import{s as Ta,o as Ca,n as jt}from"../chunks/scheduler.9bc65507.js";import{S as Pa,i as Ua,g as l,s,r as c,A as Ia,h as i,f as t,c as a,j as x,u as f,x as d,k as b,y as n,a as o,v as u,d as h,t as g,w as _}from"../chunks/index.24549926.js";import{T as Is}from"../chunks/Tip.2b78d0ae.js";import{D as w}from"../chunks/Docstring.faf778e5.js";import{C as Et}from"../chunks/CodeBlock.6ca813ee.js";import{E as Xs}from"../chunks/ExampleCodeBlock.e4699344.js";import{H as ie}from"../chunks/Heading.cc20227a.js";function Xa(Z){let m,y=`This class method is simply calling the feature extractor
<a href="/docs/transformers/main/zh/main_classes/feature_extractor#transformers.FeatureExtractionMixin.from_pretrained">from_pretrained()</a>, image processor
<a href="/docs/transformers/main/zh/main_classes/image_processor#transformers.ImageProcessingMixin">ImageProcessingMixin</a> and the tokenizer
<code>~tokenization_utils_base.PreTrainedTokenizer.from_pretrained</code> methods. Please refer to the docstrings of the
methods above for more information.`;return{c(){m=l("p"),m.innerHTML=y},l(v){m=i(v,"P",{"data-svelte-h":!0}),d(m)!=="svelte-1numcrz"&&(m.innerHTML=y)},m(v,$){o(v,m,$)},p:jt,d(v){v&&t(m)}}}function Za(Z){let m,y="Examples:",v,$,M;return $=new Et({props:{code:"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",highlighted:`<span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoProcessor

processor = AutoProcessor.from_pretrained(<span class="hljs-string">&quot;bert-base-cased&quot;</span>)

<span class="hljs-comment"># Push the processor to your namespace with the name &quot;my-finetuned-bert&quot;.</span>
processor.push_to_hub(<span class="hljs-string">&quot;my-finetuned-bert&quot;</span>)

<span class="hljs-comment"># Push the processor to an organization with the name &quot;my-finetuned-bert&quot;.</span>
processor.push_to_hub(<span class="hljs-string">&quot;huggingface/my-finetuned-bert&quot;</span>)`,wrap:!1}}),{c(){m=l("p"),m.textContent=y,v=s(),c($.$$.fragment)},l(p){m=i(p,"P",{"data-svelte-h":!0}),d(m)!=="svelte-kvfsh7"&&(m.textContent=y),v=a(p),f($.$$.fragment,p)},m(p,C){o(p,m,C),o(p,v,C),u($,p,C),M=!0},p:jt,i(p){M||(h($.$$.fragment,p),M=!0)},o(p){g($.$$.fragment,p),M=!1},d(p){p&&(t(m),t(v)),_($,p)}}}function ka(Z){let m,y="This API is experimental and may have some slight breaking changes in the next releases.";return{c(){m=l("p"),m.textContent=y},l(v){m=i(v,"P",{"data-svelte-h":!0}),d(m)!=="svelte-15rpg4"&&(m.textContent=y)},m(v,$){o(v,m,$)},p:jt,d(v){v&&t(m)}}}function Ja(Z){let m,y=`This class method is simply calling <a href="/docs/transformers/main/zh/main_classes/feature_extractor#transformers.FeatureExtractionMixin.save_pretrained">save_pretrained()</a> and
<code>save_pretrained()</code>. Please refer to the docstrings of the
methods above for more information.`;return{c(){m=l("p"),m.innerHTML=y},l(v){m=i(v,"P",{"data-svelte-h":!0}),d(m)!=="svelte-pbtpd9"&&(m.innerHTML=y)},m(v,$){o(v,m,$)},p:jt,d(v){v&&t(m)}}}function La(Z){let m,y="Examples:",v,$,M;return $=new Et({props:{code:"aW1wb3J0JTIwdGVuc29yZmxvd19kYXRhc2V0cyUyMGFzJTIwdGZkcyUwQSUwQWRhdGFzZXQlMjAlM0QlMjB0ZmRzLmxvYWQoJTIyc3F1YWQlMjIpJTBBJTBBdHJhaW5pbmdfZXhhbXBsZXMlMjAlM0QlMjBnZXRfZXhhbXBsZXNfZnJvbV9kYXRhc2V0KGRhdGFzZXQlMkMlMjBldmFsdWF0ZSUzREZhbHNlKSUwQWV2YWx1YXRpb25fZXhhbXBsZXMlMjAlM0QlMjBnZXRfZXhhbXBsZXNfZnJvbV9kYXRhc2V0KGRhdGFzZXQlMkMlMjBldmFsdWF0ZSUzRFRydWUp",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">import</span> tensorflow_datasets <span class="hljs-keyword">as</span> tfds

