0});var Dft=s(OO);zsr=r(Dft,"TFXLMWithLMHeadModel"),Dft.forEach(t),Wsr=r(y0e," (XLM model)"),y0e.forEach(t),Qsr=i(ee),HT=n(ee,"LI",{});var w0e=s(HT);spe=n(w0e,"STRONG",{});var Nft=s(spe);Hsr=r(Nft,"xlm-roberta"),Nft.forEach(t),Usr=r(w0e," \u2014 "),GO=n(w0e,"A",{href:!0});var qft=s(GO);Jsr=r(qft,"TFXLMRobertaForMaskedLM"),qft.forEach(t),Ysr=r(w0e," (XLM-RoBERTa model)"),w0e.forEach(t),ee.forEach(t),Ksr=i(va),lpe=n(va,"P",{});var Oft=s(lpe);Zsr=r(Oft,"Examples:"),Oft.forEach(t),elr=i(va),m(d6.$$.fragment,va),va.forEach(t),Ql.forEach(t),Fke=i(d),Lc=n(d,"H2",{class:!0});var SSe=s(Lc);UT=n(SSe,"A",{id:!0,class:!0,href:!0});var Gft=s(UT);ipe=n(Gft,"SPAN",{});var Xft=s(ipe);m(c6.$$.fragment,Xft),Xft.forEach(t),Gft.forEach(t),olr=i(SSe),dpe=n(SSe,"SPAN",{});var Vft=s(dpe);rlr=r(Vft,"TFAutoModelForSeq2SeqLM"),Vft.forEach(t),SSe.forEach(t),Cke=i(d),Er=n(d,"DIV",{class:!0});var Ul=s(Er);m(f6.$$.fragment,Ul),tlr=i(Ul),Bc=n(Ul,"P",{});var LW=s(Bc);alr=r(LW,`This is a generic model class that will be instantiated as one of the model classes of the library (with a sequence-to-sequence language modeling head) when created
with the `),cpe=n(LW,"CODE",{});var zft=s(cpe);nlr=r(zft,"from_pretrained()"),zft.forEach(t),slr=r(LW,"class method or the "),fpe=n(LW,"CODE",{});var Wft=s(fpe);llr=r(Wft,"from_config()"),Wft.forEach(t),ilr=r(LW,`class
method.`),LW.forEach(t),dlr=i(Ul),m6=n(Ul,"P",{});var PSe=s(m6);clr=r(PSe,"This class cannot be instantiated directly using "),mpe=n(PSe,"CODE",{});var Qft=s(mpe);flr=r(Qft,"__init__()"),Qft.forEach(t),mlr=r(PSe," (throws an error)."),PSe.forEach(t),glr=i(Ul),bt=n(Ul,"DIV",{class:!0});var Jl=s(bt);m(g6.$$.fragment,Jl),hlr=i(Jl),gpe=n(Jl,"P",{});var Hft=s(gpe);plr=r(Hft,"Instantiates one of the model classes of the library (with a sequence-to-sequence language modeling head) from a configuration."),Hft.forEach(t),_lr=i(Jl),xc=n(Jl,"P",{});var BW=s(xc);ulr=r(BW,`Note:
Loading a model from its configuration file does `),hpe=n(BW,"STRONG",{});var Uft=s(hpe);blr=r(Uft,"not"),Uft.forEach(t),vlr=r(BW,` load the model weights. It only affects the
model\u2019s configuration. Use `),ppe=n(BW,"CODE",{});var Jft=s(ppe);Tlr=r(Jft,"from_pretrained()"),Jft.forEach(t),Flr=r(BW,"to load the model weights."),BW.forEach(t),Clr=i(Jl),_pe=n(Jl,"P",{});var Yft=s(_pe);Mlr=r(Yft,"Examples:"),Yft.forEach(t),Elr=i(Jl),m(h6.$$.fragment,Jl),Jl.forEach(t),ylr=i(Ul),vo=n(Ul,"DIV",{class:!0});var Ta=s(vo);m(p6.$$.fragment,Ta),wlr=i(Ta),upe=n(Ta,"P",{});var Kft=s(upe);Alr=r(Kft,"Instantiate one of the model classes of the library (with a sequence-to-sequence language modeling head) from a pretrained model."),Kft.forEach(t),Llr=i(Ta),Fn=n(Ta,"P",{});var AM=s(Fn);Blr=r(AM,"The model class to instantiate is selected based on the "),bpe=n(AM,"CODE",{});var Zft=s(bpe);xlr=r(Zft,"model_type"),Zft.forEach(t),klr=r(AM,` property of the config object (either
passed as an argument or loaded from `),vpe=n(AM,"CODE",{});var emt=s(vpe);Rlr=r(emt,"pretrained_model_name_or_path"),emt.forEach(t),Slr=r(AM,` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),Tpe=n(AM,"CODE",{});var omt=s(Tpe);Plr=r(omt,"pretrained_model_name_or_path"),omt.forEach(t),$lr=r(AM,":"),AM.forEach(t),Ilr=i(Ta),_e=n(Ta,"UL",{});var Ee=s(_e);JT=n(Ee,"LI",{});var A0e=s(JT);Fpe=n(A0e,"STRONG",{});var rmt=s(Fpe);jlr=r(rmt,"bart"),rmt.forEach(t),Dlr=r(A0e," \u2014 "),XO=n(A0e,"A",{href:!0});var tmt=s(XO);Nlr=r(tmt,"TFBartForConditionalGeneration"),tmt.forEach(t),qlr=r(A0e," (BART model)"),A0e.forEach(t),Olr=i(Ee),YT=n(Ee,"LI",{});var L0e=s(YT);Cpe=n(L0e,"STRONG",{});var amt=s(Cpe);Glr=r(amt,"blenderbot"),amt.forEach(t),Xlr=r(L0e," \u2014 "),VO=n(L0e,"A",{href:!0});var nmt=s(VO);Vlr=r(nmt,"TFBlenderbotForConditionalGeneration"),nmt.forEach(t),zlr=r(L0e," (Blenderbot model)"),L0e.forEach(t),Wlr=i(Ee),KT=n(Ee,"LI",{});var B0e=s(KT);Mpe=n(B0e,"STRONG",{});var smt=s(Mpe);Qlr=r(smt,"blenderbot-small"),smt.forEach(t),Hlr=r(B0e," \u2014 "),zO=n(B0e,"A",{href:!0});var lmt=s(zO);Ulr=r(lmt,"TFBlenderbotSmallForConditionalGeneration"),lmt.forEach(t),Jlr=r(B0e," (BlenderbotSmall model)"),B0e.forEach(t),Ylr=i(Ee),ZT=n(Ee,"LI",{});var x0e=s(ZT);Epe=n(x0e,"STRONG",{});var imt=s(Epe);Klr=r(imt,"encoder-decoder"),imt.forEach(t),Zlr=r(x0e," \u2014 "),WO=n(x0e,"A",{href:!0});var dmt=s(WO);eir=r(dmt,"TFEncoderDecoderModel"),dmt.forEach(t),oir=r(x0e," (Encoder decoder model)"),x0e.forEach(t),rir=i(Ee),e8=n(Ee,"LI",{});var k0e=s(e8);ype=n(k0e,"STRONG",{});var cmt=s(ype);tir=r(cmt,"led"),cmt.forEach(t),air=r(k0e," \u2014 "),QO=n(k0e,"A",{href:!0});var fmt=s(QO);nir=r(fmt,"TFLEDForConditionalGeneration"),fmt.forEach(t),sir=r(k0e," (LED model)"),k0e.forEach(t),lir=i(Ee),o8=n(Ee,"LI",{});var R0e=s(o8);wpe=n(R0e,"STRONG",{});var mmt=s(wpe);iir=r(mmt,"marian"),mmt.forEach(t),dir=r(R0e," \u2014 "),HO=n(R0e,"A",{href:!0});var gmt=s(HO);cir=r(gmt,"TFMarianMTModel"),gmt.forEach(t),fir=r(R0e," (Marian model)"),R0e.forEach(t),mir=i(Ee),r8=n(Ee,"LI",{});var S0e=s(r8);Ape=n(S0e,"STRONG",{});var hmt=s(Ape);gir=r(hmt,"mbart"),hmt.forEach(t),hir=r(S0e," \u2014 "),UO=n(S0e,"A",{href:!0});var pmt=s(UO);pir=r(pmt,"TFMBartForConditionalGeneration"),pmt.forEach(t),_ir=r(S0e," (mBART model)"),S0e.forEach(t),uir=i(Ee),t8=n(Ee,"LI",{});var P0e=s(t8);Lpe=n(P0e,"STRONG",{});var _mt=s(Lpe);bir=r(_mt,"mt5"),_mt.forEach(t),vir=r(P0e," \u2014 "),JO=n(P0e,"A",{href:!0});var umt=s(JO);Tir=r(umt,"TFMT5ForConditionalGeneration"),umt.forEach(t),Fir=r(P0e," (mT5 model)"),P0e.forEach(t),Cir=i(Ee),a8=n(Ee,"LI",{});var $0e=s(a8);Bpe=n($0e,"STRONG",{});var bmt=s(Bpe);Mir=r(bmt,"pegasus"),bmt.forEach(t),Eir=r($0e," \u2014 "),YO=n($0e,"A",{href:!0});var vmt=s(YO);yir=r(vmt,"TFPegasusForConditionalGeneration"),vmt.forEach(t),wir=r($0e," (Pegasus model)"),$0e.forEach(t),Air=i(Ee),n8=n(Ee,"LI",{});var I0e=s(n8);xpe=n(I0e,"STRONG",{});var Tmt=s(xpe);Lir=r(Tmt,"t5"),Tmt.forEach(t),Bir=r(I0e," \u2014 "),KO=n(I0e,"A",{href:!0});var Fmt=s(KO);xir=r(Fmt,"TFT5ForConditionalGeneration"),Fmt.forEach(t),kir=r(I0e," (T5 model)"),I0e.forEach(t),Ee.forEach(t),Rir=i(Ta),kpe=n(Ta,"P",{});var Cmt=s(kpe);Sir=r(Cmt,"Examples:"),Cmt.forEach(t),Pir=i(Ta),m(_6.$$.fragment,Ta),Ta.forEach(t),Ul.forEach(t),Mke=i(d),kc=n(d,"H2",{class:!0});var $Se=s(kc);s8=n($Se,"A",{id:!0,class:!0,href:!0});var Mmt=s(s8);Rpe=n(Mmt,"SPAN",{});var Emt=s(Rpe);m(u6.$$.fragment,Emt),Emt.forEach(t),Mmt.forEach(t),$ir=i($Se),Spe=n($Se,"SPAN",{});var ymt=s(Spe);Iir=r(ymt,"TFAutoModelForSequenceClassification"),ymt.forEach(t),$Se.forEach(t),Eke=i(d),yr=n(d,"DIV",{class:!0});var Yl=s(yr);m(b6.$$.fragment,Yl),jir=i(Yl),Rc=n(Yl,"P",{});var xW=s(Rc);Dir=r(xW,`This is a generic model class that will be instantiated as one of the model classes of the library (with a sequence classification head) when created
with the `),Ppe=n(xW,"CODE",{});var wmt=s(Ppe);Nir=r(wmt,"from_pretrained()"),wmt.forEach(t),qir=r(xW,"class method or the "),$pe=n(xW,"CODE",{});var Amt=s($pe);Oir=r(Amt,"from_config()"),Amt.forEach(t),Gir=r(xW,`class
method.`),xW.forEach(t),Xir=i(Yl),v6=n(Yl,"P",{});var ISe=s(v6);Vir=r(ISe,"This class cannot be instantiated directly using "),Ipe=n(ISe,"CODE",{});var Lmt=s(Ipe);zir=r(Lmt,"__init__()"),Lmt.forEach(t),Wir=r(ISe," (throws an error)."),ISe.forEach(t),Qir=i(Yl),vt=n(Yl,"DIV",{class:!0});var Kl=s(vt);m(T6.$$.fragment,Kl),Hir=i(Kl),jpe=n(Kl,"P",{});var Bmt=s(jpe);Uir=r(Bmt,"Instantiates one of the model classes of the library (with a sequence classification head) from a configuration."),Bmt.forEach(t),Jir=i(Kl),Sc=n(Kl,"P",{});var kW=s(Sc);Yir=r(kW,`Note:
Loading a model from its configuration file does `),Dpe=n(kW,"STRONG",{});var xmt=s(Dpe);Kir=r(xmt,"not"),xmt.forEach(t),Zir=r(kW,` load the model weights. It only affects the
model\u2019s configuration. Use `),Npe=n(kW,"CODE",{});var kmt=s(Npe);edr=r(kmt,"from_pretrained()"),kmt.forEach(t),odr=r(kW,"to load the model weights."),kW.forEach(t),rdr=i(Kl),qpe=n(Kl,"P",{});var Rmt=s(qpe);tdr=r(Rmt,"Examples:"),Rmt.forEach(t),adr=i(Kl),m(F6.$$.fragment,Kl),Kl.forEach(t),ndr=i(Yl),To=n(Yl,"DIV",{class:!0});var Fa=s(To);m(C6.$$.fragment,Fa),sdr=i(Fa),Ope=n(Fa,"P",{});var Smt=s(Ope);ldr=r(Smt,"Instantiate one of the model classes of the library (with a sequence classification head) from a pretrained model."),Smt.forEach(t),idr=i(Fa),Cn=n(Fa,"P",{});var LM=s(Cn);ddr=r(LM,"The model class to instantiate is selected based on the "),Gpe=n(LM,"CODE",{});var Pmt=s(Gpe);cdr=r(Pmt,"model_type"),Pmt.forEach(t),fdr=r(LM,` property of the config object (either
passed as an argument or loaded from `),Xpe=n(LM,"CODE",{});var $mt=s(Xpe);mdr=r($mt,"pretrained_model_name_or_path"),$mt.forEach(t),gdr=r(LM,` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),Vpe=n(LM,"CODE",{});var Imt=s(Vpe);hdr=r(Imt,"pretrained_model_name_or_path"),Imt.forEach(t),pdr=r(LM,":"),LM.forEach(t),_dr=i(Fa),V=n(Fa,"UL",{});var W=s(V);l8=n(W,"LI",{});var j0e=s(l8);zpe=n(j0e,"STRONG",{});var jmt=s(zpe);udr=r(jmt,"albert"),jmt.forEach(t),bdr=r(j0e," \u2014 "),ZO=n(j0e,"A",{href:!0});var Dmt=s(ZO);vdr=r(Dmt,"TFAlbertForSequenceClassification"),Dmt.forEach(t),Tdr=r(j0e," (ALBERT model)"),j0e.forEach(t),Fdr=i(W),i8=n(W,"LI",{});var D0e=s(i8);Wpe=n(D0e,"STRONG",{});var Nmt=s(Wpe);Cdr=r(Nmt,"bert"),Nmt.forEach(t),Mdr=r(D0e," \u2014 "),eG=n(D0e,"A",{href:!0});var qmt=s(eG);Edr=r(qmt,"TFBertForSequenceClassification"),qmt.forEach(t),ydr=r(D0e," (BERT model)"),D0e.forEach(t),wdr=i(W),d8=n(W,"LI",{});var N0e=s(d8);Qpe=n(N0e,"STRONG",{});var Omt=s(Qpe);Adr=r(Omt,"camembert"),Omt.forEach(t),Ldr=r(N0e," \u2014 "),oG=n(N0e,"A",{href:!0});var Gmt=s(oG);Bdr=r(Gmt,"TFCamembertForSequenceClassification"),Gmt.forEach(t),xdr=r(N0e," (CamemBERT model)"),N0e.forEach(t),kdr=i(W),c8=n(W,"LI",{});var q0e=s(c8);Hpe=n(q0e,"STRONG",{});var Xmt=s(Hpe);Rdr=r(Xmt,"convbert"),Xmt.forEach(t),Sdr=r(q0e," \u2014 "),rG=n(q0e,"A",{href:!0});var Vmt=s(rG);Pdr=r(Vmt,"TFConvBertForSequenceClassification"),Vmt.forEach(t),$dr=r(q0e," (ConvBERT model)"),q0e.forEach(t),Idr=i(W),f8=n(W,"LI",{});var O0e=s(f8);Upe=n(O0e,"STRONG",{});var zmt=s(Upe);jdr=r(zmt,"ctrl"),zmt.forEach(t),Ddr=r(O0e," \u2014 "),tG=n(O0e,"A",{href:!0});var Wmt=s(tG);Ndr=r(Wmt,"TFCTRLForSequenceClassification"),Wmt.forEach(t),qdr=r(O0e," (CTRL model)"),O0e.forEach(t),Odr=i(W),m8=n(W,"LI",{});var G0e=s(m8);Jpe=n(G0e,"STRONG",{});var Qmt=s(Jpe);Gdr=r(Qmt,"deberta"),Qmt.forEach(t),Xdr=r(G0e," \u2014 "),aG=n(G0e,"A",{href:!0});var Hmt=s(aG);Vdr=r(Hmt,"TFDebertaForSequenceClassification"),Hmt.forEach(t),zdr=r(G0e," (DeBERTa model)"),G0e.forEach(t),Wdr=i(W),g8=n(W,"LI",{});var X0e=s(g8);Ype=n(X0e,"STRONG",{});var Umt=s(Ype);Qdr=r(Umt,"deberta-v2"),Umt.forEach(t),Hdr=r(X0e," \u2014 "),nG=n(X0e,"A",{href:!0});var Jmt=s(nG);Udr=r(Jmt,"TFDebertaV2ForSequenceClassification"),Jmt.forEach(t),Jdr=r(X0e," (DeBERTa-v2 model)"),X0e.forEach(t),Ydr=i(W),h8=n(W,"LI",{});var V0e=s(h8);Kpe=n(V0e,"STRONG",{});var Ymt=s(Kpe);Kdr=r(Ymt,"distilbert"),Ymt.forEach(t),Zdr=r(V0e," \u2014 "),sG=n(V0e,"A",{href:!0});var Kmt=s(sG);ecr=r(Kmt,"TFDistilBertForSequenceClassification"),Kmt.forEach(t),ocr=r(V0e," (DistilBERT model)"),V0e.forEach(t),rcr=i(W),p8=n(W,"LI",{});var z0e=s(p8);Zpe=n(z0e,"STRONG",{});var Zmt=s(Zpe);tcr=r(Zmt,"electra"),Zmt.forEach(t),acr=r(z0e," \u2014 "),lG=n(z0e,"A",{href:!0});var egt=s(lG);ncr=r(egt,"TFElectraForSequenceClassification"),egt.forEach(t),scr=r(z0e," (ELECTRA model)"),z0e.forEach(t),lcr=i(W),_8=n(W,"LI",{});var W0e=s(_8);e_e=n(W0e,"STRONG",{});var ogt=s(e_e);icr=r(ogt,"flaubert"),ogt.forEach(t),dcr=r(W0e," \u2014 "),iG=n(W0e,"A",{href:!0});var rgt=s(iG);ccr=r(rgt,"TFFlaubertForSequenceClassification"),rgt.forEach(t),fcr=r(W0e," (FlauBERT model)"),W0e.forEach(t),mcr=i(W),u8=n(W,"LI",{});var Q0e=s(u8);o_e=n(Q0e,"STRONG",{});var tgt=s(o_e);gcr=r(tgt,"funnel"),tgt.forEach(t),hcr=r(Q0e," \u2014 "),dG=n(Q0e,"A",{href:!0});var agt=s(dG);pcr=r(agt,"TFFunnelForSequenceClassification"),agt.forEach(t),_cr=r(Q0e," (Funnel Transformer model)"),Q0e.forEach(t),ucr=i(W),b8=n(W,"LI",{});var H0e=s(b8);r_e=n(H0e,"STRONG",{});var ngt=s(r_e);bcr=r(ngt,"gpt2"),ngt.forEach(t),vcr=r(H0e," \u2014 "),cG=n(H0e,"A",{href:!0});var