<span class="hljs-meta">&gt;&gt;&gt; </span>dataset = tfds.load(<span class="hljs-string">&quot;squad&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span>training_examples = get_examples_from_dataset(dataset, evaluate=<span class="hljs-literal">False</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>evaluation_examples = get_examples_from_dataset(dataset, evaluate=<span class="hljs-literal">True</span>)`,wrap:!1}}),{c(){m=l("p"),m.textContent=y,v=s(),c($.$$.fragment)},l(p){m=i(p,"P",{"data-svelte-h":!0}),d(m)!=="svelte-kvfsh7"&&(m.textContent=y),v=a(p),f($.$$.fragment,p)},m(p,C){o(p,m,C),o(p,v,C),u($,p,C),M=!0},p:jt,i(p){M||(h($.$$.fragment,p),M=!0)},o(p){g($.$$.fragment,p),M=!1},d(p){p&&(t(m),t(v)),_($,p)}}}function za(Z){let m,y="Example:",v,$,M;return $=new Et({props:{code:"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",highlighted:`processor = SquadV2Processor()
examples = processor.get_dev_examples(data_dir)

features = squad_convert_examples_to_features(
    examples=examples,
    tokenizer=tokenizer,
    max_seq_length=args.max_seq_length,
    doc_stride=args.doc_stride,
    max_query_length=args.max_query_length,
    is_training=<span class="hljs-keyword">not</span> evaluate,
)`,wrap:!1}}),{c(){m=l("p"),m.textContent=y,v=s(),c($.$$.fragment)},l(p){m=i(p,"P",{"data-svelte-h":!0}),d(m)!=="svelte-11lpom8"&&(m.textContent=y),v=a(p),f($.$$.fragment,p)},m(p,C){o(p,m,C),o(p,v,C),u($,p,C),M=!0},p:jt,i(p){M||(h($.$$.fragment,p),M=!0)},o(p){g($.$$.fragment,p),M=!1},d(p){p&&(t(m),t(v)),_($,p)}}}function Va(Z){let m,y,v,$,M,p,C,Zs="在 Transformers 库中，processors可以有两种不同的含义：",Bt,me,ks='<li>为多模态模型，例如<a href="../model_doc/wav2vec2">Wav2Vec2</a>（语音和文本）或<a href="../model_doc/clip">CLIP</a>（文本和视觉）预处理输入的对象</li> <li>在库的旧版本中用于预处理GLUE或SQUAD数据的已弃用对象。</li>',Ht,de,Ft,pe,Js="任何多模态模型都需要一个对象来编码或解码将多个模态（包括文本、视觉和音频）组合在一起的数据。这由称为processors的对象处理，这些processors将两个或多个处理对象组合在一起，例如tokenizers（用于文本模态），image processors（用于视觉）和feature extractors（用于音频）。",Nt,ce,Ls="这些processors继承自以下实现保存和加载功能的基类：",Gt,U,fe,Br,pt,zs="This is a mixin used to provide saving/loading functionality for all processor classes.",Hr,z,ue,Fr,ct,Vs="Instantiate a processor associated with a pretrained model.",Nr,W,Gr,V,he,Sr,ft,js="Upload the processor files to the 🤗 Model Hub.",Wr,R,Rr,j,ge,Qr,ut,qs=`Register this class with a given auto class. This should only be used for custom feature extractors as the ones
in the library are already mapped with <code>AutoProcessor</code>.`,Dr,Q,Yr,q,_e,Ar,ht,Es=`Saves the attributes of this processor (feature extractor, tokenizer…) in the specified directory so that it
can be reloaded using the <a href="/docs/transformers/main/zh/main_classes/processors#transformers.ProcessorMixin.from_pretrained">from_pretrained()</a> method.`,Or,D,St,ve,Wt,$e,Bs='所有processor都遵循与 <a href="/docs/transformers/main/zh/main_classes/processors#transformers.DataProcessor">DataProcessor</a> 相同的架构。processor返回一个 <a href="/docs/transformers/main/zh/main_classes/processors#transformers.InputExample">InputExample</a> 列表。这些 <a href="/docs/transformers/main/zh/main_classes/processors#transformers.InputExample">InputExample</a> 可以转换为 <a href="/docs/transformers/main/zh/main_classes/processors#transformers.InputFeatures">InputFeatures</a> 以供输送到模型。',Rt,T,xe,Kr,gt,Hs="Base class for data converters for sequence classification data sets.",es,Y,be,ts,_t,Fs='Gets a collection of <a href="/docs/transformers/main/zh/main_classes/processors#transformers.InputExample">InputExample</a> for the dev set.',rs,A,ye,ss,vt,Ns="Gets an example from a dict with tensorflow tensors.",as,O,Me,os,$t,Gs="Gets the list of labels for this data set.",ns,K,we,ls,xt,Ss='Gets a collection of <a href="/docs/transformers/main/zh/main_classes/processors#transformers.InputExample">InputExample</a> for the test set.',is,ee,Te,ms,bt,Ws='Gets a collection of <a href="/docs/transformers/main/zh/main_classes/processors#transformers.InputExample">InputExample</a> for the train set.',ds,te,Ce,ps,yt,Rs=`Some tensorflow_datasets datasets are not formatted the same way the GLUE datasets are. This method converts
examples to the correct format.`,Qt,k,Pe,cs,Mt,Qs="A single training/test example for simple sequence classification.",fs,re,Ue,us,wt,Ds="Serializes this instance to a JSON string.",Dt,J,Ie,hs,Tt,Ys="A single set of features of data. Property names are the same names as the corresponding inputs to a model.",gs,se,Xe,_s,Ct,As="Serializes this instance to a JSON string.",Yt,Ze,At,ke,Os='<a href="https://gluebenchmark.com/" rel="nofollow">General Language Understanding Evaluation (GLUE)</a> 是一个基准测试，评估模型在各种现有的自然语言理解任务上的性能。它与论文 <a href="https://openreview.net/pdf?id=rJ4km2R5t7" rel="nofollow">GLUE: A multi-task benchmark and analysis platform for natural language understanding</a> 一同发布。',Ot,Je,Ks="该库为以下任务提供了总共10个processor：MRPC、MNLI、MNLI（mismatched）、CoLA、SST2、STSB、QQP、QNLI、RTE 和 WNLI。",Kt,Le,ea="这些processor是：",er,ze,ta="<li><code>~data.processors.utils.MrpcProcessor</code></li> <li><code>~data.processors.utils.MnliProcessor</code></li> <li><code>~data.processors.utils.MnliMismatchedProcessor</code></li> <li><code>~data.processors.utils.Sst2Processor</code></li> <li><code>~data.processors.utils.StsbProcessor</code></li> <li><code>~data.processors.utils.QqpProcessor</code></li> <li><code>~data.processors.utils.QnliProcessor</code></li> <li><code>~data.processors.utils.RteProcessor</code></li> <li><code>~data.processors.utils.WnliProcessor</code></li>",tr,Ve,ra='此外，还可以使用以下方法从数据文件加载值并将其转换为 <a href="/docs/transformers/main/zh/main_classes/processors#transformers.InputExample">InputExample</a> 列表。',rr,H,je,vs,Pt,sa="Loads a data file into a list of <code>InputFeatures</code>",sr,qe,ar,Ee,aa='<a href="https://www.nyu.edu/projects/bowman/xnli/" rel="nofollow">跨语言NLI语料库（XNLI）</a> 是一个评估跨语言文本表示质量的基准测试。XNLI是一个基于<a href="http://www.nyu.edu/projects/bowman/multinli/" rel="nofollow"><em>MultiNLI</em></a>的众包数据集：”文本对“被标记为包含15种不同语言（包括英语等高资源语言和斯瓦希里语等低资源语言）的文本蕴涵注释。',or,Be,oa='它与论文 <a href="https://arxiv.org/abs/1809.05053" rel="nofollow">XNLI: Evaluating Cross-lingual Sentence Representations</a> 一同发布。',nr,He,na="该库提供了加载XNLI数据的processor：",lr,Fe,la="<li><code>~data.processors.utils.XnliProcessor</code></li>",ir,Ne,ia="请注意，由于测试集上有“gold”标签，因此评估是在测试集上进行的。",mr,Ge,ma='使用这些processor的示例在 <a href="https://github.com/huggingface/transformers/tree/main/examples/pytorch/text-classification/run_xnli.py" rel="nofollow">run_xnli.py</a> 脚本中提供。',dr,Se,pr,We,da='<a href="https://rajpurkar.github.io/SQuAD-explorer//" rel="nofollow">斯坦福问答数据集（SQuAD）</a> 是一个评估模型在问答上性能的基准测试。有两个版本，v1.1 和 v2.0。第一个版本（v1.1）与论文 <a href="https://arxiv.org/abs/1606.05250" rel="nofollow">SQuAD: 100,000+ Questions for Machine Comprehension of Text</a> 一同发布。第二个版本（v2.0）与论文 <a href="https://arxiv.org/abs/1806.03822" rel="nofollow">Know What You Don’t Know: Unanswerable Questions for SQuAD</a> 一同发布。',cr,Re,pa="该库为两个版本各自提供了一个processor：",fr,Qe,ur,De,ca="这两个processor是：",hr,Ye,fa="<li><code>~data.processors.utils.SquadV1Processor</code></li> <li><code>~data.processors.utils.SquadV2Processor</code></li>",gr,Ae,ua="它们都继承自抽象类 <code>~data.processors.utils.SquadProcessor</code>。",_r,I,Oe,$s,Ut,ha=`Processor for the SQuAD data set. overridden by SquadV1Processor and SquadV2Processor, used by the version 1.1 and
version 2.0 of SQuAD, respectively.`,xs,ae,Ke,bs,It,ga="Returns the evaluation example from the data directory.",ys,E,et,Ms,Xt,_a="Creates a list of <code>SquadExample</code> using a TFDS dataset.",ws,oe,Ts,ne,tt,Cs,Zt,va="Returns the training examples from the data directory.",vr,rt,$a="此外，可以使用以下方法将 SQuAD 示例转换为可用作模型输入的 <code>~data.processors.utils.SquadFeatures</code>。",$r,L,st,Ps,kt,xa=`Converts a list of examples into a list of features that can be directly given as input to a model. It is