sgt=s(cG);Tcr=r(sgt,"TFGPT2ForSequenceClassification"),sgt.forEach(t),Fcr=r(H0e," (OpenAI GPT-2 model)"),H0e.forEach(t),Ccr=i(W),v8=n(W,"LI",{});var U0e=s(v8);t_e=n(U0e,"STRONG",{});var lgt=s(t_e);Mcr=r(lgt,"layoutlm"),lgt.forEach(t),Ecr=r(U0e," \u2014 "),fG=n(U0e,"A",{href:!0});var igt=s(fG);ycr=r(igt,"TFLayoutLMForSequenceClassification"),igt.forEach(t),wcr=r(U0e," (LayoutLM model)"),U0e.forEach(t),Acr=i(W),T8=n(W,"LI",{});var J0e=s(T8);a_e=n(J0e,"STRONG",{});var dgt=s(a_e);Lcr=r(dgt,"longformer"),dgt.forEach(t),Bcr=r(J0e," \u2014 "),mG=n(J0e,"A",{href:!0});var cgt=s(mG);xcr=r(cgt,"TFLongformerForSequenceClassification"),cgt.forEach(t),kcr=r(J0e," (Longformer model)"),J0e.forEach(t),Rcr=i(W),F8=n(W,"LI",{});var Y0e=s(F8);n_e=n(Y0e,"STRONG",{});var fgt=s(n_e);Scr=r(fgt,"mobilebert"),fgt.forEach(t),Pcr=r(Y0e," \u2014 "),gG=n(Y0e,"A",{href:!0});var mgt=s(gG);$cr=r(mgt,"TFMobileBertForSequenceClassification"),mgt.forEach(t),Icr=r(Y0e," (MobileBERT model)"),Y0e.forEach(t),jcr=i(W),C8=n(W,"LI",{});var K0e=s(C8);s_e=n(K0e,"STRONG",{});var ggt=s(s_e);Dcr=r(ggt,"mpnet"),ggt.forEach(t),Ncr=r(K0e," \u2014 "),hG=n(K0e,"A",{href:!0});var hgt=s(hG);qcr=r(hgt,"TFMPNetForSequenceClassification"),hgt.forEach(t),Ocr=r(K0e," (MPNet model)"),K0e.forEach(t),Gcr=i(W),M8=n(W,"LI",{});var Z0e=s(M8);l_e=n(Z0e,"STRONG",{});var pgt=s(l_e);Xcr=r(pgt,"openai-gpt"),pgt.forEach(t),Vcr=r(Z0e," \u2014 "),pG=n(Z0e,"A",{href:!0});var _gt=s(pG);zcr=r(_gt,"TFOpenAIGPTForSequenceClassification"),_gt.forEach(t),Wcr=r(Z0e," (OpenAI GPT model)"),Z0e.forEach(t),Qcr=i(W),E8=n(W,"LI",{});var eLe=s(E8);i_e=n(eLe,"STRONG",{});var ugt=s(i_e);Hcr=r(ugt,"rembert"),ugt.forEach(t),Ucr=r(eLe," \u2014 "),_G=n(eLe,"A",{href:!0});var bgt=s(_G);Jcr=r(bgt,"TFRemBertForSequenceClassification"),bgt.forEach(t),Ycr=r(eLe," (RemBERT model)"),eLe.forEach(t),Kcr=i(W),y8=n(W,"LI",{});var oLe=s(y8);d_e=n(oLe,"STRONG",{});var vgt=s(d_e);Zcr=r(vgt,"roberta"),vgt.forEach(t),efr=r(oLe," \u2014 "),uG=n(oLe,"A",{href:!0});var Tgt=s(uG);ofr=r(Tgt,"TFRobertaForSequenceClassification"),Tgt.forEach(t),rfr=r(oLe," (RoBERTa model)"),oLe.forEach(t),tfr=i(W),w8=n(W,"LI",{});var rLe=s(w8);c_e=n(rLe,"STRONG",{});var Fgt=s(c_e);afr=r(Fgt,"roformer"),Fgt.forEach(t),nfr=r(rLe," \u2014 "),bG=n(rLe,"A",{href:!0});var Cgt=s(bG);sfr=r(Cgt,"TFRoFormerForSequenceClassification"),Cgt.forEach(t),lfr=r(rLe," (RoFormer model)"),rLe.forEach(t),ifr=i(W),A8=n(W,"LI",{});var tLe=s(A8);f_e=n(tLe,"STRONG",{});var Mgt=s(f_e);dfr=r(Mgt,"tapas"),Mgt.forEach(t),cfr=r(tLe," \u2014 "),vG=n(tLe,"A",{href:!0});var Egt=s(vG);ffr=r(Egt,"TFTapasForSequenceClassification"),Egt.forEach(t),mfr=r(tLe," (TAPAS model)"),tLe.forEach(t),gfr=i(W),L8=n(W,"LI",{});var aLe=s(L8);m_e=n(aLe,"STRONG",{});var ygt=s(m_e);hfr=r(ygt,"transfo-xl"),ygt.forEach(t),pfr=r(aLe," \u2014 "),TG=n(aLe,"A",{href:!0});var wgt=s(TG);_fr=r(wgt,"TFTransfoXLForSequenceClassification"),wgt.forEach(t),ufr=r(aLe," (Transformer-XL model)"),aLe.forEach(t),bfr=i(W),B8=n(W,"LI",{});var nLe=s(B8);g_e=n(nLe,"STRONG",{});var Agt=s(g_e);vfr=r(Agt,"xlm"),Agt.forEach(t),Tfr=r(nLe," \u2014 "),FG=n(nLe,"A",{href:!0});var Lgt=s(FG);Ffr=r(Lgt,"TFXLMForSequenceClassification"),Lgt.forEach(t),Cfr=r(nLe," (XLM model)"),nLe.forEach(t),Mfr=i(W),x8=n(W,"LI",{});var sLe=s(x8);h_e=n(sLe,"STRONG",{});var Bgt=s(h_e);Efr=r(Bgt,"xlm-roberta"),Bgt.forEach(t),yfr=r(sLe," \u2014 "),CG=n(sLe,"A",{href:!0});var xgt=s(CG);wfr=r(xgt,"TFXLMRobertaForSequenceClassification"),xgt.forEach(t),Afr=r(sLe," (XLM-RoBERTa model)"),sLe.forEach(t),Lfr=i(W),k8=n(W,"LI",{});var lLe=s(k8);p_e=n(lLe,"STRONG",{});var kgt=s(p_e);Bfr=r(kgt,"xlnet"),kgt.forEach(t),xfr=r(lLe," \u2014 "),MG=n(lLe,"A",{href:!0});var Rgt=s(MG);kfr=r(Rgt,"TFXLNetForSequenceClassification"),Rgt.forEach(t),Rfr=r(lLe," (XLNet model)"),lLe.forEach(t),W.forEach(t),Sfr=i(Fa),__e=n(Fa,"P",{});var Sgt=s(__e);Pfr=r(Sgt,"Examples:"),Sgt.forEach(t),$fr=i(Fa),m(M6.$$.fragment,Fa),Fa.forEach(t),Yl.forEach(t),yke=i(d),Pc=n(d,"H2",{class:!0});var jSe=s(Pc);R8=n(jSe,"A",{id:!0,class:!0,href:!0});var Pgt=s(R8);u_e=n(Pgt,"SPAN",{});var $gt=s(u_e);m(E6.$$.fragment,$gt),$gt.forEach(t),Pgt.forEach(t),Ifr=i(jSe),b_e=n(jSe,"SPAN",{});var Igt=s(b_e);jfr=r(Igt,"TFAutoModelForMultipleChoice"),Igt.forEach(t),jSe.forEach(t),wke=i(d),wr=n(d,"DIV",{class:!0});var Zl=s(wr);m(y6.$$.fragment,Zl),Dfr=i(Zl),$c=n(Zl,"P",{});var RW=s($c);Nfr=r(RW,`This is a generic model class that will be instantiated as one of the model classes of the library (with a multiple choice head) when created
with the `),v_e=n(RW,"CODE",{});var jgt=s(v_e);qfr=r(jgt,"from_pretrained()"),jgt.forEach(t),Ofr=r(RW,"class method or the "),T_e=n(RW,"CODE",{});var Dgt=s(T_e);Gfr=r(Dgt,"from_config()"),Dgt.forEach(t),Xfr=r(RW,`class
method.`),RW.forEach(t),Vfr=i(Zl),w6=n(Zl,"P",{});var DSe=s(w6);zfr=r(DSe,"This class cannot be instantiated directly using "),F_e=n(DSe,"CODE",{});var Ngt=s(F_e);Wfr=r(Ngt,"__init__()"),Ngt.forEach(t),Qfr=r(DSe," (throws an error)."),DSe.forEach(t),Hfr=i(Zl),Tt=n(Zl,"DIV",{class:!0});var ei=s(Tt);m(A6.$$.fragment,ei),Ufr=i(ei),C_e=n(ei,"P",{});var qgt=s(C_e);Jfr=r(qgt,"Instantiates one of the model classes of the library (with a multiple choice head) from a configuration."),qgt.forEach(t),Yfr=i(ei),Ic=n(ei,"P",{});var SW=s(Ic);Kfr=r(SW,`Note:
Loading a model from its configuration file does `),M_e=n(SW,"STRONG",{});var Ogt=s(M_e);Zfr=r(Ogt,"not"),Ogt.forEach(t),emr=r(SW,` load the model weights. It only affects the
model\u2019s configuration. Use `),E_e=n(SW,"CODE",{});var Ggt=s(E_e);omr=r(Ggt,"from_pretrained()"),Ggt.forEach(t),rmr=r(SW,"to load the model weights."),SW.forEach(t),tmr=i(ei),y_e=n(ei,"P",{});var Xgt=s(y_e);amr=r(Xgt,"Examples:"),Xgt.forEach(t),nmr=i(ei),m(L6.$$.fragment,ei),ei.forEach(t),smr=i(Zl),Fo=n(Zl,"DIV",{class:!0});var Ca=s(Fo);m(B6.$$.fragment,Ca),lmr=i(Ca),w_e=n(Ca,"P",{});var Vgt=s(w_e);imr=r(Vgt,"Instantiate one of the model classes of the library (with a multiple choice head) from a pretrained model."),Vgt.forEach(t),dmr=i(Ca),Mn=n(Ca,"P",{});var BM=s(Mn);cmr=r(BM,"The model class to instantiate is selected based on the "),A_e=n(BM,"CODE",{});var zgt=s(A_e);fmr=r(zgt,"model_type"),zgt.forEach(t),mmr=r(BM,` property of the config object (either
passed as an argument or loaded from `),L_e=n(BM,"CODE",{});var Wgt=s(L_e);gmr=r(Wgt,"pretrained_model_name_or_path"),Wgt.forEach(t),hmr=r(BM,` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),B_e=n(BM,"CODE",{});var Qgt=s(B_e);pmr=r(Qgt,"pretrained_model_name_or_path"),Qgt.forEach(t),_mr=r(BM,":"),BM.forEach(t),umr=i(Ca),te=n(Ca,"UL",{});var se=s(te);S8=n(se,"LI",{});var iLe=s(S8);x_e=n(iLe,"STRONG",{});var Hgt=s(x_e);bmr=r(Hgt,"albert"),Hgt.forEach(t),vmr=r(iLe," \u2014 "),EG=n(iLe,"A",{href:!0});var Ugt=s(EG);Tmr=r(Ugt,"TFAlbertForMultipleChoice"),Ugt.forEach(t),Fmr=r(iLe," (ALBERT model)"),iLe.forEach(t),Cmr=i(se),P8=n(se,"LI",{});var dLe=s(P8);k_e=n(dLe,"STRONG",{});var Jgt=s(k_e);Mmr=r(Jgt,"bert"),Jgt.forEach(t),Emr=r(dLe," \u2014 "),yG=n(dLe,"A",{href:!0});var Ygt=s(yG);ymr=r(Ygt,"TFBertForMultipleChoice"),Ygt.forEach(t),wmr=r(dLe," (BERT model)"),dLe.forEach(t),Amr=i(se),$8=n(se,"LI",{});var cLe=s($8);R_e=n(cLe,"STRONG",{});var Kgt=s(R_e);Lmr=r(Kgt,"camembert"),Kgt.forEach(t),Bmr=r(cLe," \u2014 "),wG=n(cLe,"A",{href:!0});var Zgt=s(wG);xmr=r(Zgt,"TFCamembertForMultipleChoice"),Zgt.forEach(t),kmr=r(cLe," (CamemBERT model)"),cLe.forEach(t),Rmr=i(se),I8=n(se,"LI",{});var fLe=s(I8);S_e=n(fLe,"STRONG",{});var eht=s(S_e);Smr=r(eht,"convbert"),eht.forEach(t),Pmr=r(fLe," \u2014 "),AG=n(fLe,"A",{href:!0});var oht=s(AG);$mr=r(oht,"TFConvBertForMultipleChoice"),oht.forEach(t),Imr=r(fLe," (ConvBERT model)"),fLe.forEach(t),jmr=i(se),j8=n(se,"LI",{});var mLe=s(j8);P_e=n(mLe,"STRONG",{});var rht=s(P_e);Dmr=r(rht,"distilbert"),rht.forEach(t),Nmr=r(mLe," \u2014 "),LG=n(mLe,"A",{href:!0});var tht=s(LG);qmr=r(tht,"TFDistilBertForMultipleChoice"),tht.forEach(t),Omr=r(mLe," (DistilBERT model)"),mLe.forEach(t),Gmr=i(se),D8=n(se,"LI",{});var gLe=s(D8);$_e=n(gLe,"STRONG",{});var aht=s($_e);Xmr=r(aht,"electra"),aht.forEach(t),Vmr=r(gLe," \u2014 "),BG=n(gLe,"A",{href:!0});var nht=s(BG);zmr=r(nht,"TFElectraForMultipleChoice"),nht.forEach(t),Wmr=r(gLe," (ELECTRA model)"),gLe.forEach(t),Qmr=i(se),N8=n(se,"LI",{});var hLe=s(N8);I_e=n(hLe,"STRONG",{});var sht=s(I_e);Hmr=r(sht,"flaubert"),sht.forEach(t),Umr=r(hLe," \u2014 "),xG=n(hLe,"A",{href:!0});var lht=s(xG);Jmr=r(lht,"TFFlaubertForMultipleChoice"),lht.forEach(t),Ymr=r(hLe," (FlauBERT model)"),hLe.forEach(t),Kmr=i(se),q8=n(se,"LI",{});var pLe=s(q8);j_e=n(pLe,"STRONG",{});var iht=s(j_e);Zmr=r(iht,"funnel"),iht.forEach(t),egr=r(pLe," \u2014 "),kG=n(pLe,"A",{href:!0});var dht=s(kG);ogr=r(dht,"TFFunnelForMultipleChoice"),dht.forEach(t),rgr=r(pLe," (Funnel Transformer model)"),pLe.forEach(t),tgr=i(se),O8=n(se,"LI",{});var _Le=s(O8);D_e=n(_Le,"STRONG",{});var cht=s(D_e);agr=r(cht,"longformer"),cht.forEach(t),ngr=r(_Le," \u2014 "),RG=n(_Le,"A",{href:!0});var fht=s(RG);sgr=r(fht,"TFLongformerForMultipleChoice"),fht.forEach(t),lgr=r(_Le," (Longformer model)"),_Le.forEach(t),igr=i(se),G8=n(se,"LI",{});var uLe=s(G8);N_e=n(uLe,"STRONG",{});var mht=s(N_e);dgr=r(mht,"mobilebert"),mht.forEach(t),cgr=r(uLe," \u2014 "),SG=n(uLe,"A",{href:!0});var ght=s(SG);fgr=r(ght,"TFMobileBertForMultipleChoice"),ght.forEach(t),mgr=r(uLe," (MobileBERT model)"),uLe.forEach(t),ggr=i(se),X8=n(se,"LI",{});var bLe=s(X8);q_e=n(bLe,"STRONG",{});var hht=s(q_e);hgr=r(hht,"mpnet"),hht.forEach(t),pgr=r(bLe," \u2014 "),PG=n(bLe,"A",{href:!0});var pht=s(PG);_gr=r(pht,"TFMPNetForMultipleChoice"),pht.forEach(t),ugr=r(bLe," (MPNet model)"),bLe.forEach(t),bgr=i(se),V8=n(se,"LI",{});var vLe=s(V8);O_e=n(vLe,"STRONG",{});var _ht=s(O_e);vgr=r(_ht,"rembert"),_ht.forEach(t),Tgr=r(vLe," \u2014 "),$G=n(vLe,"A",{href:!0});var uht=s($G);Fgr=r(uht,"TFRemBertForMultipleChoice"),uht.forEach(t),Cgr=r(vLe," (RemBERT model)"),vLe.forEach(t),Mgr=i(se),z8=n(se,"LI",{});var TLe=s(z8);G_e=n(TLe,"STRONG",{});var bht=s(G_e);Egr=r(bht,"roberta"),bht.forEach(t),ygr=r(TLe," \u2014 "),IG=n(TLe,"A",{href:!0});var vht=s(IG);wgr=r(vht,"TFRobertaForMultipleChoice"),vht.forEach(t),Agr=r(TLe," (RoBERTa model)"),TLe.forEach(t),Lgr=i(se),W8=n(se,"LI",{});var FLe=s(W8);X_e=n(FLe,"STRONG",{});var Tht=s(X_e);Bgr=r(Tht,"roformer"),Tht.forEach(t),xgr=r(FLe," \u2014 "),jG=n(FLe,"A",{href:!0});var Fht=s(jG);kgr=r(Fht,"TFRoFormerForMultipleChoice"),Fht.forEach(t),Rgr=r(FLe," (RoFormer model)"),FLe.forEach(t),Sgr=i(se),Q8=n(se,"LI",{});var CLe=s(Q8);V_e=n(CLe,"STRONG",{});var Cht=s(V_e);Pgr=r(Cht,"xlm"),Cht.forEach(t),$gr=r(CLe," \u2014 "),DG=n(CLe,"A",{href:!0});var Mht=s(DG);Igr=r(Mht,"TFXLMForMultipleChoice"),Mht.forEach(t),jgr=r(CLe," (XLM model)"),CLe.forEach(t),Dgr=i(se),H8=n(se,"LI",{});var MLe=s(H8);z_e=n(MLe,"STRONG",{});var Eht=s(z_e);Ngr=r(Eht,"xlm-roberta"),Eht.forEach(t),qgr=r(MLe," \u2014 "),NG=n(MLe,"A",{href:!0});var yht=s(NG);Ogr=r(yht,"TFXLMRobertaForMultipleChoice"),yht.forEach(t),Ggr=r(MLe," (XLM-RoBERTa model)"),MLe.forEach(t),Xgr=i(se),U8=n(se,"LI",{});var ELe=s(U8);W_e=n(ELe,"STRONG",{});var wht=s(W_e);Vgr=r(wht,"xlnet"),wht.forEach(t),zgr=r(ELe," \u2014 "),qG=n(ELe,"A",{href:!0});var Aht=s(qG);Wgr=r(Aht,"TFXLNetForMultipleChoice"),Aht.forEach(t),Qgr=r(ELe," (XLNet model)"),ELe.forEach(t),se.forEach(t),Hgr=i(Ca),Q_e=n(Ca,"P",{});var Lht=s(Q_e);Ugr=r(Lht,"Examples:"),Lht.forEach(t),Jgr=i(Ca),m(x6.$$.fragment,Ca),Ca.forEach(t),Zl.forEach(t),Ake=i(d),jc=n(d,"H2",{class:!0});var NSe=s(jc);J8=n(NSe,"A",{id:!0,class:!0,href:!0});var Bht=s(J8);H_e=n(Bht,"SPAN",{});var xht=s(H_e);m(k6.$$.fragment,xht),xht.forEach(t),Bht.forEach(t),Ygr=i(NSe),U_e=n(NSe,"SPAN",{});var kht=s(U_e);Kgr=r(kht,"TFAutoModelForTableQuestionAnswering"),kht.forEach(t),NSe.forEach(t),Lke=i(d),Ar=n(d,"DIV",{class:!0});var oi=s(Ar);m(R6.$$.fragment,oi),Zgr=i(oi),Dc=n(oi,"P",{});var PW=s(Dc);ehr=r(PW,`This is a generic model class that will be instantiated as one of the model classes of the library (with a table question answering head) when created