model-dependant and takes advantage of many of the tokenizer’s features to create the model’s inputs.`,Us,le,xr,at,ba="这些processor以及前面提到的方法可以与包含数据的文件以及tensorflow_datasets包一起使用。下面给出了示例。",br,ot,yr,nt,ya="以下是使用processor以及使用数据文件的转换方法的示例：",Mr,lt,wr,it,Ma="使用 <em>tensorflow_datasets</em> 就像使用数据文件一样简单：",Tr,mt,Cr,dt,wa='另一个使用这些processor的示例在 <a href="https://github.com/huggingface/transformers/tree/main/examples/legacy/question-answering/run_squad.py" rel="nofollow">run_squad.py</a> 脚本中提供。',Pr,qt,Ur;return M=new ie({props:{title:"Processors",local:"processors",headingTag:"h1"}}),de=new ie({props:{title:"多模态processors",local:"transformers.ProcessorMixin",headingTag:"h2"}}),fe=new w({props:{name:"class transformers.ProcessorMixin",anchor:"transformers.ProcessorMixin",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/processing_utils.py#L42"}}),ue=new w({props:{name:"from_pretrained",anchor:"transformers.ProcessorMixin.from_pretrained",parameters:[{name:"pretrained_model_name_or_path",val:": typing.Union[str, os.PathLike]"},{name:"cache_dir",val:": typing.Union[str, os.PathLike, NoneType] = None"},{name:"force_download",val:": bool = False"},{name:"local_files_only",val:": bool = False"},{name:"token",val:": typing.Union[bool, str, NoneType] = None"},{name:"revision",val:": str = 'main'"},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.ProcessorMixin.from_pretrained.pretrained_model_name_or_path",description:`<strong>pretrained_model_name_or_path</strong> (<code>str</code> or <code>os.PathLike</code>) &#x2014;
This can be either:</p>
<ul>
<li>a string, the <em>model id</em> of a pretrained feature_extractor hosted inside a model repo on
huggingface.co. Valid model ids can be located at the root-level, like <code>bert-base-uncased</code>, or
namespaced under a user or organization name, like <code>dbmdz/bert-base-german-cased</code>.</li>
<li>a path to a <em>directory</em> containing a feature extractor file saved using the
<a href="/docs/transformers/main/zh/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>.
**kwargs &#x2014;
Additional keyword arguments passed along to both
<a href="/docs/transformers/main/zh/main_classes/feature_extractor#transformers.FeatureExtractionMixin.from_pretrained">from_pretrained()</a> and
<code>~tokenization_utils_base.PreTrainedTokenizer.from_pretrained</code>.</li>
</ul>`,name:"pretrained_model_name_or_path"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/processing_utils.py#L168"}}),W=new Is({props:{$$slots:{default:[Xa]},$$scope:{ctx:Z}}}),he=new w({props:{name:"push_to_hub",anchor:"transformers.ProcessorMixin.push_to_hub",parameters:[{name:"repo_id",val:": str"},{name:"use_temp_dir",val:": typing.Optional[bool] = None"},{name:"commit_message",val:": typing.Optional[str] = None"},{name:"private",val:": typing.Optional[bool] = None"},{name:"token",val:": typing.Union[bool, str, NoneType] = None"},{name:"max_shard_size",val:": typing.Union[int, str, NoneType] = '5GB'"},{name:"create_pr",val:": bool = False"},{name:"safe_serialization",val:": bool = True"},{name:"revision",val:": str = None"},{name:"commit_description",val:": str = None"},{name:"**deprecated_kwargs",val:""}],parametersDescription:[{anchor:"transformers.ProcessorMixin.push_to_hub.repo_id",description:`<strong>repo_id</strong> (<code>str</code>) &#x2014;
The name of the repository you want to push your processor to. It should contain your organization name
when pushing to a given organization.`,name:"repo_id"},{anchor:"transformers.ProcessorMixin.push_to_hub.use_temp_dir",description:`<strong>use_temp_dir</strong> (<code>bool</code>, <em>optional</em>) &#x2014;
Whether or not to use a temporary directory to store the files saved before they are pushed to the Hub.