with the `),J_e=n(PW,"CODE",{});var Rht=s(J_e);ohr=r(Rht,"from_pretrained()"),Rht.forEach(t),rhr=r(PW,"class method or the "),Y_e=n(PW,"CODE",{});var Sht=s(Y_e);thr=r(Sht,"from_config()"),Sht.forEach(t),ahr=r(PW,`class
method.`),PW.forEach(t),nhr=i(oi),S6=n(oi,"P",{});var qSe=s(S6);shr=r(qSe,"This class cannot be instantiated directly using "),K_e=n(qSe,"CODE",{});var Pht=s(K_e);lhr=r(Pht,"__init__()"),Pht.forEach(t),ihr=r(qSe," (throws an error)."),qSe.forEach(t),dhr=i(oi),Ft=n(oi,"DIV",{class:!0});var ri=s(Ft);m(P6.$$.fragment,ri),chr=i(ri),Z_e=n(ri,"P",{});var $ht=s(Z_e);fhr=r($ht,"Instantiates one of the model classes of the library (with a table question answering head) from a configuration."),$ht.forEach(t),mhr=i(ri),Nc=n(ri,"P",{});var $W=s(Nc);ghr=r($W,`Note:
Loading a model from its configuration file does `),eue=n($W,"STRONG",{});var Iht=s(eue);hhr=r(Iht,"not"),Iht.forEach(t),phr=r($W,` load the model weights. It only affects the
model\u2019s configuration. Use `),oue=n($W,"CODE",{});var jht=s(oue);_hr=r(jht,"from_pretrained()"),jht.forEach(t),uhr=r($W,"to load the model weights."),$W.forEach(t),bhr=i(ri),rue=n(ri,"P",{});var Dht=s(rue);vhr=r(Dht,"Examples:"),Dht.forEach(t),Thr=i(ri),m($6.$$.fragment,ri),ri.forEach(t),Fhr=i(oi),Co=n(oi,"DIV",{class:!0});var Ma=s(Co);m(I6.$$.fragment,Ma),Chr=i(Ma),tue=n(Ma,"P",{});var Nht=s(tue);Mhr=r(Nht,"Instantiate one of the model classes of the library (with a table question answering head) from a pretrained model."),Nht.forEach(t),Ehr=i(Ma),En=n(Ma,"P",{});var xM=s(En);yhr=r(xM,"The model class to instantiate is selected based on the "),aue=n(xM,"CODE",{});var qht=s(aue);whr=r(qht,"model_type"),qht.forEach(t),Ahr=r(xM,` property of the config object (either
passed as an argument or loaded from `),nue=n(xM,"CODE",{});var Oht=s(nue);Lhr=r(Oht,"pretrained_model_name_or_path"),Oht.forEach(t),Bhr=r(xM,` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),sue=n(xM,"CODE",{});var Ght=s(sue);xhr=r(Ght,"pretrained_model_name_or_path"),Ght.forEach(t),khr=r(xM,":"),xM.forEach(t),Rhr=i(Ma),lue=n(Ma,"UL",{});var Xht=s(lue);Y8=n(Xht,"LI",{});var yLe=s(Y8);iue=n(yLe,"STRONG",{});var Vht=s(iue);Shr=r(Vht,"tapas"),Vht.forEach(t),Phr=r(yLe," \u2014 "),OG=n(yLe,"A",{href:!0});var zht=s(OG);$hr=r(zht,"TFTapasForQuestionAnswering"),zht.forEach(t),Ihr=r(yLe," (TAPAS model)"),yLe.forEach(t),Xht.forEach(t),jhr=i(Ma),due=n(Ma,"P",{});var Wht=s(due);Dhr=r(Wht,"Examples:"),Wht.forEach(t),Nhr=i(Ma),m(j6.$$.fragment,Ma),Ma.forEach(t),oi.forEach(t),Bke=i(d),qc=n(d,"H2",{class:!0});var OSe=s(qc);K8=n(OSe,"A",{id:!0,class:!0,href:!0});var Qht=s(K8);cue=n(Qht,"SPAN",{});var Hht=s(cue);m(D6.$$.fragment,Hht),Hht.forEach(t),Qht.forEach(t),qhr=i(OSe),fue=n(OSe,"SPAN",{});var Uht=s(fue);Ohr=r(Uht,"TFAutoModelForTokenClassification"),Uht.forEach(t),OSe.forEach(t),xke=i(d),Lr=n(d,"DIV",{class:!0});var ti=s(Lr);m(N6.$$.fragment,ti),Ghr=i(ti),Oc=n(ti,"P",{});var IW=s(Oc);Xhr=r(IW,`This is a generic model class that will be instantiated as one of the model classes of the library (with a token classification head) when created
with the `),mue=n(IW,"CODE",{});var Jht=s(mue);Vhr=r(Jht,"from_pretrained()"),Jht.forEach(t),zhr=r(IW,"class method or the "),gue=n(IW,"CODE",{});var Yht=s(gue);Whr=r(Yht,"from_config()"),Yht.forEach(t),Qhr=r(IW,`class
method.`),IW.forEach(t),Hhr=i(ti),q6=n(ti,"P",{});var GSe=s(q6);Uhr=r(GSe,"This class cannot be instantiated directly using "),hue=n(GSe,"CODE",{});var Kht=s(hue);Jhr=r(Kht,"__init__()"),Kht.forEach(t),Yhr=r(GSe," (throws an error)."),GSe.forEach(t),Khr=i(ti),Ct=n(ti,"DIV",{class:!0});var ai=s(Ct);m(O6.$$.fragment,ai),Zhr=i(ai),pue=n(ai,"P",{});var Zht=s(pue);epr=r(Zht,"Instantiates one of the model classes of the library (with a token classification head) from a configuration."),Zht.forEach(t),opr=i(ai),Gc=n(ai,"P",{});var jW=s(Gc);rpr=r(jW,`Note:
Loading a model from its configuration file does `),_ue=n(jW,"STRONG",{});var ept=s(_ue);tpr=r(ept,"not"),ept.forEach(t),apr=r(jW,` load the model weights. It only affects the
model\u2019s configuration. Use `),uue=n(jW,"CODE",{});var opt=s(uue);npr=r(opt,"from_pretrained()"),opt.forEach(t),spr=r(jW,"to load the model weights."),jW.forEach(t),lpr=i(ai),bue=n(ai,"P",{});var rpt=s(bue);ipr=r(rpt,"Examples:"),rpt.forEach(t),dpr=i(ai),m(G6.$$.fragment,ai),ai.forEach(t),cpr=i(ti),Mo=n(ti,"DIV",{class:!0});var Ea=s(Mo);m(X6.$$.fragment,Ea),fpr=i(Ea),vue=n(Ea,"P",{});var tpt=s(vue);mpr=r(tpt,"Instantiate one of the model classes of the library (with a token classification head) from a pretrained model."),tpt.forEach(t),gpr=i(Ea),yn=n(Ea,"P",{});var kM=s(yn);hpr=r(kM,"The model class to instantiate is selected based on the "),Tue=n(kM,"CODE",{});var apt=s(Tue);ppr=r(apt,"model_type"),apt.forEach(t),_pr=r(kM,` property of the config object (either
passed as an argument or loaded from `),Fue=n(kM,"CODE",{});var npt=s(Fue);upr=r(npt,"pretrained_model_name_or_path"),npt.forEach(t),bpr=r(kM,` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),Cue=n(kM,"CODE",{});var spt=s(Cue);vpr=r(spt,"pretrained_model_name_or_path"),spt.forEach(t),Tpr=r(kM,":"),kM.forEach(t),Fpr=i(Ea),K=n(Ea,"UL",{});var oe=s(K);Z8=n(oe,"LI",{});var wLe=s(Z8);Mue=n(wLe,"STRONG",{});var lpt=s(Mue);Cpr=r(lpt,"albert"),lpt.forEach(t),Mpr=r(wLe," \u2014 "),GG=n(wLe,"A",{href:!0});var ipt=s(GG);Epr=r(ipt,"TFAlbertForTokenClassification"),ipt.forEach(t),ypr=r(wLe," (ALBERT model)"),wLe.forEach(t),wpr=i(oe),eF=n(oe,"LI",{});var ALe=s(eF);Eue=n(ALe,"STRONG",{});var dpt=s(Eue);Apr=r(dpt,"bert"),dpt.forEach(t),Lpr=r(ALe," \u2014 "),XG=n(ALe,"A",{href:!0});var cpt=s(XG);Bpr=r(cpt,"TFBertForTokenClassification"),cpt.forEach(t),xpr=r(ALe," (BERT model)"),ALe.forEach(t),kpr=i(oe),oF=n(oe,"LI",{});var LLe=s(oF);yue=n(LLe,"STRONG",{});var fpt=s(yue);Rpr=r(fpt,"camembert"),fpt.forEach(t),Spr=r(LLe," \u2014 "),VG=n(LLe,"A",{href:!0});var mpt=s(VG);Ppr=r(mpt,"TFCamembertForTokenClassification"),mpt.forEach(t),$pr=r(LLe," (CamemBERT model)"),LLe.forEach(t),Ipr=i(oe),rF=n(oe,"LI",{});var BLe=s(rF);wue=n(BLe,"STRONG",{});var gpt=s(wue);jpr=r(gpt,"convbert"),gpt.forEach(t),Dpr=r(BLe," \u2014 "),zG=n(BLe,"A",{href:!0});var hpt=s(zG);Npr=r(hpt,"TFConvBertForTokenClassification"),hpt.forEach(t),qpr=r(BLe," (ConvBERT model)"),BLe.forEach(t),Opr=i(oe),tF=n(oe,"LI",{});var xLe=s(tF);Aue=n(xLe,"STRONG",{});var ppt=s(Aue);Gpr=r(ppt,"deberta"),ppt.forEach(t),Xpr=r(xLe," \u2014 "),WG=n(xLe,"A",{href:!0});var _pt=s(WG);Vpr=r(_pt,"TFDebertaForTokenClassification"),_pt.forEach(t),zpr=r(xLe," (DeBERTa model)"),xLe.forEach(t),Wpr=i(oe),aF=n(oe,"LI",{});var kLe=s(aF);Lue=n(kLe,"STRONG",{});var upt=s(Lue);Qpr=r(upt,"deberta-v2"),upt.forEach(t),Hpr=r(kLe," \u2014 "),QG=n(kLe,"A",{href:!0});var bpt=s(QG);Upr=r(bpt,"TFDebertaV2ForTokenClassification"),bpt.forEach(t),Jpr=r(kLe," (DeBERTa-v2 model)"),kLe.forEach(t),Ypr=i(oe),nF=n(oe,"LI",{});var RLe=s(nF);Bue=n(RLe,"STRONG",{});var vpt=s(Bue);Kpr=r(vpt,"distilbert"),vpt.forEach(t),Zpr=r(RLe," \u2014 "),HG=n(RLe,"A",{href:!0});var Tpt=s(HG);e_r=r(Tpt,"TFDistilBertForTokenClassification"),Tpt.forEach(t),o_r=r(RLe," (DistilBERT model)"),RLe.forEach(t),r_r=i(oe),sF=n(oe,"LI",{});var SLe=s(sF);xue=n(SLe,"STRONG",{});var Fpt=s(xue);t_r=r(Fpt,"electra"),Fpt.forEach(t),a_r=r(SLe," \u2014 "),UG=n(SLe,"A",{href:!0});var Cpt=s(UG);n_r=r(Cpt,"TFElectraForTokenClassification"),Cpt.forEach(t),s_r=r(SLe," (ELECTRA model)"),SLe.forEach(t),l_r=i(oe),lF=n(oe,"LI",{});var PLe=s(lF);kue=n(PLe,"STRONG",{});var Mpt=s(kue);i_r=r(Mpt,"flaubert"),Mpt.forEach(t),d_r=r(PLe," \u2014 "),JG=n(PLe,"A",{href:!0});var Ept=s(JG);c_r=r(Ept,"TFFlaubertForTokenClassification"),Ept.forEach(t),f_r=r(PLe," (FlauBERT model)"),PLe.forEach(t),m_r=i(oe),iF=n(oe,"LI",{});var $Le=s(iF);Rue=n($Le,"STRONG",{});var ypt=s(Rue);g_r=r(ypt,"funnel"),ypt.forEach(t),h_r=r($Le," \u2014 "),YG=n($Le,"A",{href:!0});var 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falling back to using pattern matching on `),r1e=n(RM,"CODE",{});var l_t=s(r1e);Uur=r(l_t,"pretrained_model_name_or_path"),l_t.forEach(t),Jur=r(RM,":"),RM.forEach(t),Yur=i(ya),Z=n(ya,"UL",{});var re=s(Z);TF=n(re,"LI",{});var WLe=s(TF);t1e=n(WLe,"STRONG",{});var i_t=s(t1e);Kur=r(i_t,"albert"),i_t.forEach(t),Zur=r(WLe," \u2014 "),iX=n(WLe,"A",{href:!0});var d_t=s(iX);e1r=r(d_t,"TFAlbertForQuestionAnswering"),d_t.forEach(t),o1r=r(WLe," (ALBERT model)"),WLe.forEach(t),r1r=i(re),FF=n(re,"LI",{});var QLe=s(FF);a1e=n(QLe,"STRONG",{});var c_t=s(a1e);t1r=r(c_t,"bert"),c_t.forEach(t),a1r=r(QLe," \u2014 "),dX=n(QLe,"A",{href:!0});var f_t=s(dX);n1r=r(f_t,"TFBertForQuestionAnswering"),f_t.forEach(t),s1r=r(QLe," (BERT model)"),QLe.forEach(t),l1r=i(re),CF=n(re,"LI",{});var HLe=s(CF);n1e=n(HLe,"STRONG",{});var m_t=s(n1e);i1r=r(m_t,"camembert"),m_t.forEach(t),d1r=r(HLe," \u2014 "),cX=n(HLe,"A",{href:!0});var g_t=s(cX);c1r=r(g_t,"TFCamembertForQuestionAnswering"),g_t.forEach(t),f1r=r(HLe," (CamemBERT 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method.`),qW.forEach(t),Ubr=i(li),eA=n(li,"P",{});var WSe=s(eA);Jbr=r(WSe,"This class cannot be instantiated directly using "),A1e=n(WSe,"CODE",{});var K_t=s(A1e);Ybr=r(K_t,"__init__()"),K_t.forEach(t),Kbr=r(WSe," (throws an error)."),WSe.forEach(t),Zbr=i(li),Et=n(li,"DIV",{class:!0});var ii=s(Et);m(oA.$$.fragment,ii),e3r=i(ii),L1e=n(ii,"P",{});var Z_t=s(L1e);o3r=r(Z_t,"Instantiates one of the model classes of the library (with a vision-to-text modeling head) from a configuration."),Z_t.forEach(t),r3r=i(ii),Hc=n(ii,"P",{});var OW=s(Hc);t3r=r(OW,`Note:
Loading a model from its configuration file does `),B1e=n(OW,"STRONG",{});var eut=s(B1e);a3r=r(eut,"not"),eut.forEach(t),n3r=r(OW,` load the model weights. It only affects the
model\u2019s configuration. Use `),x1e=n(OW,"CODE",{});var out=s(x1e);s3r=r(out,"from_pretrained()"),out.forEach(t),l3r=r(OW,"to load the model weights."),OW.forEach(t),i3r=i(ii),k1e=n(ii,"P",{});var rut=s(k1e);d3r=r(rut,"Examples:"),rut.forEach(t),c3r=i(ii),m(rA.$$.fragment,ii),ii.forEach(t),f3r=i(li),yo=n(li,"DIV",{class:!0});var wa=s(yo);m(tA.$$.fragment,wa),m3r=i(wa),R1e=n(wa,"P",{});var tut=s(R1e);g3r=r(tut,"Instantiate one of the model classes of the library (with a vision-to-text modeling head) from a pretrained model."),tut.forEach(t),h3r=i(wa),An=n(wa,"P",{});var SM=s(An);p3r=r(SM,"The model class to instantiate is selected based on the "),S1e=n(SM,"CODE",{});var aut=s(S1e);_3r=r(aut,"model_type"),aut.forEach(t),u3r=r(SM,` property of the config object (either
passed as an argument or loaded from `),P1e=n(SM,"CODE",{});var nut=s(P1e);b3r=r(nut,"pretrained_model_name_or_path"),nut.forEach(t),v3r=r(SM,` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),$1e=n(SM,"CODE",{});var sut=s($1e);T3r=r(sut,"pretrained_model_name_or_path"),sut.forEach(t),F3r=r(SM,":"),SM.forEach(t),C3r=i(wa),I1e=n(wa,"UL",{});var lut=s(I1e);qF=n(lut,"LI",{});var f7e=s(qF);j1e=n(f7e,"STRONG",{});var iut=s(j1e);M3r=r(iut,"vision-encoder-decoder"),iut.forEach(t),E3r=r(f7e," \u2014 "),AX=n(f7e,"A",{href:!0});var dut=s(AX);y3r=r(dut,"TFVisionEncoderDecoderModel"),dut.forEach(t),w3r=r(f7e," (Vision Encoder decoder model)"),f7e.forEach(t),lut.forEach(t),A3r=i(wa),D1e=n(wa,"P",{});var cut=s(D1e);L3r=r(cut,"Examples:"),cut.forEach(t),B3r=i(wa),m(aA.$$.fragment,wa),wa.forEach(t),li.forEach(t),$ke=i(d),Uc=n(d,"H2",{class:!0});var QSe=s(Uc);OF=n(QSe,"A",{id:!0,class:!0,href:!0});var fut=s(OF);N1e=n(fut,"SPAN",{});var mut=s(N1e);m(nA.$$.fragment,mut),mut.forEach(t),fut.forEach(t),x3r=i(QSe),q1e=n(QSe,"SPAN",{});var gut=s(q1e);k3r=r(gut,"TFAutoModelForSpeechSeq2Seq"),gut.forEach(t),QSe.forEach(t),Ike=i(d),kr=n(d,"DIV",{class:!0});var di=s(kr);m(sA.$$.fragment,di),R3r=i(di),Jc=n(di,"P",{});var GW=s(Jc);S3r=r(GW,`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 `),O1e=n(GW,"CODE",{});var hut=s(O1e);P3r=r(hut,"from_pretrained()"),hut.forEach(t),$3r=r(GW,"class method or the "),G1e=n(GW,"CODE",{});var put=s(G1e);I3r=r(put,"from_config()"),put.forEach(t),j3r=r(GW,`class
method.`),GW.forEach(t),D3r=i(di),lA=n(di,"P",{});var HSe=s(lA);N3r=r(HSe,"This class cannot be instantiated directly using "),X1e=n(HSe,"CODE",{});var _ut=s(X1e);q3r=r(_ut,"__init__()"),_ut.forEach(t),O3r=r(HSe," (throws an error)."),HSe.forEach(t),G3r=i(di),yt=n(di,"DIV",{class:!0});var ci=s(yt);m(iA.$$.fragment,ci),X3r=i(ci),V1e=n(ci,"P",{});var uut=s(V1e);V3r=r(uut,"Instantiates one of the model classes of the library (with a sequence-to-sequence speech-to-text modeling head) from a configuration."),uut.forEach(t),z3r=i(ci),Yc=n(ci,"P",{});var XW=s(Yc);W3r=r(XW,`Note:
Loading a model from its configuration file does `),z1e=n(XW,"STRONG",{});var but=s(z1e);Q3r=r(but,"not"),but.forEach(t),H3r=r(XW,` load the model weights. It only affects the
model\u2019s configuration. Use `),W1e=n(XW,"CODE",{});var vut=s(W1e);U3r=r(vut,"from_pretrained()"),vut.forEach(t),J3r=r(XW,"to load the model weights."),XW.forEach(t),Y3r=i(ci),Q1e=n(ci,"P",{});var Tut=s(Q1e);K3r=r(Tut,"Examples:"),Tut.forEach(t),Z3r=i(ci),m(dA.$$.fragment,ci),ci.forEach(t),e5r=i(di),wo=n(di,"DIV",{class:!0});var Aa=s(wo);m(cA.$$.fragment,Aa),o5r=i(Aa),H1e=n(Aa,"P",{});var Fut=s(H1e);r5r=r(Fut,"Instantiate one of the model classes of the library (with a sequence-to-sequence speech-to-text modeling head) from a pretrained model."),Fut.forEach(t),t5r=i(Aa),Ln=n(Aa,"P",{});var PM=s(Ln);a5r=r(PM,"The model class to instantiate is selected based on the "),U1e=n(PM,"CODE",{});var Cut=s(U1e);n5r=r(Cut,"model_type"),Cut.forEach(t),s5r=r(PM,` property of the config object (either
passed as an argument or loaded from `),J1e=n(PM,"CODE",{});var Mut=s(J1e);l5r=r(Mut,"pretrained_model_name_or_path"),Mut.forEach(t),i5r=r(PM,` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),Y1e=n(PM,"CODE",{});var Eut=s(Y1e);d5r=r(Eut,"pretrained_model_name_or_path"),Eut.forEach(t),c5r=r(PM,":"),PM.forEach(t),f5r=i(Aa),K1e=n(Aa,"UL",{});var yut=s(K1e);GF=n(yut,"LI",{});var m7e=s(GF);Z1e=n(m7e,"STRONG",{});var wut=s(Z1e);m5r=r(wut,"speech_to_text"),wut.forEach(t),g5r=r(m7e," \u2014 "),LX=n(m7e,"A",{href:!0});var Aut=s(LX);h5r=r(Aut,"TFSpeech2TextForConditionalGeneration"),Aut.forEach(t),p5r=r(m7e," (Speech2Text model)"),m7e.forEach(t),yut.forEach(t),_5r=i(Aa),ebe=n(Aa,"P",{});var Lut=s(ebe);u5r=r(Lut,"Examples:"),Lut.forEach(t),b5r=i(Aa),m(fA.$$.fragment,Aa),Aa.forEach(t),di.forEach(t),jke=i(d),Kc=n(d,"H2",{class:!0});var USe=s(Kc);XF=n(USe,"A",{id:!0,class:!0,href:!0});var But=s(XF);obe=n(But,"SPAN",{});var xut=s(obe);m(mA.$$.fragment,xut),xut.forEach(t),But.forEach(t),v5r=i(USe),rbe=n(USe,"SPAN",{});var kut=s(rbe);T5r=r(kut,"FlaxAutoModel"),kut.forEach(t),USe.forEach(t),Dke=i(d),Rr=n(d,"DIV",{class:!0});var fi=s(Rr);m(gA.$$.fragment,fi),F5r=i(fi),Zc=n(fi,"P",{});var VW=s(Zc);C5r=r(VW,`This is a generic model class that will be instantiated as one of the base model classes of the library when created
with the `),tbe=n(VW,"CODE",{});var Rut=s(tbe);M5r=r(Rut,"from_pretrained()"),Rut.forEach(t),E5r=r(VW,"class method or the "),abe=n(VW,"CODE",{});var Sut=s(abe);y5r=r(Sut,"from_config()"),Sut.forEach(t),w5r=r(VW,`class
method.`),VW.forEach(t),A5r=i(fi),hA=n(fi,"P",{});var JSe=s(hA);L5r=r(JSe,"This class cannot be instantiated directly using "),nbe=n(JSe,"CODE",{});var Put=s(nbe);B5r=r(Put,"__init__()"),Put.forEach(t),x5r=r(JSe," (throws an error)."),JSe.forEach(t),k5r=i(fi),wt=n(fi,"DIV",{class:!0});var mi=s(wt);m(pA.$$.fragment,mi),R5r=i(mi),sbe=n(mi,"P",{});var $ut=s(sbe);S5r=r($ut,"Instantiates one of the base model classes of the library from a configuration."),$ut.forEach(t),P5r=i(mi),ef=n(mi,"P",{});var zW=s(ef);$5r=r(zW,`Note:
Loading a model from its configuration file does `),lbe=n(zW,"STRONG",{});var Iut=s(lbe);I5r=r(Iut,"not"),Iut.forEach(t),j5r=r(zW,` load the model weights. It only affects the
model\u2019s configuration. Use `),ibe=n(zW,"CODE",{});var jut=s(ibe);D5r=r(jut,"from_pretrained()"),jut.forEach(t),N5r=r(zW,"to load the model weights."),zW.forEach(t),q5r=i(mi),dbe=n(mi,"P",{});var Dut=s(dbe);O5r=r(Dut,"Examples:"),Dut.forEach(t),G5r=i(mi),m(_A.$$.fragment,mi),mi.forEach(t),X5r=i(fi),Ao=n(fi,"DIV",{class:!0});var La=s(Ao);m(uA.$$.fragment,La),V5r=i(La),cbe=n(La,"P",{});var Nut=s(cbe);z5r=r(Nut,"Instantiate one of the base model classes of the library from a pretrained model."),Nut.forEach(t),W5r=i(La),Bn=n(La,"P",{});var $M=s(Bn);Q5r=r($M,"The model class to instantiate is selected based on the "),fbe=n($M,"CODE",{});var qut=s(fbe);H5r=r(qut,"model_type"),qut.forEach(t),U5r=r($M,` property of the config object (either
passed as an argument or loaded from `),mbe=n($M,"CODE",{});var Out=s(mbe);J5r=r(Out,"pretrained_model_name_or_path"),Out.forEach(t),Y5r=r($M,` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),gbe=n($M,"CODE",{});var Gut=s(gbe);K5r=r(Gut,"pretrained_model_name_or_path"),Gut.forEach(t),Z5r=r($M,":"),$M.forEach(t),e2r=i(La),z=n(La,"UL",{});var Q=s(z);VF=n(Q,"LI",{});var g7e=s(VF);hbe=n(g7e,"STRONG",{});var Xut=s(hbe);o2r=r(Xut,"albert"),Xut.forEach(t),r2r=r(g7e," \u2014 "),BX=n(g7e,"A",{href:!0});var Vut=s(BX);t2r=r(Vut,"FlaxAlbertModel"),Vut.forEach(t),a2r=r(g7e," (ALBERT model)"),g7e.forEach(t),n2r=i(Q),zF=n(Q,"LI",{});var h7e=s(zF);pbe=n(h7e,"STRONG",{});var zut=s(pbe);s2r=r(zut,"bart"),zut.forEach(t),l2r=r(h7e," \u2014 "),xX=n(h7e,"A",{href:!0});var Wut=s(xX);i2r=r(Wut,"FlaxBartModel"),Wut.forEach(t),d2r=r(h7e," (BART model)"),h7e.forEach(t),c2r=i(Q),WF=n(Q,"LI",{});var p7e=s(WF);_be=n(p7e,"STRONG",{});var Qut=s(_be);f2r=r(Qut,"beit"),Qut.forEach(t),m2r=r(p7e," \u2014 "),kX=n(p7e,"A",{href:!0});var Hut=s(kX);g2r=r(Hut,"FlaxBeitModel"),Hut.forEach(t),h2r=r(p7e," (BEiT model)"),p7e.forEach(t),p2r=i(Q),QF=n(Q,"LI",{});var _7e=s(QF);ube=n(_7e,"STRONG",{});var Uut=s(ube);_2r=r(Uut,"bert"),Uut.forEach(t),u2r=r(_7e," \u2014 "),RX=n(_7e,"A",{href:!0});var Jut=s(RX);b2r=r(Jut,"FlaxBertModel"),Jut.forEach(t),v2r=r(_7e," (BERT model)"),_7e.forEach(t),T2r=i(Q),HF=n(Q,"LI",{});var u7e=s(HF);bbe=n(u7e,"STRONG",{});var Yut=s(bbe);F2r=r(Yut,"big_bird"),Yut.forEach(t),C2r=r(u7e," \u2014 "),SX=n(u7e,"A",{href:!0});var Kut=s(SX);M2r=r(Kut,"FlaxBigBirdModel"),Kut.forEach(t),E2r=r(u7e," (BigBird model)"),u7e.forEach(t),y2r=i(Q),UF=n(Q,"LI",{});var b7e=s(UF);vbe=n(b7e,"STRONG",{});var Zut=s(vbe);w2r=r(Zut,"blenderbot"),Zut.forEach(t),A2r=r(b7e," \u2014 "),PX=n(b7e,"A",{href:!0});var e1t=s(PX);L2r=r(e1t,"FlaxBlenderbotModel"),e1t.forEach(t),B2r=r(b7e," (Blenderbot model)"),b7e.forEach(t),x2r=i(Q),JF=n(Q,"LI",{});var v7e=s(JF);Tbe=n(v7e,"STRONG",{});var o1t=s(Tbe);k2r=r(o1t,"blenderbot-small"),o1t.forEach(t),R2r=r(v7e," \u2014 "),$X=n(v7e,"A",{href:!0});var r1t=s($X);S2r=r(r1t,"FlaxBlenderbotSmallModel"),r1t.forEach(t),P2r=r(v7e," (BlenderbotSmall model)"),v7e.forEach(t),$2r=i(Q),YF=n(Q,"LI",{});var T7e=s(YF);Fbe=n(T7e,"STRONG",{});var t1t=s(Fbe);I2r=r(t1t,"clip"),t1t.forEach(t),j2r=r(T7e," \u2014 "),IX=n(T7e,"A",{href:!0});var a1t=s(IX);D2r=r(a1t,"FlaxCLIPModel"),a1t.forEach(t),N2r=r(T7e," (CLIP model)"),T7e.forEach(t),q2r=i(Q),KF=n(Q,"LI",{});var F7e=s(KF);Cbe=n(F7e,"STRONG",{});var n1t=s(Cbe);O2r=r(n1t,"distilbert"),n1t.forEach(t),G2r=r(F7e," \u2014 "),jX=n(F7e,"A",{href:!0});var s1t=s(jX);X2r=r(s1t,"FlaxDistilBertModel"),s1t.forEach(t),V2r=r(F7e," (DistilBERT model)"),F7e.forEach(t),z2r=i(Q),ZF=n(Q,"LI",{});var C7e=s(ZF);Mbe=n(C7e,"STRONG",{});var l1t=s(Mbe);W2r=r(l1t,"electra"),l1t.forEach(t),Q2r=r(C7e," \u2014 "),DX=n(C7e,"A",{href:!0});var i1t=s(DX);H2r=r(i1t,"FlaxElectraModel"),i1t.forEach(t),U2r=r(C7e," (ELECTRA model)"),C7e.forEach(t),J2r=i(Q),e9=n(Q,"LI",{});var M7e=s(e9);Ebe=n(M7e,"STRONG",{});var d1t=s(Ebe);Y2r=r(d1t,"gpt2"),d1t.forEach(t),K2r=r(M7e," \u2014 "),NX=n(M7e,"A",{href:!0});var c1t=s(NX);Z2r=r(c1t,"FlaxGPT2Model"),c1t.forEach(t),evr=r(M7e," (OpenAI GPT-2 model)"),M7e.forEach(t),ovr=i(Q),o9=n(Q,"LI",{});var E7e=s(o9);ybe=n(E7e,"STRONG",{});var f1t=s(ybe);rvr=r(f1t,"gpt_neo"),f1t.forEach(t),tvr=r(E7e," \u2014 "),qX=n(E7e,"A",{href:!0});var m1t=s(qX);avr=r(m1t,"FlaxGPTNeoModel"),m1t.forEach(t),nvr=r(E7e," (GPT Neo model)"),E7e.forEach(t),svr=i(Q),r9=n(Q,"LI",{});var y7e=s(r9);wbe=n(y7e,"STRONG",{});var g1t=s(wbe);lvr=r(g1t,"gptj"),g1t.forEach(t),ivr=r(y7e," \u2014 "),OX=n(y7e,"A",{href:!0});var h1t=s(OX);dvr=r(h1t,"FlaxGPTJModel"),h1t.forEach(t),cvr=r(y7e," (GPT-J model)"),y7e.forEach(t),fvr=i(Q),t9=n(Q,"LI",{});var w7e=s(t9);Abe=n(w7e,"STRONG",{});var p1t=s(Abe);mvr=r(p1t,"marian"),p1t.forEach(t),gvr=r(w7e," \u2014 "),GX=n(w7e,"A",{href:!0});var _1t=s(GX);hvr=r(_1t,"FlaxMarianModel"),_1t.forEach(t),pvr=r(w7e," (Marian model)"),w7e.forEach(t),_vr=i(Q),a9=n(Q,"LI",{});var A7e=s(a9);Lbe=n(A7e,"STRONG",{});var u1t=s(Lbe);uvr=r(u1t,"mbart"),u1t.forEach(t),bvr=r(A7e," \u2014 "),XX=n(A7e,"A",{href:!0});var b1t=s(XX);vvr=r(b1t,"FlaxMBartModel"),b1t.forEach(t),Tvr=r(A7e," (mBART model)"),A7e.forEach(t),Fvr=i(Q),n9=n(Q,"LI",{});var L7e=s(n9);Bbe=n(L7e,"STRONG",{});var v1t=s(Bbe);Cvr=r(v1t,"mt5"),v1t.forEach(t),Mvr=r(L7e," \u2014 "),VX=n(L7e,"A",{href:!0});var T1t=s(VX);Evr=r(T1t,"FlaxMT5Model"),T1t.forEach(t),yvr=r(L7e," (mT5 model)"),L7e.forEach(t),wvr=i(Q),s9=n(Q,"LI",{});var B7e=s(s9);xbe=n(B7e,"STRONG",{});var F1t=s(xbe);Avr=r(F1t,"pegasus"),F1t.forEach(t),Lvr=r(B7e," \u2014 "),zX=n(B7e,"A",{href:!0});var C1t=s(zX);Bvr=r(C1t,"FlaxPegasusModel"),C1t.forEach(t),xvr=r(B7e," (Pegasus model)"),B7e.forEach(t),kvr=i(Q),l9=n(Q,"LI",{});var x7e=s(l9);kbe=n(x7e,"STRONG",{});var M1t=s(kbe);Rvr=r(M1t,"roberta"),M1t.forEach(t),Svr=r(x7e," \u2014 "),WX=n(x7e,"A",{href:!0});var E1t=s(WX);Pvr=r(E1t,"FlaxRobertaModel"),E1t.forEach(t),$vr=r(x7e," (RoBERTa model)"),x7e.forEach(t),Ivr=i(Q),i9=n(Q,"LI",{});var k7e=s(i9);Rbe=n(k7e,"STRONG",{});var y1t=s(Rbe);jvr=r(y1t,"roformer"),y1t.forEach(t),Dvr=r(k7e," \u2014 "),QX=n(k7e,"A",{href:!0});var w1t=s(QX);Nvr=r(w1t,"FlaxRoFormerModel"),w1t.forEach(t),qvr=r(k7e," (RoFormer model)"),k7e.forEach(t),Ovr=i(Q),d9=n(Q,"LI",{});var R7e=s(d9);Sbe=n(R7e,"STRONG",{});var A1t=s(Sbe);Gvr=r(A1t,"t5"),A1t.forEach(t),Xvr=r(R7e," \u2014 "),HX=n(R7e,"A",{href:!0});var L1t=s(HX);Vvr=r(L1t,"FlaxT5Model"),L1t.forEach(t),zvr=r(R7e," (T5 model)"),R7e.forEach(t),Wvr=i(Q),c9=n(Q,"LI",{});var S7e=s(c9);Pbe=n(S7e,"STRONG",{});var B1t=s(Pbe);Qvr=r(B1t,"vision-text-dual-encoder"),B1t.forEach(t),Hvr=r(S7e," \u2014 "),UX=n(S7e,"A",{href:!0});var x1t=s(UX);Uvr=r(x1t,"FlaxVisionTextDualEncoderModel"),x1t.forEach(t),Jvr=r(S7e," (VisionTextDualEncoder model)"),S7e.forEach(t),Yvr=i(Q),f9=n(Q,"LI",{});var P7e=s(f9);$be=n(P7e,"STRONG",{});var k1t=s($be);Kvr=r(k1t,"vit"),k1t.forEach(t),Zvr=r(P7e," \u2014 "),JX=n(P7e,"A",{href:!0});var R1t=s(JX);eTr=r(R1t,"FlaxViTModel"),R1t.forEach(t),oTr=r(P7e," (ViT model)"),P7e.forEach(t),rTr=i(Q),m9=n(Q,"LI",{});var $7e=s(m9);Ibe=n($7e,"STRONG",{});var S1t=s(Ibe);tTr=r(S1t,"wav2vec2"),S1t.forEach(t),aTr=r($7e," \u2014 "),YX=n($7e,"A",{href:!0});var P1t=s(YX);nTr=r(P1t,"FlaxWav2Vec2Model"),P1t.forEach(t),sTr=r($7e," (Wav2Vec2 model)"),$7e.forEach(t),lTr=i(Q),g9=n(Q,"LI",{});var I7e=s(g9);jbe=n(I7e,"STRONG",{});var $1t=s(jbe);iTr=r($1t,"xglm"),$1t.forEach(t),dTr=r(I7e," \u2014 "),KX=n(I7e,"A",{href:!0});var I1t=s(KX);cTr=r(I1t,"FlaxXGLMModel"),I1t.forEach(t),fTr=r(I7e," (XGLM model)"),I7e.forEach(t),mTr=i(Q),h9=n(Q,"LI",{});var j7e=s(h9);Dbe=n(j7e,"STRONG",{});var j1t=s(Dbe);gTr=r(j1t,"xlm-roberta"),j1t.forEach(t),hTr=r(j7e," \u2014 "),ZX=n(j7e,"A",{href:!0});var D1t=s(ZX);pTr=r(D1t,"FlaxXLMRobertaModel"),D1t.forEach(t),_Tr=r(j7e," (XLM-RoBERTa model)"),j7e.forEach(t),Q.forEach(t),uTr=i(La),Nbe=n(La,"P",{});var N1t=s(Nbe);bTr=r(N1t,"Examples:"),N1t.forEach(t),vTr=i(La),m(bA.$$.fragment,La),La.forEach(t),fi.forEach(t),Nke=i(d),of=n(d,"H2",{class:!0});var YSe=s(of);p9=n(YSe,"A",{id:!0,class:!0,href:!0});var q1t=s(p9);qbe=n(q1t,"SPAN",{});var O1t=s(qbe);m(vA.$$.fragment,O1t),O1t.forEach(t),q1t.forEach(t),TTr=i(YSe),Obe=n(YSe,"SPAN",{});var G1t=s(Obe);FTr=r(G1t,"FlaxAutoModelForCausalLM"),G1t.forEach(t),YSe.forEach(t),qke=i(d),Sr=n(d,"DIV",{class:!0});var gi=s(Sr);m(TA.$$.fragment,gi),CTr=i(gi),rf=n(gi,"P",{});var WW=s(rf);MTr=r(WW,`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 `),Gbe=n(WW,"CODE",{});var X1t=s(Gbe);ETr=r(X1t,"from_pretrained()"),X1t.forEach(t),yTr=r(WW,"class method or the "),Xbe=n(WW,"CODE",{});var V1t=s(Xbe);wTr=r(V1t,"from_config()"),V1t.forEach(t),ATr=r(WW,`class