Will default to <code>True</code> if there is no directory named like <code>repo_id</code>, <code>False</code> otherwise.`,name:"use_temp_dir"},{anchor:"transformers.ProcessorMixin.push_to_hub.commit_message",description:`<strong>commit_message</strong> (<code>str</code>, <em>optional</em>) &#x2014;
Message to commit while pushing. Will default to <code>&quot;Upload processor&quot;</code>.`,name:"commit_message"},{anchor:"transformers.ProcessorMixin.push_to_hub.private",description:`<strong>private</strong> (<code>bool</code>, <em>optional</em>) &#x2014;
Whether or not the repository created should be private.`,name:"private"},{anchor:"transformers.ProcessorMixin.push_to_hub.token",description:`<strong>token</strong> (<code>bool</code> or <code>str</code>, <em>optional</em>) &#x2014;
The token to use as HTTP bearer authorization for remote files. If <code>True</code>, will use the token generated
when running <code>huggingface-cli login</code> (stored in <code>~/.huggingface</code>). Will default to <code>True</code> if <code>repo_url</code>
is not specified.`,name:"token"},{anchor:"transformers.ProcessorMixin.push_to_hub.max_shard_size",description:`<strong>max_shard_size</strong> (<code>int</code> or <code>str</code>, <em>optional</em>, defaults to <code>&quot;5GB&quot;</code>) &#x2014;
Only applicable for models. The maximum size for a checkpoint before being sharded. Checkpoints shard
will then be each of size lower than this size. If expressed as a string, needs to be digits followed
by a unit (like <code>&quot;5MB&quot;</code>). We default it to <code>&quot;5GB&quot;</code> so that users can easily load models on free-tier
Google Colab instances without any CPU OOM issues.`,name:"max_shard_size"},{anchor:"transformers.ProcessorMixin.push_to_hub.create_pr",description:`<strong>create_pr</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to create a PR with the uploaded files or directly commit.`,name:"create_pr"},{anchor:"transformers.ProcessorMixin.push_to_hub.safe_serialization",description:`<strong>safe_serialization</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>True</code>) &#x2014;
Whether or not to convert the model weights in safetensors format for safer serialization.`,name:"safe_serialization"},{anchor:"transformers.ProcessorMixin.push_to_hub.revision",description:`<strong>revision</strong> (<code>str</code>, <em>optional</em>) &#x2014;
Branch to push the uploaded files to.`,name:"revision"},{anchor:"transformers.ProcessorMixin.push_to_hub.commit_description",description:`<strong>commit_description</strong> (<code>str</code>, <em>optional</em>) &#x2014;
The description of the commit that will be created`,name:"commit_description"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/utils/hub.py#L759"}}),R=new Xs({props:{anchor:"transformers.ProcessorMixin.push_to_hub.example",$$slots:{default:[Za]},$$scope:{ctx:Z}}}),ge=new w({props:{name:"register_for_auto_class",anchor:"transformers.ProcessorMixin.register_for_auto_class",parameters:[{name:"auto_class",val:" = 'AutoProcessor'"}],parametersDescription:[{anchor:"transformers.ProcessorMixin.register_for_auto_class.auto_class",description:`<strong>auto_class</strong> (<code>str</code> or <code>type</code>, <em>optional</em>, defaults to <code>&quot;AutoProcessor&quot;</code>) &#x2014;
The auto class to register this new feature extractor with.`,name:"auto_class"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/processing_utils.py#L231"}}),Q=new Is({props:{warning:!0,$$slots:{default:[ka]},$$scope:{ctx:Z}}}),_e=new w({props:{name:"save_pretrained",anchor:"transformers.ProcessorMixin.save_pretrained",parameters:[{name:"save_directory",val:""},{name:"push_to_hub",val:": bool = False"},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.ProcessorMixin.save_pretrained.save_directory",description:`<strong>save_directory</strong> (<code>str</code> or <code>os.PathLike</code>) &#x2014;