method.`),WW.forEach(t),LTr=i(gi),FA=n(gi,"P",{});var KSe=s(FA);BTr=r(KSe,"This class cannot be instantiated directly using "),Vbe=n(KSe,"CODE",{});var z1t=s(Vbe);xTr=r(z1t,"__init__()"),z1t.forEach(t),kTr=r(KSe," (throws an error)."),KSe.forEach(t),RTr=i(gi),At=n(gi,"DIV",{class:!0});var hi=s(At);m(CA.$$.fragment,hi),STr=i(hi),zbe=n(hi,"P",{});var W1t=s(zbe);PTr=r(W1t,"Instantiates one of the model classes of the library (with a causal language modeling head) from a configuration."),W1t.forEach(t),$Tr=i(hi),tf=n(hi,"P",{});var QW=s(tf);ITr=r(QW,`Note:
Loading a model from its configuration file does `),Wbe=n(QW,"STRONG",{});var Q1t=s(Wbe);jTr=r(Q1t,"not"),Q1t.forEach(t),DTr=r(QW,` load the model weights. It only affects the
model\u2019s configuration. Use `),Qbe=n(QW,"CODE",{});var H1t=s(Qbe);NTr=r(H1t,"from_pretrained()"),H1t.forEach(t),qTr=r(QW,"to load the model weights."),QW.forEach(t),OTr=i(hi),Hbe=n(hi,"P",{});var U1t=s(Hbe);GTr=r(U1t,"Examples:"),U1t.forEach(t),XTr=i(hi),m(MA.$$.fragment,hi),hi.forEach(t),VTr=i(gi),Lo=n(gi,"DIV",{class:!0});var Ba=s(Lo);m(EA.$$.fragment,Ba),zTr=i(Ba),Ube=n(Ba,"P",{});var J1t=s(Ube);WTr=r(J1t,"Instantiate one of the model classes of the library (with a causal language modeling head) from a pretrained model."),J1t.forEach(t),QTr=i(Ba),xn=n(Ba,"P",{});var IM=s(xn);HTr=r(IM,"The model class to instantiate is selected based on the "),Jbe=n(IM,"CODE",{});var Y1t=s(Jbe);UTr=r(Y1t,"model_type"),Y1t.forEach(t),JTr=r(IM,` property of the config object (either
passed as an argument or loaded from `),Ybe=n(IM,"CODE",{});var K1t=s(Ybe);YTr=r(K1t,"pretrained_model_name_or_path"),K1t.forEach(t),KTr=r(IM,` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),Kbe=n(IM,"CODE",{});var Z1t=s(Kbe);ZTr=r(Z1t,"pretrained_model_name_or_path"),Z1t.forEach(t),e8r=r(IM,":"),IM.forEach(t),o8r=i(Ba),ca=n(Ba,"UL",{});var pi=s(ca);_9=n(pi,"LI",{});var D7e=s(_9);Zbe=n(D7e,"STRONG",{});var ebt=s(Zbe);r8r=r(ebt,"bart"),ebt.forEach(t),t8r=r(D7e," \u2014 "),eV=n(D7e,"A",{href:!0});var obt=s(eV);a8r=r(obt,"FlaxBartForCausalLM"),obt.forEach(t),n8r=r(D7e," (BART model)"),D7e.forEach(t),s8r=i(pi),u9=n(pi,"LI",{});var N7e=s(u9);e3e=n(N7e,"STRONG",{});var rbt=s(e3e);l8r=r(rbt,"gpt2"),rbt.forEach(t),i8r=r(N7e," \u2014 "),oV=n(N7e,"A",{href:!0});var tbt=s(oV);d8r=r(tbt,"FlaxGPT2LMHeadModel"),tbt.forEach(t),c8r=r(N7e," (OpenAI GPT-2 model)"),N7e.forEach(t),f8r=i(pi),b9=n(pi,"LI",{});var q7e=s(b9);o3e=n(q7e,"STRONG",{});var abt=s(o3e);m8r=r(abt,"gpt_neo"),abt.forEach(t),g8r=r(q7e," \u2014 "),rV=n(q7e,"A",{href:!0});var nbt=s(rV);h8r=r(nbt,"FlaxGPTNeoForCausalLM"),nbt.forEach(t),p8r=r(q7e," (GPT Neo model)"),q7e.forEach(t),_8r=i(pi),v9=n(pi,"LI",{});var O7e=s(v9);r3e=n(O7e,"STRONG",{});var sbt=s(r3e);u8r=r(sbt,"gptj"),sbt.forEach(t),b8r=r(O7e," \u2014 "),tV=n(O7e,"A",{href:!0});var lbt=s(tV);v8r=r(lbt,"FlaxGPTJForCausalLM"),lbt.forEach(t),T8r=r(O7e," (GPT-J model)"),O7e.forEach(t),F8r=i(pi),T9=n(pi,"LI",{});var G7e=s(T9);t3e=n(G7e,"STRONG",{});var ibt=s(t3e);C8r=r(ibt,"xglm"),ibt.forEach(t),M8r=r(G7e," \u2014 "),aV=n(G7e,"A",{href:!0});var dbt=s(aV);E8r=r(dbt,"FlaxXGLMForCausalLM"),dbt.forEach(t),y8r=r(G7e," (XGLM model)"),G7e.forEach(t),pi.forEach(t),w8r=i(Ba),a3e=n(Ba,"P",{});var cbt=s(a3e);A8r=r(cbt,"Examples:"),cbt.forEach(t),L8r=i(Ba),m(yA.$$.fragment,Ba),Ba.forEach(t),gi.forEach(t),Oke=i(d),af=n(d,"H2",{class:!0});var ZSe=s(af);F9=n(ZSe,"A",{id:!0,class:!0,href:!0});var fbt=s(F9);n3e=n(fbt,"SPAN",{});var mbt=s(n3e);m(wA.$$.fragment,mbt),mbt.forEach(t),fbt.forEach(t),B8r=i(ZSe),s3e=n(ZSe,"SPAN",{});var gbt=s(s3e);x8r=r(gbt,"FlaxAutoModelForPreTraining"),gbt.forEach(t),ZSe.forEach(t),Gke=i(d),Pr=n(d,"DIV",{class:!0});var _i=s(Pr);m(AA.$$.fragment,_i),k8r=i(_i),nf=n(_i,"P",{});var HW=s(nf);R8r=r(HW,`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 `),l3e=n(HW,"CODE",{});var hbt=s(l3e);S8r=r(hbt,"from_pretrained()"),hbt.forEach(t),P8r=r(HW,"class method or the "),i3e=n(HW,"CODE",{});var pbt=s(i3e);$8r=r(pbt,"from_config()"),pbt.forEach(t),I8r=r(HW,`class
method.`),HW.forEach(t),j8r=i(_i),LA=n(_i,"P",{});var ePe=s(LA);D8r=r(ePe,"This class cannot be instantiated directly using "),d3e=n(ePe,"CODE",{});var _bt=s(d3e);N8r=r(_bt,"__init__()"),_bt.forEach(t),q8r=r(ePe," (throws an error)."),ePe.forEach(t),O8r=i(_i),Lt=n(_i,"DIV",{class:!0});var ui=s(Lt);m(BA.$$.fragment,ui),G8r=i(ui),c3e=n(ui,"P",{});var ubt=s(c3e);X8r=r(ubt,"Instantiates one of the model classes of the library (with a pretraining head) from a configuration."),ubt.forEach(t),V8r=i(ui),sf=n(ui,"P",{});var UW=s(sf);z8r=r(UW,`Note:
Loading a model from its configuration file does `),f3e=n(UW,"STRONG",{});var bbt=s(f3e);W8r=r(bbt,"not"),bbt.forEach(t),Q8r=r(UW,` load the model weights. It only affects the
model\u2019s configuration. Use `),m3e=n(UW,"CODE",{});var vbt=s(m3e);H8r=r(vbt,"from_pretrained()"),vbt.forEach(t),U8r=r(UW,"to load the model weights."),UW.forEach(t),J8r=i(ui),g3e=n(ui,"P",{});var Tbt=s(g3e);Y8r=r(Tbt,"Examples:"),Tbt.forEach(t),K8r=i(ui),m(xA.$$.fragment,ui),ui.forEach(t),Z8r=i(_i),Bo=n(_i,"DIV",{class:!0});var xa=s(Bo);m(kA.$$.fragment,xa),eFr=i(xa),h3e=n(xa,"P",{});var Fbt=s(h3e);oFr=r(Fbt,"Instantiate one of the model classes of the library (with a pretraining head) from a pretrained model."),Fbt.forEach(t),rFr=i(xa),kn=n(xa,"P",{});var jM=s(kn);tFr=r(jM,"The model class to instantiate is selected based on the "),p3e=n(jM,"CODE",{});var Cbt=s(p3e);aFr=r(Cbt,"model_type"),Cbt.forEach(t),nFr=r(jM,` property of the config object (either
passed as an argument or loaded from `),_3e=n(jM,"CODE",{});var Mbt=s(_3e);sFr=r(Mbt,"pretrained_model_name_or_path"),Mbt.forEach(t),lFr=r(jM,` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),u3e=n(jM,"CODE",{});var Ebt=s(u3e);iFr=r(Ebt,"pretrained_model_name_or_path"),Ebt.forEach(t),dFr=r(jM,":"),jM.forEach(t),cFr=i(xa),ce=n(xa,"UL",{});var ge=s(ce);C9=n(ge,"LI",{});var X7e=s(C9);b3e=n(X7e,"STRONG",{});var ybt=s(b3e);fFr=r(ybt,"albert"),ybt.forEach(t),mFr=r(X7e," \u2014 "),nV=n(X7e,"A",{href:!0});var wbt=s(nV);gFr=r(wbt,"FlaxAlbertForPreTraining"),wbt.forEach(t),hFr=r(X7e," (ALBERT model)"),X7e.forEach(t),pFr=i(ge),M9=n(ge,"LI",{});var V7e=s(M9);v3e=n(V7e,"STRONG",{});var Abt=s(v3e);_Fr=r(Abt,"bart"),Abt.forEach(t),uFr=r(V7e," \u2014 "),sV=n(V7e,"A",{href:!0});var Lbt=s(sV);bFr=r(Lbt,"FlaxBartForConditionalGeneration"),Lbt.forEach(t),vFr=r(V7e," (BART model)"),V7e.forEach(t),TFr=i(ge),E9=n(ge,"LI",{});var z7e=s(E9);T3e=n(z7e,"STRONG",{});var Bbt=s(T3e);FFr=r(Bbt,"bert"),Bbt.forEach(t),CFr=r(z7e," \u2014 "),lV=n(z7e,"A",{href:!0});var xbt=s(lV);MFr=r(xbt,"FlaxBertForPreTraining"),xbt.forEach(t),EFr=r(z7e," (BERT model)"),z7e.forEach(t),yFr=i(ge),y9=n(ge,"LI",{});var W7e=s(y9);F3e=n(W7e,"STRONG",{});var kbt=s(F3e);wFr=r(kbt,"big_bird"),kbt.forEach(t),AFr=r(W7e," \u2014 "),iV=n(W7e,"A",{href:!0});var Rbt=s(iV);LFr=r(Rbt,"FlaxBigBirdForPreTraining"),Rbt.forEach(t),BFr=r(W7e," (BigBird model)"),W7e.forEach(t),xFr=i(ge),w9=n(ge,"LI",{});var Q7e=s(w9);C3e=n(Q7e,"STRONG",{});var Sbt=s(C3e);kFr=r(Sbt,"electra"),Sbt.forEach(t),RFr=r(Q7e," \u2014 "),dV=n(Q7e,"A",{href:!0});var Pbt=s(dV);SFr=r(Pbt,"FlaxElectraForPreTraining"),Pbt.forEach(t),PFr=r(Q7e," (ELECTRA model)"),Q7e.forEach(t),$Fr=i(ge),A9=n(ge,"LI",{});var H7e=s(A9);M3e=n(H7e,"STRONG",{});var $bt=s(M3e);IFr=r($bt,"mbart"),$bt.forEach(t),jFr=r(H7e," \u2014 "),cV=n(H7e,"A",{href:!0});var Ibt=s(cV);DFr=r(Ibt,"FlaxMBartForConditionalGeneration"),Ibt.forEach(t),NFr=r(H7e," (mBART model)"),H7e.forEach(t),qFr=i(ge),L9=n(ge,"LI",{});var U7e=s(L9);E3e=n(U7e,"STRONG",{});var jbt=s(E3e);OFr=r(jbt,"mt5"),jbt.forEach(t),GFr=r(U7e," \u2014 "),fV=n(U7e,"A",{href:!0});var Dbt=s(fV);XFr=r(Dbt,"FlaxMT5ForConditionalGeneration"),Dbt.forEach(t),VFr=r(U7e," (mT5 model)"),U7e.forEach(t),zFr=i(ge),B9=n(ge,"LI",{});var J7e=s(B9);y3e=n(J7e,"STRONG",{});var Nbt=s(y3e);WFr=r(Nbt,"roberta"),Nbt.forEach(t),QFr=r(J7e," \u2014 "),mV=n(J7e,"A",{href:!0});var qbt=s(mV);HFr=r(qbt,"FlaxRobertaForMaskedLM"),qbt.forEach(t),UFr=r(J7e," (RoBERTa model)"),J7e.forEach(t),JFr=i(ge),x9=n(ge,"LI",{});var Y7e=s(x9);w3e=n(Y7e,"STRONG",{});var Obt=s(w3e);YFr=r(Obt,"roformer"),Obt.forEach(t),KFr=r(Y7e," \u2014 "),gV=n(Y7e,"A",{href:!0});var Gbt=s(gV);ZFr=r(Gbt,"FlaxRoFormerForMaskedLM"),Gbt.forEach(t),e9r=r(Y7e," (RoFormer model)"),Y7e.forEach(t),o9r=i(ge),k9=n(ge,"LI",{});var K7e=s(k9);A3e=n(K7e,"STRONG",{});var Xbt=s(A3e);r9r=r(Xbt,"t5"),Xbt.forEach(t),t9r=r(K7e," \u2014 "),hV=n(K7e,"A",{href:!0});var Vbt=s(hV);a9r=r(Vbt,"FlaxT5ForConditionalGeneration"),Vbt.forEach(t),n9r=r(K7e," (T5 model)"),K7e.forEach(t),s9r=i(ge),R9=n(ge,"LI",{});var Z7e=s(R9);L3e=n(Z7e,"STRONG",{});var zbt=s(L3e);l9r=r(zbt,"wav2vec2"),zbt.forEach(t),i9r=r(Z7e," \u2014 "),pV=n(Z7e,"A",{href:!0});var Wbt=s(pV);d9r=r(Wbt,"FlaxWav2Vec2ForPreTraining"),Wbt.forEach(t),c9r=r(Z7e," (Wav2Vec2 model)"),Z7e.forEach(t),f9r=i(ge),S9=n(ge,"LI",{});var eBe=s(S9);B3e=n(eBe,"STRONG",{});var Qbt=s(B3e);m9r=r(Qbt,"xlm-roberta"),Qbt.forEach(t),g9r=r(eBe," \u2014 "),_V=n(eBe,"A",{href:!0});var Hbt=s(_V);h9r=r(Hbt,"FlaxXLMRobertaForMaskedLM"),Hbt.forEach(t),p9r=r(eBe," (XLM-RoBERTa model)"),eBe.forEach(t),ge.forEach(t),_9r=i(xa),x3e=n(xa,"P",{});var Ubt=s(x3e);u9r=r(Ubt,"Examples:"),Ubt.forEach(t),b9r=i(xa),m(RA.$$.fragment,xa),xa.forEach(t),_i.forEach(t),Xke=i(d),lf=n(d,"H2",{class:!0});var oPe=s(lf);P9=n(oPe,"A",{id:!0,class:!0,href:!0});var Jbt=s(P9);k3e=n(Jbt,"SPAN",{});var Ybt=s(k3e);m(SA.$$.fragment,Ybt),Ybt.forEach(t),Jbt.forEach(t),v9r=i(oPe),R3e=n(oPe,"SPAN",{});var Kbt=s(R3e);T9r=r(Kbt,"FlaxAutoModelForMaskedLM"),Kbt.forEach(t),oPe.forEach(t),Vke=i(d),$r=n(d,"DIV",{class:!0});var bi=s($r);m(PA.$$.fragment,bi),F9r=i(bi),df=n(bi,"P",{});var JW=s(df);C9r=r(JW,`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 `),S3e=n(JW,"CODE",{});var Zbt=s(S3e);M9r=r(Zbt,"from_pretrained()"),Zbt.forEach(t),E9r=r(JW,"class method or the "),P3e=n(JW,"CODE",{});var e3t=s(P3e);y9r=r(e3t,"from_config()"),e3t.forEach(t),w9r=r(JW,`class
method.`),JW.forEach(t),A9r=i(bi),$A=n(bi,"P",{});var rPe=s($A);L9r=r(rPe,"This class cannot be instantiated directly using "),$3e=n(rPe,"CODE",{});var o3t=s($3e);B9r=r(o3t,"__init__()"),o3t.forEach(t),x9r=r(rPe," (throws an error)."),rPe.forEach(t),k9r=i(bi),Bt=n(bi,"DIV",{class:!0});var vi=s(Bt);m(IA.$$.fragment,vi),R9r=i(vi),I3e=n(vi,"P",{});var r3t=s(I3e);S9r=r(r3t,"Instantiates one of the model classes of the library (with a masked language modeling head) from a configuration."),r3t.forEach(t),P9r=i(vi),cf=n(vi,"P",{});var YW=s(cf);$9r=r(YW,`Note:
Loading a model from its configuration file does `),j3e=n(YW,"STRONG",{});var t3t=s(j3e);I9r=r(t3t,"not"),t3t.forEach(t),j9r=r(YW,` load the model weights. It only affects the
model\u2019s configuration. Use `),D3e=n(YW,"CODE",{});var a3t=s(D3e);D9r=r(a3t,"from_pretrained()"),a3t.forEach(t),N9r=r(YW,"to load the model weights."),YW.forEach(t),q9r=i(vi),N3e=n(vi,"P",{});var n3t=s(N3e);O9r=r(n3t,"Examples:"),n3t.forEach(t),G9r=i(vi),m(jA.$$.fragment,vi),vi.forEach(t),X9r=i(bi),xo=n(bi,"DIV",{class:!0});var ka=s(xo);m(DA.$$.fragment,ka),V9r=i(ka),q3e=n(ka,"P",{});var s3t=s(q3e);z9r=r(s3t,"Instantiate one of the model classes of the library (with a masked language modeling head) from a pretrained model."),s3t.forEach(t),W9r=i(ka),Rn=n(ka,"P",{});var DM=s(Rn);Q9r=r(DM,"The model class to instantiate is selected based on the "),O3e=n(DM,"CODE",{});var l3t=s(O3e);H9r=r(l3t,"model_type"),l3t.forEach(t),U9r=r(DM,` property of the config object (either
passed as an argument or loaded from `),G3e=n(DM,"CODE",{});var i3t=s(G3e);J9r=r(i3t,"pretrained_model_name_or_path"),i3t.forEach(t),Y9r=r(DM,` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),X3e=n(DM,"CODE",{});var d3t=s(X3e);K9r=r(d3t,"pretrained_model_name_or_path"),d3t.forEach(t),Z9r=r(DM,":"),DM.forEach(t),eCr=i(ka),ue=n(ka,"UL",{});var ye=s(ue);$9=n(ye,"LI",{});var oBe=s($9);V3e=n(oBe,"STRONG",{});var c3t=s(V3e);oCr=r(c3t,"albert"),c3t.forEach(t),rCr=r(oBe," \u2014 "),uV=n(oBe,"A",{href:!0});var f3t=s(uV);tCr=r(f3t,"FlaxAlbertForMaskedLM"),f3t.forEach(t),aCr=r(oBe," (ALBERT model)"),oBe.forEach(t),nCr=i(ye),I9=n(ye,"LI",{});var rBe=s(I9);z3e=n(rBe,"STRONG",{});var m3t=s(z3e);sCr=r(m3t,"bart"),m3t.forEach(t),lCr=r(rBe," \u2014 "),bV=n(rBe,"A",{href:!0});var g3t=s(bV);iCr=r(g3t,"FlaxBartForConditionalGeneration"),g3t.forEach(t),dCr=r(rBe," (BART model)"),rBe.forEach(t),cCr=i(ye),j9=n(ye,"LI",{});var tBe=s(j9);W3e=n(tBe,"STRONG",{});var h3t=s(W3e);fCr=r(h3t,"bert"),h3t.forEach(t),mCr=r(tBe," \u2014 "),vV=n(tBe,"A",{href:!0});var p3t=s(vV);gCr=r(p3t,"FlaxBertForMaskedLM"),p3t.forEach(t),hCr=r(tBe," (BERT model)"),tBe.forEach(t),pCr=i(ye),D9=n(ye,"LI",{});var aBe=s(D9);Q3e=n(aBe,"STRONG",{});var _3t=s(Q3e);_Cr=r(_3t,"big_bird"),_3t.forEach(t),uCr=r(aBe," \u2014 "),TV=n(aBe,"A",{href:!0});var u3t=s(TV);bCr=r(u3t,"FlaxBigBirdForMaskedLM"),u3t.forEach(t),vCr=r(aBe," (BigBird model)"),aBe.forEach(t),TCr=i(ye),N9=n(ye,"LI",{});var nBe=s(N9);H3e=n(nBe,"STRONG",{});var b3t=s(H3e);FCr=r(b3t,"distilbert"),b3t.forEach(t),CCr=r(nBe," \u2014 "),FV=n(nBe,"A",{href:!0});var v3t=s(FV);MCr=r(v3t,"FlaxDistilBertForMaskedLM"),v3t.forEach(t),ECr=r(nBe," (DistilBERT model)"),nBe.forEach(t),yCr=i(ye),q9=n(ye,"LI",{});var sBe=s(q9);U3e=n(sBe,"STRONG",{});var T3t=s(U3e);wCr=r(T3t,"electra"),T3t.forEach(t),ACr=r(sBe," \u2014 "),CV=n(sBe,"A",{href:!0});var F3t=s(CV);LCr=r(F3t,"FlaxElectraForMaskedLM"),F3t.forEach(t),BCr=r(sBe," (ELECTRA model)"),sBe.forEach(t),xCr=i(ye),O9=n(ye,"LI",{});var lBe=s(O9);J3e=n(lBe,"STRONG",{});var C3t=s(J3e);kCr=r(C3t,"mbart"),C3t.forEach(t),RCr=r(lBe," \u2014 "),MV=n(lBe,"A",{href:!0});var M3t=s(MV);SCr=r(M3t,"FlaxMBartForConditionalGeneration"),M3t.forEach(t),PCr=r(lBe," (mBART model)"),lBe.forEach(t),$Cr=i(ye),G9=n(ye,"LI",{});var iBe=s(G9);Y3e=n(iBe,"STRONG",{});var E3t=s(Y3e);ICr=r(E3t,"roberta"),E3t.forEach(t),jCr=r(iBe," \u2014 "),EV=n(iBe,"A",{href:!0});var y3t=s(EV);DCr=r(y3t,"FlaxRobertaForMaskedLM"),y3t.forEach(t),NCr=r(iBe," (RoBERTa model)"),iBe.forEach(t),qCr=i(ye),X9=n(ye,"LI",{});var dBe=s(X9);K3e=n(dBe,"STRONG",{});var w3t=s(K3e);OCr=r(w3t,"roformer"),w3t.forEach(t),GCr=r(dBe," \u2014 "),yV=n(dBe,"A",{href:!0});var A3t=s(yV);XCr=r(A3t,"FlaxRoFormerForMaskedLM"),A3t.forEach(t),VCr=r(dBe," (RoFormer model)"),dBe.forEach(t),zCr=i(ye),V9=n(ye,"LI",{});var cBe=s(V9);Z3e=n(cBe,"STRONG",{});var L3t=s(Z3e);WCr=r(L3t,"xlm-roberta"),L3t.forEach(t),QCr=r(cBe," \u2014 "),wV=n(cBe,"A",{href:!0});var B3t=s(wV);HCr=r(B3t,"FlaxXLMRobertaForMaskedLM"),B3t.forEach(t),UCr=r(cBe," (XLM-RoBERTa model)"),cBe.forEach(t),ye.forEach(t),JCr=i(ka),e5e=n(ka,"P",{});var x3t=s(e5e);YCr=r(x3t,"Examples:"),x3t.forEach(t),KCr=i(ka),m(NA.$$.fragment,ka),ka.forEach(t),bi.forEach(t),zke=i(d),ff=n(d,"H2",{class:!0});var tPe=s(ff);z9=n(tPe,"A",{id:!0,class:!0,href:!0});var k3t=s(z9);o5e=n(k3t,"SPAN",{});var R3t=s(o5e);m(qA.$$.fragment,R3t),R3t.forEach(t),k3t.forEach(t),ZCr=i(tPe),r5e=n(tPe,"SPAN",{});var S3t=s(r5e);eMr=r(S3t,"FlaxAutoModelForSeq2SeqLM"),S3t.forEach(t),tPe.forEach(t),Wke=i(d),Ir=n(d,"DIV",{class:!0});var Ti=s(Ir);m(OA.$$.fragment,Ti),oMr=i(Ti),mf=n(Ti,"P",{});var KW=s(mf);rMr=r(KW,`This is a generic model class that will be instantiated as one of the model classes of the library (with a sequence-to-sequence language modeling head) when created
with the `),t5e=n(KW,"CODE",{});var P3t=s(t5e);tMr=r(P3t,"from_pretrained()"),P3t.forEach(t),aMr=r(KW,"class method or the "),a5e=n(KW,"CODE",{});var $3t=s(a5e);nMr=r($3t,"from_config()"),$3t.forEach(t),sMr=r(KW,`class
method.`),KW.forEach(t),lMr=i(Ti),GA=n(Ti,"P",{});var aPe=s(GA);iMr=r(aPe,"This class cannot be instantiated directly using "),n5e=n(aPe,"CODE",{});var I3t=s(n5e);dMr=r(I3t,"__init__()"),I3t.forEach(t),cMr=r(aPe," (throws an error)."),aPe.forEach(t),fMr=i(Ti),xt=n(Ti,"DIV",{class:!0});var Fi=s(xt);m(XA.$$.fragment,Fi),mMr=i(Fi),s5e=n(Fi,"P",{});var j3t=s(s5e);gMr=r(j3t,"Instantiates one of the model classes of the library (with a sequence-to-sequence language modeling head) from a configuration."),j3t.forEach(t),hMr=i(Fi),gf=n(Fi,"P",{});var ZW=s(gf);pMr=r(ZW,`Note:
Loading a model from its configuration file does `),l5e=n(ZW,"STRONG",{});var D3t=s(l5e);_Mr=r(D3t,"not"),D3t.forEach(t),uMr=r(ZW,` load the model weights. It only affects the
model\u2019s configuration. Use `),i5e=n(ZW,"CODE",{});var N3t=s(i5e);bMr=r(N3t,"from_pretrained()"),N3t.forEach(t),vMr=r(ZW,"to load the model weights."),ZW.forEach(t),TMr=i(Fi),d5e=n(Fi,"P",{});var q3t=s(d5e);FMr=r(q3t,"Examples:"),q3t.forEach(t),CMr=i(Fi),m(VA.$$.fragment,Fi),Fi.forEach(t),MMr=i(Ti),ko=n(Ti,"DIV",{class:!0});var Ra=s(ko);m(zA.$$.fragment,Ra),EMr=i(Ra),c5e=n(Ra,"P",{});var O3t=s(c5e);yMr=r(O3t,"Instantiate one of the model classes of the library (with a sequence-to-sequence language modeling head) from a pretrained model."),O3t.forEach(t),wMr=i(Ra),Sn=n(Ra,"P",{});var NM=s(Sn);AMr=r(NM,"The model class to instantiate is selected based on the "),f5e=n(NM,"CODE",{});var G3t=s(f5e);LMr=r(G3t,"model_type"),G3t.forEach(t),BMr=r(NM,` property of the config object (either
passed as an argument or loaded from `),m5e=n(NM,"CODE",{});var X3t=s(m5e);xMr=r(X3t,"pretrained_model_name_or_path"),X3t.forEach(t),kMr=r(NM,` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),g5e=n(NM,"CODE",{});var V3t=s(g5e);RMr=r(V3t,"pretrained_model_name_or_path"),V3t.forEach(t),SMr=r(NM,":"),NM.forEach(t),PMr=i(Ra),Me=n(Ra,"UL",{});var lo=s(Me);W9=n(lo,"LI",{});var fBe=s(W9);h5e=n(fBe,"STRONG",{});var z3t=s(h5e);$Mr=r(z3t,"bart"),z3t.forEach(t),IMr=r(fBe," \u2014 "),AV=n(fBe,"A",{href:!0});var W3t=s(AV);jMr=r(W3t,"FlaxBartForConditionalGeneration"),W3t.forEach(t),DMr=r(fBe," (BART model)"),fBe.forEach(t),NMr=i(lo),Q9=n(lo,"LI",{});var mBe=s(Q9);p5e=n(mBe,"STRONG",{});var Q3t=s(p5e);qMr=r(Q3t,"blenderbot"),Q3t.forEach(t),OMr=r(mBe," \u2014 "),LV=n(mBe,"A",{href:!0});var H3t=s(LV);GMr=r(H3t,"FlaxBlenderbotForConditionalGeneration"),H3t.forEach(t),XMr=r(mBe," (Blenderbot model)"),mBe.forEach(t),VMr=i(lo),H9=n(lo,"LI",{});var gBe=s(H9);_5e=n(gBe,"STRONG",{});var U3t=s(_5e);zMr=r(U3t,"blenderbot-small"),U3t.forEach(t),WMr=r(gBe," \u2014 "),BV=n(gBe,"A",{href:!0});var J3t=s(BV);QMr=r(J3t,"FlaxBlenderbotSmallForConditionalGeneration"),J3t.forEach(t),HMr=r(gBe," (BlenderbotSmall model)"),gBe.forEach(t),UMr=i(lo),U9=n(lo,"LI",{});var hBe=s(U9);u5e=n(hBe,"STRONG",{});var Y3t=s(u5e);JMr=r(Y3t,"encoder-decoder"),Y3t.forEach(t),YMr=r(hBe," \u2014 "),xV=n(hBe,"A",{href:!0});var K3t=s(xV);KMr=r(K3t,"FlaxEncoderDecoderModel"),K3t.forEach(t),ZMr=r(hBe," (Encoder decoder model)"),hBe.forEach(t),e4r=i(lo),J9=n(lo,"LI",{});var pBe=s(J9);b5e=n(pBe,"STRONG",{});var Z3t=s(b5e);o4r=r(Z3t,"marian"),Z3t.forEach(t),r4r=r(pBe," \u2014 "),kV=n(pBe,"A",{href:!0});var e5t=s(kV);t4r=r(e5t,"FlaxMarianMTModel"),e5t.forEach(t),a4r=r(pBe," (Marian model)"),pBe.forEach(t),n4r=i(lo),Y9=n(lo,"LI",{});var _Be=s(Y9);v5e=n(_Be,"STRONG",{});var o5t=s(v5e);s4r=r(o5t,"mbart"),o5t.forEach(t),l4r=r(_Be," \u2014 "),RV=n(_Be,"A",{href:!0});var r5t=s(RV);i4r=r(r5t,"FlaxMBartForConditionalGeneration"),r5t.forEach(t),d4r=r(_Be," (mBART model)"),_Be.forEach(t),c4r=i(lo),K9=n(lo,"LI",{});var uBe=s(K9);T5e=n(uBe,"STRONG",{});var t5t=s(T5e);f4r=r(t5t,"mt5"),t5t.forEach(t),m4r=r(uBe," \u2014 "),SV=n(uBe,"A",{href:!0});var a5t=s(SV);g4r=r(a5t,"FlaxMT5ForConditionalGeneration"),a5t.forEach(t),h4r=r(uBe," (mT5 model)"),uBe.forEach(t),p4r=i(lo),Z9=n(lo,"LI",{});var bBe=s(Z9);F5e=n(bBe,"STRONG",{});var n5t=s(F5e);_4r=r(n5t,"pegasus"),n5t.forEach(t),u4r=r(bBe," \u2014 "),PV=n(bBe,"A",{href:!0});var s5t=s(PV);b4r=r(s5t,"FlaxPegasusForConditionalGeneration"),s5t.forEach(t),v4r=r(bBe," (Pegasus model)"),bBe.forEach(t),T4r=i(lo),eC=n(lo,"LI",{});var vBe=s(eC);C5e=n(vBe,"STRONG",{});var l5t=s(C5e);F4r=r(l5t,"t5"),l5t.forEach(t),C4r=r(vBe," \u2014 "),$V=n(vBe,"A",{href:!0});var i5t=s($V);M4r=r(i5t,"FlaxT5ForConditionalGeneration"),i5t.forEach(t),E4r=r(vBe," (T5 model)"),vBe.forEach(t),lo.forEach(t),y4r=i(Ra),M5e=n(Ra,"P",{});var d5t=s(M5e);w4r=r(d5t,"Examples:"),d5t.forEach(t),A4r=i(Ra),m(WA.$$.fragment,Ra),Ra.forEach(t),Ti.forEach(t),Qke=i(d),hf=n(d,"H2",{class:!0});var nPe=s(hf);oC=n(nPe,"A",{id:!0,class:!0,href:!0});var c5t=s(oC);E5e=n(c5t,"SPAN",{});var f5t=s(E5e);m(QA.$$.fragment,f5t),f5t.forEach(t),c5t.forEach(t),L4r=i(nPe),y5e=n(nPe,"SPAN",{});var m5t=s(y5e);B4r=r(m5t,"FlaxAutoModelForSequenceClassification"),m5t.forEach(t),nPe.forEach(t),Hke=i(d),jr=n(d,"DIV",{class:!0});var Ci=s(jr);m(HA.$$.fragment,Ci),x4r=i(Ci),pf=n(Ci,"P",{});var eQ=s(pf);k4r=r(eQ,`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 `),w5e=n(eQ,"CODE",{});var g5t=s(w5e);R4r=r(g5t,"from_pretrained()"),g5t.forEach(t),S4r=r(eQ,"class method or the "),A5e=n(eQ,"CODE",{});var h5t=s(A5e);P4r=r(h5t,"from_config()"),h5t.forEach(t),$4r=r(eQ,`class
method.`),eQ.forEach(t),I4r=i(Ci),UA=n(Ci,"P",{});var sPe=s(UA);j4r=r(sPe,"This class cannot be instantiated directly using "),L5e=n(sPe,"CODE",{});var p5t=s(L5e);D4r=r(p5t,"__init__()"),p5t.forEach(t),N4r=r(sPe," (throws an error)."),sPe.forEach(t),q4r=i(Ci),kt=n(Ci,"DIV",{class:!0});var Mi=s(kt);m(JA.$$.fragment,Mi),O4r=i(Mi),B5e=n(Mi,"P",{});var _5t=s(B5e);G4r=r(_5t,"Instantiates one of the model classes of the library (with a sequence classification head) from a configuration."),_5t.forEach(t),X4r=i(Mi),_f=n(Mi,"P",{});var oQ=s(_f);V4r=r(oQ,`Note:
Loading a model from its configuration file does `),x5e=n(oQ,"STRONG",{});var u5t=s(x5e);z4r=r(u5t,"not"),u5t.forEach(t),W4r=r(oQ,` load the model weights. It only affects the
model\u2019s configuration. Use `),k5e=n(oQ,"CODE",{});var b5t=s(k5e);Q4r=r(b5t,"from_pretrained()"),b5t.forEach(t),H4r=r(oQ,"to load the model weights."),oQ.forEach(t),U4r=i(Mi),R5e=n(Mi,"P",{});var v5t=s(R5e);J4r=r(v5t,"Examples:"),v5t.forEach(t),Y4r=i(Mi),m(YA.$$.fragment,Mi),Mi.forEach(t),K4r=i(Ci),Ro=n(Ci,"DIV",{class:!0});var Sa=s(Ro);m(KA.$$.fragment,Sa),Z4r=i(Sa),S5e=n(Sa,"P",{});var T5t=s(S5e);eEr=r(T5t,"Instantiate one of the model classes of the library (with a sequence classification head) from a pretrained model."),T5t.forEach(t),oEr=i(Sa),Pn=n(Sa,"P",{});var qM=s(Pn);rEr=r(qM,"The model class to instantiate is selected based on the "),P5e=n(qM,"CODE",{});var F5t=s(P5e);tEr=r(F5t,"model_type"),F5t.forEach(t),aEr=r(qM,` property of the config object (either