Directory where the feature extractor JSON file and the tokenizer files will be saved (directory will
be created if it does not exist).`,name:"save_directory"},{anchor:"transformers.ProcessorMixin.save_pretrained.push_to_hub",description:`<strong>push_to_hub</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to push your model to the Hugging Face model hub after saving it. You can specify the
repository you want to push to with <code>repo_id</code> (will default to the name of <code>save_directory</code> in your
namespace).`,name:"push_to_hub"},{anchor:"transformers.ProcessorMixin.save_pretrained.kwargs",description:`<strong>kwargs</strong> (<code>Dict[str, Any]</code>, <em>optional</em>) &#x2014;
Additional key word arguments passed along to the <a href="/docs/transformers/main/zh/main_classes/tokenizer#transformers.PreTrainedTokenizerFast.push_to_hub">push_to_hub()</a> method.`,name:"kwargs"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/processing_utils.py#L93"}}),D=new Is({props:{$$slots:{default:[Ja]},$$scope:{ctx:Z}}}),ve=new ie({props:{title:"已弃用的processors",local:"transformers.DataProcessor",headingTag:"h2"}}),xe=new w({props:{name:"class transformers.DataProcessor",anchor:"transformers.DataProcessor",parameters:[],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/data/processors/utils.py#L80"}}),be=new w({props:{name:"get_dev_examples",anchor:"transformers.DataProcessor.get_dev_examples",parameters:[{name:"data_dir",val:""}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/data/processors/utils.py#L97"}}),ye=new w({props:{name:"get_example_from_tensor_dict",anchor:"transformers.DataProcessor.get_example_from_tensor_dict",parameters:[{name:"tensor_dict",val:""}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/data/processors/utils.py#L83"}}),Me=new w({props:{name:"get_labels",anchor:"transformers.DataProcessor.get_labels",parameters:[],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/data/processors/utils.py#L105"}}),we=new w({props:{name:"get_test_examples",anchor:"transformers.DataProcessor.get_test_examples",parameters:[{name:"data_dir",val:""}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/data/processors/utils.py#L101"}}),Te=new w({props:{name:"get_train_examples",anchor:"transformers.DataProcessor.get_train_examples",parameters:[{name:"data_dir",val:""}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/data/processors/utils.py#L93"}}),Ce=new w({props:{name:"tfds_map",anchor:"transformers.DataProcessor.tfds_map",parameters:[{name:"example",val:""}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/data/processors/utils.py#L109"}}),Pe=new w({props:{name:"class transformers.InputExample",anchor:"transformers.InputExample",parameters:[{name:"guid",val:": str"},{name:"text_a",val:": str"},{name:"text_b",val:": typing.Optional[str] = None"},{name:"label",val:": typing.Optional[str] = None"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/data/processors/utils.py#L30"}}),Ue=new w({props:{name:"to_json_string",anchor:"transformers.InputExample.to_json_string",parameters:[],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/data/processors/utils.py#L49"}}),Ie=new w({props:{name:"class transformers.InputFeatures",anchor:"transformers.InputFeatures",parameters:[{name:"input_ids",val:": typing.List[int]"},{name:"attention_mask",val:": typing.Optional[typing.List[int]] = None"},{name:"token_type_ids",val:": typing.Optional[typing.List[int]] = None"},{name:"label",val:": typing.Union[int, float, NoneType] = None"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/data/processors/utils.py#L55"}}),Xe=new w({props:{name:"to_json_string",anchor:"transformers.InputFeatures.to_json_string",parameters:[],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/data/processors/utils.py#L75"}}),Ze=new