passed as an argument or loaded from `),$5e=n(qM,"CODE",{});var C5t=s($5e);nEr=r(C5t,"pretrained_model_name_or_path"),C5t.forEach(t),sEr=r(qM,` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),I5e=n(qM,"CODE",{});var M5t=s(I5e);lEr=r(M5t,"pretrained_model_name_or_path"),M5t.forEach(t),iEr=r(qM,":"),qM.forEach(t),dEr=i(Sa),be=n(Sa,"UL",{});var we=s(be);rC=n(we,"LI",{});var TBe=s(rC);j5e=n(TBe,"STRONG",{});var E5t=s(j5e);cEr=r(E5t,"albert"),E5t.forEach(t),fEr=r(TBe," \u2014 "),IV=n(TBe,"A",{href:!0});var y5t=s(IV);mEr=r(y5t,"FlaxAlbertForSequenceClassification"),y5t.forEach(t),gEr=r(TBe," (ALBERT model)"),TBe.forEach(t),hEr=i(we),tC=n(we,"LI",{});var FBe=s(tC);D5e=n(FBe,"STRONG",{});var w5t=s(D5e);pEr=r(w5t,"bart"),w5t.forEach(t),_Er=r(FBe," \u2014 "),jV=n(FBe,"A",{href:!0});var A5t=s(jV);uEr=r(A5t,"FlaxBartForSequenceClassification"),A5t.forEach(t),bEr=r(FBe," (BART model)"),FBe.forEach(t),vEr=i(we),aC=n(we,"LI",{});var CBe=s(aC);N5e=n(CBe,"STRONG",{});var L5t=s(N5e);TEr=r(L5t,"bert"),L5t.forEach(t),FEr=r(CBe," \u2014 "),DV=n(CBe,"A",{href:!0});var B5t=s(DV);CEr=r(B5t,"FlaxBertForSequenceClassification"),B5t.forEach(t),MEr=r(CBe," (BERT model)"),CBe.forEach(t),EEr=i(we),nC=n(we,"LI",{});var MBe=s(nC);q5e=n(MBe,"STRONG",{});var x5t=s(q5e);yEr=r(x5t,"big_bird"),x5t.forEach(t),wEr=r(MBe," \u2014 "),NV=n(MBe,"A",{href:!0});var k5t=s(NV);AEr=r(k5t,"FlaxBigBirdForSequenceClassification"),k5t.forEach(t),LEr=r(MBe," (BigBird model)"),MBe.forEach(t),BEr=i(we),sC=n(we,"LI",{});var EBe=s(sC);O5e=n(EBe,"STRONG",{});var R5t=s(O5e);xEr=r(R5t,"distilbert"),R5t.forEach(t),kEr=r(EBe," \u2014 "),qV=n(EBe,"A",{href:!0});var S5t=s(qV);REr=r(S5t,"FlaxDistilBertForSequenceClassification"),S5t.forEach(t),SEr=r(EBe," (DistilBERT model)"),EBe.forEach(t),PEr=i(we),lC=n(we,"LI",{});var yBe=s(lC);G5e=n(yBe,"STRONG",{});var P5t=s(G5e);$Er=r(P5t,"electra"),P5t.forEach(t),IEr=r(yBe," \u2014 "),OV=n(yBe,"A",{href:!0});var $5t=s(OV);jEr=r($5t,"FlaxElectraForSequenceClassification"),$5t.forEach(t),DEr=r(yBe," (ELECTRA model)"),yBe.forEach(t),NEr=i(we),iC=n(we,"LI",{});var wBe=s(iC);X5e=n(wBe,"STRONG",{});var I5t=s(X5e);qEr=r(I5t,"mbart"),I5t.forEach(t),OEr=r(wBe," \u2014 "),GV=n(wBe,"A",{href:!0});var j5t=s(GV);GEr=r(j5t,"FlaxMBartForSequenceClassification"),j5t.forEach(t),XEr=r(wBe," (mBART model)"),wBe.forEach(t),VEr=i(we),dC=n(we,"LI",{});var ABe=s(dC);V5e=n(ABe,"STRONG",{});var D5t=s(V5e);zEr=r(D5t,"roberta"),D5t.forEach(t),WEr=r(ABe," \u2014 "),XV=n(ABe,"A",{href:!0});var N5t=s(XV);QEr=r(N5t,"FlaxRobertaForSequenceClassification"),N5t.forEach(t),HEr=r(ABe," (RoBERTa model)"),ABe.forEach(t),UEr=i(we),cC=n(we,"LI",{});var LBe=s(cC);z5e=n(LBe,"STRONG",{});var q5t=s(z5e);JEr=r(q5t,"roformer"),q5t.forEach(t),YEr=r(LBe," \u2014 "),VV=n(LBe,"A",{href:!0});var O5t=s(VV);KEr=r(O5t,"FlaxRoFormerForSequenceClassification"),O5t.forEach(t),ZEr=r(LBe," (RoFormer model)"),LBe.forEach(t),eyr=i(we),fC=n(we,"LI",{});var BBe=s(fC);W5e=n(BBe,"STRONG",{});var G5t=s(W5e);oyr=r(G5t,"xlm-roberta"),G5t.forEach(t),ryr=r(BBe," \u2014 "),zV=n(BBe,"A",{href:!0});var X5t=s(zV);tyr=r(X5t,"FlaxXLMRobertaForSequenceClassification"),X5t.forEach(t),ayr=r(BBe," (XLM-RoBERTa model)"),BBe.forEach(t),we.forEach(t),nyr=i(Sa),Q5e=n(Sa,"P",{});var V5t=s(Q5e);syr=r(V5t,"Examples:"),V5t.forEach(t),lyr=i(Sa),m(ZA.$$.fragment,Sa),Sa.forEach(t),Ci.forEach(t),Uke=i(d),uf=n(d,"H2",{class:!0});var lPe=s(uf);mC=n(lPe,"A",{id:!0,class:!0,href:!0});var z5t=s(mC);H5e=n(z5t,"SPAN",{});var W5t=s(H5e);m(e0.$$.fragment,W5t),W5t.forEach(t),z5t.forEach(t),iyr=i(lPe),U5e=n(lPe,"SPAN",{});var Q5t=s(U5e);dyr=r(Q5t,"FlaxAutoModelForQuestionAnswering"),Q5t.forEach(t),lPe.forEach(t),Jke=i(d),Dr=n(d,"DIV",{class:!0});var Ei=s(Dr);m(o0.$$.fragment,Ei),cyr=i(Ei),bf=n(Ei,"P",{});var rQ=s(bf);fyr=r(rQ,`This is a generic model class that will be instantiated as one of the model classes of the library (with a question answering head) when created
with the `),J5e=n(rQ,"CODE",{});var H5t=s(J5e);myr=r(H5t,"from_pretrained()"),H5t.forEach(t),gyr=r(rQ,"class method or the "),Y5e=n(rQ,"CODE",{});var U5t=s(Y5e);hyr=r(U5t,"from_config()"),U5t.forEach(t),pyr=r(rQ,`class
method.`),rQ.forEach(t),_yr=i(Ei),r0=n(Ei,"P",{});var iPe=s(r0);uyr=r(iPe,"This class cannot be instantiated directly using "),K5e=n(iPe,"CODE",{});var J5t=s(K5e);byr=r(J5t,"__init__()"),J5t.forEach(t),vyr=r(iPe," (throws an error)."),iPe.forEach(t),Tyr=i(Ei),Rt=n(Ei,"DIV",{class:!0});var yi=s(Rt);m(t0.$$.fragment,yi),Fyr=i(yi),Z5e=n(yi,"P",{});var Y5t=s(Z5e);Cyr=r(Y5t,"Instantiates one of the model classes of the library (with a question answering head) from a configuration."),Y5t.forEach(t),Myr=i(yi),vf=n(yi,"P",{});var tQ=s(vf);Eyr=r(tQ,`Note:
Loading a model from its configuration file does `),e2e=n(tQ,"STRONG",{});var K5t=s(e2e);yyr=r(K5t,"not"),K5t.forEach(t),wyr=r(tQ,` load the model weights. It only affects the
model\u2019s configuration. Use `),o2e=n(tQ,"CODE",{});var Z5t=s(o2e);Ayr=r(Z5t,"from_pretrained()"),Z5t.forEach(t),Lyr=r(tQ,"to load the model weights."),tQ.forEach(t),Byr=i(yi),r2e=n(yi,"P",{});var e2t=s(r2e);xyr=r(e2t,"Examples:"),e2t.forEach(t),kyr=i(yi),m(a0.$$.fragment,yi),yi.forEach(t),Ryr=i(Ei),So=n(Ei,"DIV",{class:!0});var Pa=s(So);m(n0.$$.fragment,Pa),Syr=i(Pa),t2e=n(Pa,"P",{});var o2t=s(t2e);Pyr=r(o2t,"Instantiate one of the model classes of the library (with a question answering head) from a pretrained model."),o2t.forEach(t),$yr=i(Pa),$n=n(Pa,"P",{});var OM=s($n);Iyr=r(OM,"The model class to instantiate is selected based on the "),a2e=n(OM,"CODE",{});var r2t=s(a2e);jyr=r(r2t,"model_type"),r2t.forEach(t),Dyr=r(OM,` property of the config object (either
passed as an argument or loaded from `),n2e=n(OM,"CODE",{});var t2t=s(n2e);Nyr=r(t2t,"pretrained_model_name_or_path"),t2t.forEach(t),qyr=r(OM,` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),s2e=n(OM,"CODE",{});var a2t=s(s2e);Oyr=r(a2t,"pretrained_model_name_or_path"),a2t.forEach(t),Gyr=r(OM,":"),OM.forEach(t),Xyr=i(Pa),ve=n(Pa,"UL",{});var Ae=s(ve);gC=n(Ae,"LI",{});var xBe=s(gC);l2e=n(xBe,"STRONG",{});var n2t=s(l2e);Vyr=r(n2t,"albert"),n2t.forEach(t),zyr=r(xBe," \u2014 "),WV=n(xBe,"A",{href:!0});var s2t=s(WV);Wyr=r(s2t,"FlaxAlbertForQuestionAnswering"),s2t.forEach(t),Qyr=r(xBe," (ALBERT model)"),xBe.forEach(t),Hyr=i(Ae),hC=n(Ae,"LI",{});var kBe=s(hC);i2e=n(kBe,"STRONG",{});var l2t=s(i2e);Uyr=r(l2t,"bart"),l2t.forEach(t),Jyr=r(kBe," \u2014 "),QV=n(kBe,"A",{href:!0});var i2t=s(QV);Yyr=r(i2t,"FlaxBartForQuestionAnswering"),i2t.forEach(t),Kyr=r(kBe," (BART model)"),kBe.forEach(t),Zyr=i(Ae),pC=n(Ae,"LI",{});var RBe=s(pC);d2e=n(RBe,"STRONG",{});var d2t=s(d2e);ewr=r(d2t,"bert"),d2t.forEach(t),owr=r(RBe," \u2014 "),HV=n(RBe,"A",{href:!0});var c2t=s(HV);rwr=r(c2t,"FlaxBertForQuestionAnswering"),c2t.forEach(t),twr=r(RBe," (BERT model)"),RBe.forEach(t),awr=i(Ae),_C=n(Ae,"LI",{});var SBe=s(_C);c2e=n(SBe,"STRONG",{});var f2t=s(c2e);nwr=r(f2t,"big_bird"),f2t.forEach(t),swr=r(SBe," \u2014 "),UV=n(SBe,"A",{href:!0});var m2t=s(UV);lwr=r(m2t,"FlaxBigBirdForQuestionAnswering"),m2t.forEach(t),iwr=r(SBe," (BigBird model)"),SBe.forEach(t),dwr=i(Ae),uC=n(Ae,"LI",{});var PBe=s(uC);f2e=n(PBe,"STRONG",{});var g2t=s(f2e);cwr=r(g2t,"distilbert"),g2t.forEach(t),fwr=r(PBe," \u2014 "),JV=n(PBe,"A",{href:!0});var h2t=s(JV);mwr=r(h2t,"FlaxDistilBertForQuestionAnswering"),h2t.forEach(t),gwr=r(PBe," (DistilBERT model)"),PBe.forEach(t),hwr=i(Ae),bC=n(Ae,"LI",{});var $Be=s(bC);m2e=n($Be,"STRONG",{});var p2t=s(m2e);pwr=r(p2t,"electra"),p2t.forEach(t),_wr=r($Be," \u2014 "),YV=n($Be,"A",{href:!0});var _2t=s(YV);uwr=r(_2t,"FlaxElectraForQuestionAnswering"),_2t.forEach(t),bwr=r($Be," (ELECTRA model)"),$Be.forEach(t),vwr=i(Ae),vC=n(Ae,"LI",{});var IBe=s(vC);g2e=n(IBe,"STRONG",{});var u2t=s(g2e);Twr=r(u2t,"mbart"),u2t.forEach(t),Fwr=r(IBe," \u2014 "),KV=n(IBe,"A",{href:!0});var b2t=s(KV);Cwr=r(b2t,"FlaxMBartForQuestionAnswering"),b2t.forEach(t),Mwr=r(IBe," (mBART model)"),IBe.forEach(t),Ewr=i(Ae),TC=n(Ae,"LI",{});var jBe=s(TC);h2e=n(jBe,"STRONG",{});var v2t=s(h2e);ywr=r(v2t,"roberta"),v2t.forEach(t),wwr=r(jBe," \u2014 "),ZV=n(jBe,"A",{href:!0});var T2t=s(ZV);Awr=r(T2t,"FlaxRobertaForQuestionAnswering"),T2t.forEach(t),Lwr=r(jBe," (RoBERTa model)"),jBe.forEach(t),Bwr=i(Ae),FC=n(Ae,"LI",{});var DBe=s(FC);p2e=n(DBe,"STRONG",{});var F2t=s(p2e);xwr=r(F2t,"roformer"),F2t.forEach(t),kwr=r(DBe," \u2014 "),ez=n(DBe,"A",{href:!0});var C2t=s(ez);Rwr=r(C2t,"FlaxRoFormerForQuestionAnswering"),C2t.forEach(t),Swr=r(DBe," (RoFormer model)"),DBe.forEach(t),Pwr=i(Ae),CC=n(Ae,"LI",{});var NBe=s(CC);_2e=n(NBe,"STRONG",{});var M2t=s(_2e);$wr=r(M2t,"xlm-roberta"),M2t.forEach(t),Iwr=r(NBe," \u2014 "),oz=n(NBe,"A",{href:!0});var E2t=s(oz);jwr=r(E2t,"FlaxXLMRobertaForQuestionAnswering"),E2t.forEach(t),Dwr=r(NBe," (XLM-RoBERTa model)"),NBe.forEach(t),Ae.forEach(t),Nwr=i(Pa),u2e=n(Pa,"P",{});var y2t=s(u2e);qwr=r(y2t,"Examples:"),y2t.forEach(t),Owr=i(Pa),m(s0.$$.fragment,Pa),Pa.forEach(t),Ei.forEach(t),Yke=i(d),Tf=n(d,"H2",{class:!0});var dPe=s(Tf);MC=n(dPe,"A",{id:!0,class:!0,href:!0});var w2t=s(MC);b2e=n(w2t,"SPAN",{});var A2t=s(b2e);m(l0.$$.fragment,A2t),A2t.forEach(t),w2t.forEach(t),Gwr=i(dPe),v2e=n(dPe,"SPAN",{});var L2t=s(v2e);Xwr=r(L2t,"FlaxAutoModelForTokenClassification"),L2t.forEach(t),dPe.forEach(t),Kke=i(d),Nr=n(d,"DIV",{class:!0});var wi=s(Nr);m(i0.$$.fragment,wi),Vwr=i(wi),Ff=n(wi,"P",{});var aQ=s(Ff);zwr=r(aQ,`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 `),T2e=n(aQ,"CODE",{});var B2t=s(T2e);Wwr=r(B2t,"from_pretrained()"),B2t.forEach(t),Qwr=r(aQ,"class method or the "),F2e=n(aQ,"CODE",{});var x2t=s(F2e);Hwr=r(x2t,"from_config()"),x2t.forEach(t),Uwr=r(aQ,`class
method.`),aQ.forEach(t),Jwr=i(wi),d0=n(wi,"P",{});var cPe=s(d0);Ywr=r(cPe,"This class cannot be instantiated directly using "),C2e=n(cPe,"CODE",{});var k2t=s(C2e);Kwr=r(k2t,"__init__()"),k2t.forEach(t),Zwr=r(cPe," (throws an error)."),cPe.forEach(t),e6r=i(wi),St=n(wi,"DIV",{class:!0});var Ai=s(St);m(c0.$$.fragment,Ai),o6r=i(Ai),M2e=n(Ai,"P",{});var R2t=s(M2e);r6r=r(R2t,"Instantiates one of the model classes of the library (with a token classification head) from a configuration."),R2t.forEach(t),t6r=i(Ai),Cf=n(Ai,"P",{});var nQ=s(Cf);a6r=r(nQ,`Note:
Loading a model from its configuration file does `),E2e=n(nQ,"STRONG",{});var S2t=s(E2e);n6r=r(S2t,"not"),S2t.forEach(t),s6r=r(nQ,` load the model weights. It only affects the
model\u2019s configuration. Use `),y2e=n(nQ,"CODE",{});var P2t=s(y2e);l6r=r(P2t,"from_pretrained()"),P2t.forEach(t),i6r=r(nQ,"to load the model weights."),nQ.forEach(t),d6r=i(Ai),w2e=n(Ai,"P",{});var $2t=s(w2e);c6r=r($2t,"Examples:"),$2t.forEach(t),f6r=i(Ai),m(f0.$$.fragment,Ai),Ai.forEach(t),m6r=i(wi),Po=n(wi,"DIV",{class:!0});var $a=s(Po);m(m0.$$.fragment,$a),g6r=i($a),A2e=n($a,"P",{});var I2t=s(A2e);h6r=r(I2t,"Instantiate one of the model classes of the library (with a token classification head) from a pretrained model."),I2t.forEach(t),p6r=i($a),In=n($a,"P",{});var GM=s(In);_6r=r(GM,"The model class to instantiate is selected based on the "),L2e=n(GM,"CODE",{});var j2t=s(L2e);u6r=r(j2t,"model_type"),j2t.forEach(t),b6r=r(GM,` property of the config object (either
passed as an argument or loaded from `),B2e=n(GM,"CODE",{});var D2t=s(B2e);v6r=r(D2t,"pretrained_model_name_or_path"),D2t.forEach(t),T6r=r(GM,` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),x2e=n(GM,"CODE",{});var N2t=s(x2e);F6r=r(N2t,"pretrained_model_name_or_path"),N2t.forEach(t),C6r=r(GM,":"),GM.forEach(t),M6r=i($a),Re=n($a,"UL",{});var Go=s(Re);EC=n(Go,"LI",{});var qBe=s(EC);k2e=n(qBe,"STRONG",{});var q2t=s(k2e);E6r=r(q2t,"albert"),q2t.forEach(t),y6r=r(qBe," \u2014 "),rz=n(qBe,"A",{href:!0});var O2t=s(rz);w6r=r(O2t,"FlaxAlbertForTokenClassification"),O2t.forEach(t),A6r=r(qBe," (ALBERT model)"),qBe.forEach(t),L6r=i(Go),yC=n(Go,"LI",{});var OBe=s(yC);R2e=n(OBe,"STRONG",{});var G2t=s(R2e);B6r=r(G2t,"bert"),G2t.forEach(t),x6r=r(OBe," \u2014 "),tz=n(OBe,"A",{href:!0});var X2t=s(tz);k6r=r(X2t,"FlaxBertForTokenClassification"),X2t.forEach(t),R6r=r(OBe," (BERT model)"),OBe.forEach(t),S6r=i(Go),wC=n(Go,"LI",{});var GBe=s(wC);S2e=n(GBe,"STRONG",{});var V2t=s(S2e);P6r=r(V2t,"big_bird"),V2t.forEach(t),$6r=r(GBe," \u2014 "),az=n(GBe,"A",{href:!0});var z2t=s(az);I6r=r(z2t,"FlaxBigBirdForTokenClassification"),z2t.forEach(t),j6r=r(GBe," (BigBird model)"),GBe.forEach(t),D6r=i(Go),AC=n(Go,"LI",{});var XBe=s(AC);P2e=n(XBe,"STRONG",{});var W2t=s(P2e);N6r=r(W2t,"distilbert"),W2t.forEach(t),q6r=r(XBe," \u2014 "),nz=n(XBe,"A",{href:!0});var Q2t=s(nz);O6r=r(Q2t,"FlaxDistilBertForTokenClassification"),Q2t.forEach(t),G6r=r(XBe," (DistilBERT model)"),XBe.forEach(t),X6r=i(Go),LC=n(Go,"LI",{});var VBe=s(LC);$2e=n(VBe,"STRONG",{});var H2t=s($2e);V6r=r(H2t,"electra"),H2t.forEach(t),z6r=r(VBe," \u2014 "),sz=n(VBe,"A",{href:!0});var U2t=s(sz);W6r=r(U2t,"FlaxElectraForTokenClassification"),U2t.forEach(t),Q6r=r(VBe," (ELECTRA model)"),VBe.forEach(t),H6r=i(Go),BC=n(Go,"LI",{});var zBe=s(BC);I2e=n(zBe,"STRONG",{});var J2t=s(I2e);U6r=r(J2t,"roberta"),J2t.forEach(t),J6r=r(zBe," \u2014 "),lz=n(zBe,"A",{href:!0});var Y2t=s(lz);Y6r=r(Y2t,"FlaxRobertaForTokenClassification"),Y2t.forEach(t),K6r=r(zBe," (RoBERTa model)"),zBe.forEach(t),Z6r=i(Go),xC=n(Go,"LI",{});var WBe=s(xC);j2e=n(WBe,"STRONG",{});var K2t=s(j2e);eAr=r(K2t,"roformer"),K2t.forEach(t),oAr=r(WBe," \u2014 "),iz=n(WBe,"A",{href:!0});var Z2t=s(iz);rAr=r(Z2t,"FlaxRoFormerForTokenClassification"),Z2t.forEach(t),tAr=r(WBe," (RoFormer model)"),WBe.forEach(t),aAr=i(Go),kC=n(Go,"LI",{});var QBe=s(kC);D2e=n(QBe,"STRONG",{});var evt=s(D2e);nAr=r(evt,"xlm-roberta"),evt.forEach(t),sAr=r(QBe," \u2014 "),dz=n(QBe,"A",{href:!0});var ovt=s(dz);lAr=r(ovt,"FlaxXLMRobertaForTokenClassification"),ovt.forEach(t),iAr=r(QBe," (XLM-RoBERTa model)"),QBe.forEach(t),Go.forEach(t),dAr=i($a),N2e=n($a,"P",{});var rvt=s(N2e);cAr=r(rvt,"Examples:"),rvt.forEach(t),fAr=i($a),m(g0.$$.fragment,$a),$a.forEach(t),wi.forEach(t),Zke=i(d),Mf=n(d,"H2",{class:!0});var fPe=s(Mf);RC=n(fPe,"A",{id:!0,class:!0,href:!0});var tvt=s(RC);q2e=n(tvt,"SPAN",{});var avt=s(q2e);m(h0.$$.fragment,avt),avt.forEach(t),tvt.forEach(t),mAr=i(fPe),O2e=n(fPe,"SPAN",{});var nvt=s(O2e);gAr=r(nvt,"FlaxAutoModelForMultipleChoice"),nvt.forEach(t),fPe.forEach(t),eRe=i(d),qr=n(d,"DIV",{class:!0});var Li=s(qr);m(p0.$$.fragment,Li),hAr=i(Li),Ef=n(Li,"P",{});var sQ=s(Ef);pAr=r(sQ,`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 `),G2e=n(sQ,"CODE",{});var svt=s(G2e);_Ar=r(svt,"from_pretrained()"),svt.forEach(t),uAr=r(sQ,"class method or the "),X2e=n(sQ,"CODE",{});var lvt=s(X2e);bAr=r(lvt,"from_config()"),lvt.forEach(t),vAr=r(sQ,`class
method.`),sQ.forEach(t),TAr=i(Li),_0=n(Li,"P",{});var mPe=s(_0);FAr=r(mPe,"This class cannot be instantiated directly using "),V2e=n(mPe,"CODE",{});var ivt=s(V2e);CAr=r(ivt,"__init__()"),ivt.forEach(t),MAr=r(mPe," (throws an error)."),mPe.forEach(t),EAr=i(Li),Pt=n(Li,"DIV",{class:!0});var Bi=s(Pt);m(u0.$$.fragment,Bi),yAr=i(Bi),z2e=n(Bi,"P",{});var dvt=s(z2e);wAr=r(dvt,"Instantiates one of the model classes of the library (with a multiple choice head) from a configuration."),dvt.forEach(t),AAr=i(Bi),yf=n(Bi,"P",{});var lQ=s(yf);LAr=r(lQ,`Note:
Loading a model from its configuration file does `),W2e=n(lQ,"STRONG",{});var cvt=s(W2e);BAr=r(cvt,"not"),cvt.forEach(t),xAr=r(lQ,` load the model weights. It only affects the
model\u2019s configuration. Use `),Q2e=n(lQ,"CODE",{});var fvt=s(Q2e);kAr=r(fvt,"from_pretrained()"),fvt.forEach(t),RAr=r(lQ,"to load the model weights."),lQ.forEach(t),SAr=i(Bi),H2e=n(Bi,"P",{});var mvt=s(H2e);PAr=r(mvt,"Examples:"),mvt.forEach(t),$Ar=i(Bi),m(b0.$$.fragment,Bi),Bi.forEach(t),IAr=i(Li),$o=n(Li,"DIV",{class:!0});var Ia=s($o);m(v0.$$.fragment,Ia),jAr=i(Ia),U2e=n(Ia,"P",{});var gvt=s(U2e);DAr=r(gvt,"Instantiate one of the model classes of the library (with a multiple choice head) from a pretrained model."),gvt.forEach(t),NAr=i(Ia),jn=n(Ia,"P",{});var XM=s(jn);qAr=r(XM,"The model class to instantiate is selected based on the "),J2e=n(XM,"CODE",{});var hvt=s(J2e);OAr=r(hvt,"model_type"),hvt.forEach(t),GAr=r(XM,` property of the config object (either
passed as an argument or loaded from `),Y2e=n(XM,"CODE",{});var pvt=s(Y2e);XAr=r(pvt,"pretrained_model_name_or_path"),pvt.forEach(t),VAr=r(XM,` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),K2e=n(XM,"CODE",{});var _vt=s(K2e);zAr=r(_vt,"pretrained_model_name_or_path"),_vt.forEach(t),WAr=r(XM,":"),XM.forEach(t),QAr=i(Ia),Se=n(Ia,"UL",{});var Xo=s(Se);SC=n(Xo,"LI",{});var HBe=s(SC);Z2e=n(HBe,"STRONG",{});var uvt=s(Z2e);HAr=r(uvt,"albert"),uvt.forEach(t),UAr=r(HBe," \u2014 "),cz=n(HBe,"A",{href:!0});var bvt=s(cz);JAr=r(bvt,"FlaxAlbertForMultipleChoice"),bvt.forEach(t),YAr=r(HBe," (ALBERT model)"),HBe.forEach(t),KAr=i(Xo),PC=n(Xo,"LI",{});var UBe=s(PC);eve=n(UBe,"STRONG",{});var vvt=s(eve);ZAr=r(vvt,"bert"),vvt.forEach(t),e0r=r(UBe," \u2014 "),fz=n(UBe,"A",{href:!0});var Tvt=s(fz);o0r=r(Tvt,"FlaxBertForMultipleChoice"),Tvt.forEach(t),r0r=r(UBe," (BERT model)"),UBe.forEach(t),t0r=i(Xo),$C=n(Xo,"LI",{});var JBe=s($C);ove=n(JBe,"STRONG",{});var Fvt=s(ove);a0r=r(Fvt,"big_bird"),Fvt.forEach(t),n0r=r(JBe," \u2014 "),mz=n(JBe,"A",{href:!0});var Cvt=s(mz);s0r=r(Cvt,"FlaxBigBirdForMultipleChoice"),Cvt.forEach(t),l0r=r(JBe," (BigBird model)"),JBe.forEach(t),i0r=i(Xo),IC=n(Xo,"LI",{});var YBe=s(IC);rve=n(YBe,"STRONG",{});var Mvt=s(rve);d0r=r(Mvt,"distilbert"),Mvt.forEach(t),c0r=r(YBe," \u2014 "),gz=n(YBe,"A",{href:!0});var Evt=s(gz);f0r=r(Evt,"FlaxDistilBertForMultipleChoice"),Evt.forEach(t),m0r=r(YBe," (DistilBERT model)"),YBe.forEach(t),g0r=i(Xo),jC=n(Xo,"LI",{});var KBe=s(jC);tve=n(KBe,"STRONG",{});var yvt=s(tve);h0r=r(yvt,"electra"),yvt.forEach(t),p0r=r(KBe," \u2014 "),hz=n(KBe,"A",{href:!0});var wvt=s(hz);_0r=r(wvt,"FlaxElectraForMultipleChoice"),wvt.forEach(t),u0r=r(KBe," (ELECTRA model)"),KBe.forEach(t),b0r=i(Xo),DC=n(Xo,"LI",{});var ZBe=s(DC);ave=n(ZBe,"STRONG",{});var Avt=s(ave);v0r=r(Avt,"roberta"),Avt.forEach(t),T0r=r(ZBe," \u2014 "),pz=n(ZBe,"A",{href:!0});var Lvt=s(pz);F0r=r(Lvt,"FlaxRobertaForMultipleChoice"),Lvt.forEach(t),C0r=r(ZBe," (RoBERTa model)"),ZBe.forEach(t),M0r=i(Xo),NC=n(Xo,"LI",{});var exe=s(NC);nve=n(exe,"STRONG",{});var Bvt=s(nve);E0r=r(Bvt,"roformer"),Bvt.forEach(t),y0r=r(exe," \u2014 "),_z=n(exe,"A",{href:!0});var xvt=s(_z);w0r=r(xvt,"FlaxRoFormerForMultipleChoice"),xvt.forEach(t),A0r=r(exe," (RoFormer model)"),exe.forEach(t),L0r=i(Xo),qC=n(Xo,"LI",{});var oxe=s(qC);sve=n(oxe,"STRONG",{});var kvt=s(sve);B0r=r(kvt,"xlm-roberta"),kvt.forEach(t),x0r=r(oxe," \u2014 "),uz=n(oxe,"A",{href:!0});var Rvt=s(uz);k0r=r(Rvt,"FlaxXLMRobertaForMultipleChoice"),Rvt.forEach(t),R0r=r(oxe," (XLM-RoBERTa model)"),oxe.forEach(t),Xo.forEach(t),S0r=i(Ia),lve=n(Ia,"P",{});var Svt=s(lve);P0r=r(Svt,"Examples:"),Svt.forEach(t),$0r=i(Ia),m(T0.$$.fragment,Ia),Ia.forEach(t),Li.forEach(t),oRe=i(d),wf=n(d,"H2",{class:!0});var gPe=s(wf);OC=n(gPe,"A",{id:!0,class:!0,href:!0});var Pvt=s(OC);ive=n(Pvt,"SPAN",{});var $vt=s(ive);m(F0.$$.fragment,$vt),$vt.forEach(t),Pvt.forEach(t),I0r=i(gPe),dve=n(gPe,"SPAN",{});var Ivt=s(dve);j0r=r(Ivt,"FlaxAutoModelForNextSentencePrediction"),Ivt.forEach(t),gPe.forEach(t),rRe=i(d),Or=n(d,"DIV",{class:!0});var xi=s(Or);m(C0.$$.fragment,xi),D0r=i(xi),Af=n(xi,"P",{});var iQ=s(Af);N0r=r(iQ,`This is a generic model class that will be instantiated as one of the model classes of the library (with a next sentence prediction head) when created
with the `),cve=n(iQ,"CODE",{});var jvt=s(cve);q0r=r(jvt,"from_pretrained()"),jvt.forEach(t),O0r=r(iQ,"class method or the "),fve=n(iQ,"CODE",{});var Dvt=s(fve);G0r=r(Dvt,"from_config()"),Dvt.forEach(t),X0r=r(iQ,`class
method.`),iQ.forEach(t),V0r=i(xi),M0=n(xi,"P",{});var hPe=s(M0);z0r=r(hPe,"This class cannot be instantiated directly using "),mve=n(hPe,"CODE",{});var Nvt=s(mve);W0r=r(Nvt,"__init__()"),Nvt.forEach(t),Q0r=r(hPe," (throws an error)."),hPe.forEach(t),H0r=i(xi),$t=n(xi,"DIV",{class:!0});var ki=s($t);m(E0.$$.fragment,ki),U0r=i(ki),gve=n(ki,"P",{});var qvt=s(gve);J0r=r(qvt,"Instantiates one of the model classes of the library (with a next sentence prediction head) from a configuration."),qvt.forEach(t),Y0r=i(ki),Lf=n(ki,"P",{});var dQ=s(Lf);K0r=r(dQ,`Note:
Loading a model from its configuration file does `),hve=n(dQ,"STRONG",{});var Ovt=s(hve);Z0r=r(Ovt,"not"),Ovt.forEach(t),eLr=r(dQ,` load the model weights. It only affects the
model\u2019s configuration. Use `),pve=n(dQ,"CODE",{});var Gvt=s(pve);oLr=r(Gvt,"from_pretrained()"),Gvt.forEach(t),rLr=r(dQ,"to load the model weights."),dQ.forEach(t),tLr=i(ki),_ve=n(ki,"P",{});var Xvt=s(_ve);aLr=r(Xvt,"Examples:"),Xvt.forEach(t),nLr=i(ki),m(y0.$$.fragment,ki),ki.forEach(t),sLr=i(xi),Io=n(xi,"DIV",{class:!0});var ja=s(Io);m(w0.$$.fragment,ja),lLr=i(ja),uve=n(ja,"P",{});var Vvt=s(uve);iLr=r(Vvt,"Instantiate one of the model classes of the library (with a next sentence prediction head) from a pretrained model."),Vvt.forEach(t),dLr=i(ja),Dn=n(ja,"P",{});var VM=s(Dn);cLr=r(VM,"The model class to instantiate is selected based on the "),bve=n(VM,"CODE",{});var zvt=s(bve);fLr=r(zvt,"model_type"),zvt.forEach(t),mLr=r(VM,` property of the config object (either
passed as an argument or loaded from `),vve=n(VM,"CODE",{});var Wvt=s(vve);gLr=r(Wvt,"pretrained_model_name_or_path"),Wvt.forEach(t),hLr=r(VM,` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),Tve=n(VM,"CODE",{});var Qvt=s(Tve);pLr=r(Qvt,"pretrained_model_name_or_path"),Qvt.forEach(t),_Lr=r(VM,":"),VM.forEach(t),uLr=i(ja),Fve=n(ja,"UL",{});var Hvt=s(Fve);GC=n(Hvt,"LI",{});var rxe=s(GC);Cve=n(rxe,"STRONG",{});var Uvt=s(Cve);bLr=r(Uvt,"bert"),Uvt.forEach(t),vLr=r(rxe," \u2014 "),bz=n(rxe,"A",{href:!0});var Jvt=s(bz);TLr=r(Jvt,"FlaxBertForNextSentencePrediction"),Jvt.forEach(t),FLr=r(rxe," (BERT model)"),rxe.forEach(t),Hvt.forEach(t),CLr=i(ja),Mve=n(ja,"P",{});var Yvt=s(Mve);MLr=r(Yvt,"Examples:"),Yvt.forEach(t),ELr=i(ja),m(A0.$$.fragment,ja),ja.forEach(t),xi.forEach(t),tRe=i(d),Bf=n(d,"H2",{class:!0});var pPe=s(Bf);XC=n(pPe,"A",{id:!0,class:!0,href:!0});var Kvt=s(XC);Eve=n(Kvt,"SPAN",{});var Zvt=s(Eve);m(L0.$$.fragment,Zvt),Zvt.forEach(t),Kvt.forEach(t),yLr=i(pPe),yve=n(pPe,"SPAN",{});var eTt=s(yve);wLr=r(eTt,"FlaxAutoModelForImageClassification"),eTt.forEach(t),pPe.forEach(t),aRe=i(d),Gr=n(d,"DIV",{class:!0});var Ri=s(Gr);m(B0.$$.fragment,Ri),ALr=i(Ri),xf=n(Ri,"P",{});var cQ=s(xf);LLr=r(cQ,`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 `),wve=n(cQ,"CODE",{});var oTt=s(wve);BLr=r(oTt,"from_pretrained()"),oTt.forEach(t),xLr=r(cQ,"class method or the "),Ave=n(cQ,"CODE",{});var rTt=s(Ave);kLr=r(rTt,"from_config()"),rTt.forEach(t),RLr=r(cQ,`class
method.`),cQ.forEach(t),SLr=i(Ri),x0=n(Ri,"P",{});var _Pe=s(x0);PLr=r(_Pe,"This class cannot be instantiated directly using "),Lve=n(_Pe,"CODE",{});var tTt=s(Lve);$Lr=r(tTt,"__init__()"),tTt.forEach(t),ILr=r(_Pe," (throws an error)."),_Pe.forEach(t),jLr=i(Ri),It=n(Ri,"DIV",{class:!0});var Si=s(It);m(k0.$$.fragment,Si),DLr=i(Si),Bve=n(Si,"P",{});var aTt=s(Bve);NLr=r(aTt,"Instantiates one of the model classes of the library (with a image classification head) from a configuration."),aTt.forEach(t),qLr=i(Si),kf=n(Si,"P",{});var fQ=s(kf);OLr=r(fQ,`Note:
Loading a model from its configuration file does `),xve=n(fQ,"STRONG",{});var nTt=s(xve);GLr=r(nTt,"not"),nTt.forEach(t),XLr=r(fQ,` load the model weights. It only affects the
model\u2019s configuration. Use `),kve=n(fQ,"CODE",{});var sTt=s(kve);VLr=r(sTt,"from_pretrained()"),sTt.forEach(t),zLr=r(fQ,"to load the model weights."),fQ.forEach(t),WLr=i(Si),Rve=n(Si,"P",{});var lTt=s(Rve);QLr=r(lTt,"Examples:"),lTt.forEach(t),HLr=i(Si),m(R0.$$.fragment,Si),Si.forEach(t),ULr=i(Ri),jo=n(Ri,"DIV",{class:!0});var Da=s(jo);m(S0.$$.fragment,Da),JLr=i(Da),Sve=n(Da,"P",{});var iTt=s(Sve);YLr=r(iTt,"Instantiate one of the model classes of the library (with a image classification head) from a pretrained model."),iTt.forEach(t),KLr=i(Da),Nn=n(Da,"P",{});var zM=s(Nn);ZLr=r(zM,"The model class to instantiate is selected based on the "),Pve=n(zM,"CODE",{});var dTt=s(Pve);e7r=r(dTt,"model_type"),dTt.forEach(t),o7r=r(zM,` property of the config object (either
passed as an argument or loaded from `),$ve=n(zM,"CODE",{});var cTt=s($ve);r7r=r(cTt,"pretrained_model_name_or_path"),cTt.forEach(t),t7r=r(zM,` if possible), or when it\u2019s missing, by
falling back to using pattern matching on `),Ive=n(zM,"CODE",{});var fTt=s(Ive);a7r=r(fTt,"pretrained_model_name_or_path"),fTt.forEach(t),n7r=r(zM,":"),zM.forEach(t),s7r=i(Da),P0=n(Da,"UL",{});var uPe=s(P0);VC=n(uPe,"LI",{});var txe=s(VC);jve=n(txe,"STRONG",{});var mTt=s(jve);l7r=r(mTt,"beit"),mTt.forEach(t),i7r=r(txe," \u2014 "),vz=n(txe,"A",{href:!0});var gTt=s(vz);d7r=r(gTt,"FlaxBeitForImageClassification"),gTt.forEach(t),c7r=r(txe," (BEiT model)"),txe.forEach(t),f7r=i(uPe),zC=n(uPe,"LI",{});var axe=s(zC);Dve=n(axe,"STRONG",{});var hTt=s(Dve);m7r=r(hTt,"vit"),hTt.forEach(t),g7r=r(axe," \u2014 "),Tz=n(axe,"A",{href:!0});var pTt=s(Tz);h7r=r(pTt,"FlaxViTForImageClassification"),pTt.forEach(t),p7r=r(axe," (ViT model)"),axe.forEach(t),uPe.forEach(t),_7r=i(Da),Nve=n(Da,"P",{});var _Tt=s(Nve);u7r=r(_Tt,"Examples:"),_Tt.forEach(t),b7r=i(Da),m($0.$$.fragment,Da),Da.forEach(t),Ri.forEach(t),nRe=i(d),Rf=n(d,"H2",{class:!0});var bPe=s(Rf);WC=n(bPe,"A",{id:!0,class:!0,href:!0});var uTt=s(WC);qve=n(uTt,"SPAN",{});var bTt=s(qve);m(I0.$$.fragment,bTt),bTt.forEach(t),uTt.forEach(t),v7r=i(bPe),Ove=n(bPe,"SPAN",{});var vTt=s(Ove);T7r=r(vTt,"FlaxAutoModelForVision2Seq"),vTt.forEach(t),bPe.forEach(t),sRe=i(d),Xr=n(d,"DIV",{class:!0});var Pi=s(Xr);m(j0.$$.fragment,Pi),F7r=i(Pi),Sf=n(Pi,"P",{});var mQ=s(Sf);C7r=r(mQ,`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 `),Gve=n(mQ,"CODE",{});var TTt=s(Gve);M7r=r(TTt,"from_pretrained()"),TTt.forEach(t),E7r=r(mQ,"class method or the "),Xve=n(mQ,"CODE",{});var FTt=s(Xve);y7r=r(FTt,"from_config()"),FTt.forEach(t),w7r=r(mQ,`class
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