ie({props:{title:"GLUE",local:"transformers.glue_convert_examples_to_features",headingTag:"h2"}}),je=new w({props:{name:"transformers.glue_convert_examples_to_features",anchor:"transformers.glue_convert_examples_to_features",parameters:[{name:"examples",val:": typing.Union[typing.List[transformers.data.processors.utils.InputExample], ForwardRef('tf.data.Dataset')]"},{name:"tokenizer",val:": PreTrainedTokenizer"},{name:"max_length",val:": typing.Optional[int] = None"},{name:"task",val:" = None"},{name:"label_list",val:" = None"},{name:"output_mode",val:" = None"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/data/processors/glue.py#L41",returnDescription:`<script context="module">export const metadata = 'undefined';<\/script>


<p>If the <code>examples</code> input is a <code>tf.data.Dataset</code>, will return a <code>tf.data.Dataset</code> containing the task-specific
features. If the input is a list of <code>InputExamples</code>, will return a list of task-specific <code>InputFeatures</code> which
can be fed to the model.</p>
`}}),qe=new ie({props:{title:"XNLI",local:"xnli",headingTag:"h2"}}),Se=new ie({props:{title:"SQuAD",local:"squad",headingTag:"h2"}}),Qe=new ie({props:{title:"Processors",local:"transformers.data.processors.squad.SquadProcessor",headingTag:"h3"}}),Oe=new w({props:{name:"class transformers.data.processors.squad.SquadProcessor",anchor:"transformers.data.processors.squad.SquadProcessor",parameters:[],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/data/processors/squad.py#L541"}}),Ke=new w({props:{name:"get_dev_examples",anchor:"transformers.data.processors.squad.SquadProcessor.get_dev_examples",parameters:[{name:"data_dir",val:""},{name:"filename",val:" = None"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/data/processors/squad.py#L629"}}),et=new w({props:{name:"get_examples_from_dataset",anchor:"transformers.data.processors.squad.SquadProcessor.get_examples_from_dataset",parameters:[{name:"dataset",val:""},{name:"evaluate",val:" = False"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/data/processors/squad.py#L574",returnDescription:`<script context="module">export const metadata = 'undefined';<\/script>


<p>List of SquadExample</p>
`}}),oe=new Xs({props:{anchor:"transformers.data.processors.squad.SquadProcessor.get_examples_from_dataset.example",$$slots:{default:[La]},$$scope:{ctx:Z}}}),tt=new w({props:{name:"get_train_examples",anchor:"transformers.data.processors.squad.SquadProcessor.get_train_examples",parameters:[{name:"data_dir",val:""},{name:"filename",val:" = None"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/data/processors/squad.py#L607"}}),st=new w({props:{name:"transformers.squad_convert_examples_to_features",anchor:"transformers.squad_convert_examples_to_features",parameters:[{name:"examples",val:""},{name:"tokenizer",val:""},{name:"max_seq_length",val:""},{name:"doc_stride",val:""},{name:"max_query_length",val:""},{name:"is_training",val:""},{name:"padding_strategy",val:" = 'max_length'"},{name:"return_dataset",val:" = False"},{name:"threads",val:" = 1"},{name:"tqdm_enabled",val:" = True"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/data/processors/squad.py#L316",returnDescription:`<script context="module">export const metadata = 'undefined';<\/script>


<p>list of <code>SquadFeatures</code></p>
`}}),le=new Xs({props:{anchor:"transformers.squad_convert_examples_to_features.example",$$slots:{default:[za]},$$scope:{ctx:Z}}}),ot=new ie({props:{title:"Example使用",local:"example使用",headingTag:"h3"}}),lt=new Et({props:{code:"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",highlighted:`<span class="hljs-comment"># Loading a V2 processor</span>
processor = SquadV2Processor()
examples = processor.get_dev_examples(squad_v2_data_dir)

<span class="hljs-comment"># Loading a V1 processor</span>
processor = SquadV1Processor()
examples = processor.get_dev_examples(squad_v1_data_dir)

features = squad_convert_examples_to_features(
    examples=examples,
    tokenizer=tokenizer,
    max_seq_length=max_seq_length,
    doc_stride=args.doc_stride,
    max_query_length=max_query_length,
    is_training=<span class="hljs-keyword">not</span> evaluate,
)`,wrap:!1}}),mt=new Et({props:{code:"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",highlighted:`<span class="hljs-comment"># tensorflow_datasets only handle Squad V1.</span>
tfds_examples = tfds.load(<span class="hljs-string">&quot;squad&quot;</span>)
examples = SquadV1Processor().get_examples_from_dataset(tfds_examples, evaluate=evaluate)

features = squad_convert_examples_to_features(
    examples=examples,
    tokenizer=tokenizer,
    max_seq_length=max_seq_length,
    doc_stride=args.doc_stride,
    max_query_length=max_query_length,
    is_training=<span class="hljs-keyword">not</span> evaluate,
)`,wrap:!1}}),{c(){m=l("meta"),y=s(),v=l("p"),$=s(),c(M.$$.fragment),p=s(),C=l("p"),C.textContent=Zs,Bt=s(),me=l("ul"),me.innerHTML=ks,Ht=s(),c(de.$$.fragment),Ft=s(),pe=l("p"),pe.textContent=Js,Nt=s(),ce=l("p"),ce.textContent=Ls,Gt=s(),U=l("div"),c(fe.$$.fragment),Br=s(),pt=l("p"),pt.textContent=zs,Hr=s(),z=l("div"),c(ue.$$.fragment),Fr=s(),ct=l("p"),ct.textContent=Vs,Nr=s(),c(W.$$.fragment),Gr=s(),V=l("div"),c(he.$$.fragment),Sr=s(),ft=l("p"),ft.textContent=js,Wr=s(),c(R.$$.fragment),Rr=s(),j=l("div"),c(ge.$$.fragment),Qr=s(),ut=l("p"),ut.innerHTML=qs,Dr=s(),c(Q.$$.fragment),Yr=s(),q=l("div"),c(_e.$$.fragment),Ar=s(),ht=l("p"),ht.innerHTML=Es,Or=s(),c(D.$$.fragment),St=s(),c(ve.$$.fragment),Wt=s(),$e=l("p"),$e.innerHTML=Bs,Rt=s(),T=l("div"),c(xe.$$.fragment),Kr=s(),gt=l("p"),gt.textContent=Hs,es=s(),Y=l("div"),c(be.$$.fragment),ts=s(),_t=l("p"),_t.innerHTML=Fs,rs=s(),A=l("div"),c(ye.$$.fragment),ss=s(),vt=l("p"),vt.textContent=Ns,as=s(),O=l("div"),c(Me.$$.fragment),os=s(),$t=l("p"),$t.textContent=Gs,ns=s(),K=l("div"),c(we.$$.fragment),ls=s(),xt=l("p"),xt.innerHTML=Ss,is=s(),ee=l("div"),c(Te.$$.fragment),ms=s(),bt=l("p"),bt.innerHTML=Ws,ds=s(),te=l("div"),c(Ce.$$.fragment),ps=s(),yt=l("p"),yt.textContent=Rs,Qt=s(),k=l("div"),c(Pe.$$.fragment),cs=s(),Mt=l("p"),Mt.textContent=Qs,fs=s(),re=l("div"),c(Ue.$$.fragment),us=s(),wt=l("p"),wt.textContent=Ds,Dt=s(),J=l("div"),c(Ie.$$.fragment),hs=s(),Tt=l("p"),Tt.textContent=Ys,gs=s(),se=l("div"),c(Xe.$$.fragment),_s=s(),Ct=l("p"),Ct.textContent=As,Yt=s(),c(Ze.$$.fragment),At=s(),ke=l("p"),ke.innerHTML=Os,Ot=s(),Je=l("p"),Je.textContent=Ks,Kt=s(),Le=l("p"),Le.textContent=ea,er=s(),ze=l("ul"),ze.innerHTML=ta,tr=s(),Ve=l("p"),Ve.innerHTML=ra,rr=s(),H=l("div"),c(je.$$.fragment),vs=s(),Pt=l("p"),Pt.innerHTML=sa,sr=s(),c(qe.$$.fragment),ar=s(),Ee=l("p"),Ee.innerHTML=aa,or=s(),Be=l("p"),Be.innerHTML=oa,nr=s(),He=l("p"),He.textContent=na,lr=s(),Fe=l("ul"),Fe.innerHTML=la,ir=s(),Ne=l("p"),Ne.textContent=ia,mr=s(),Ge=l("p"),Ge.innerHTML=ma,dr=s(),c(Se.$$.fragment),pr=s(),We=l("p"),We.innerHTML=da,cr=s(),Re=l("p"),Re.textContent=pa,fr=s(),c(Qe.$$.fragment),ur=s(),De=l("p"),De.textContent=ca,hr=s(),Ye=l("ul"),Ye.innerHTML=fa,gr=s(),Ae=l("p"),Ae.innerHTML=ua,_r=s(),I=l("div"),c(Oe.$$.fragment),$s=s(),Ut=l("p"),Ut.textContent=ha,xs=s(),ae=l("div"),c(Ke.$$.fragment),bs=s(),It=l("p"),It.textContent=ga,ys=s(),E=l("div"),c(et.$$.fragment),Ms=s(),Xt=l("p"),Xt.innerHTML=_a,ws=s(),c(oe.$$.fragment),Ts=s(),ne=l("div"),c(tt.$$.fragment),Cs=s(),Zt=l("p"),Zt.textContent=va,vr=s(),rt=l("p"),rt.innerHTML=$a,$r=s(),L=l("div"),c(st.$$.fragment),Ps=s(),kt=l("p"),kt.textContent=xa,Us=s(),c(le.$$.fragment),xr=s(),at=l("p"),at.textContent=ba,br=s(),c(ot.$$.fragment),yr=s(),nt=l("p"),nt.textContent=ya,Mr=s(),c(lt.$$.fragment),wr=s(),it=l("p"),it.innerHTML=Ma,Tr=s(),c(mt.$$.fragment),Cr=s(),dt=l("p"),dt.innerHTML=wa,Pr=s(),qt=l("p"),this.h()},l(e){const 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