import{s as tn,o as rn,n as Q}from"../chunks/scheduler.25b97de1.js";import{S as sn,i as on,g as n,s,r as f,m as an,A as nn,h as i,f as t,c as o,j as y,u,x as p,n as ln,k as M,y as r,a as l,v as g,d as h,t as _,w as x}from"../chunks/index.d9030fc9.js";import{T as Go}from"../chunks/Tip.baa67368.js";import{D as P}from"../chunks/Docstring.ffac8efa.js";import{C as ye}from"../chunks/CodeBlock.e6cd0d95.js";import{E as wr}from"../chunks/ExampleCodeBlock.22dfe688.js";import{H as be,E as cn}from"../chunks/EditOnGithub.91d95064.js";function dn(U){let d,w=`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/internal/image_processing_utils#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(){d=n("p"),d.innerHTML=w},l(v){d=i(v,"P",{"data-svelte-h":!0}),p(d)!=="svelte-1yuzgkp"&&(d.innerHTML=w)},m(v,m){l(v,d,m)},p:Q,d(v){v&&t(d)}}}function pn(U){let d,w="And we define the call method as:",v,m,$;return m=new ye({props:{code:"ZGVmJTIwX19jYWxsX18oJTBBJTIwJTIwJTIwJTIwc2VsZiUyQyUwQSUyMCUyMCUyMCUyMHRleHQlM0ElMjBzdHIlMkMlMEElMjAlMjAlMjAlMjBpbWFnZXMlM0ElMjBPcHRpb25hbCU1QkltYWdlSW5wdXQlNUQlMjAlM0QlMjBOb25lJTJDJTBBJTIwJTIwJTIwJTIwKmFyZyUyQyUwQSUyMCUyMCUyMCUyMGF1ZGlvJTNETm9uZSUyQyUwQSUyMCUyMCUyMCUyMHZpZGVvcyUzRE5vbmUlMkMlMEEp",highlighted:`<span class="hljs-keyword">def</span> <span class="hljs-title function_">__call__</span>(<span class="hljs-params">
    self,
    text: <span class="hljs-built_in">str</span>,
    images: <span class="hljs-type">Optional</span>[ImageInput] = <span class="hljs-literal">None</span>,
    *arg,
    audio=<span class="hljs-literal">None</span>,
    videos=<span class="hljs-literal">None</span>,
</span>)`,wrap:!1}}),{c(){d=n("p"),d.textContent=w,v=s(),f(m.$$.fragment)},l(c){d=i(c,"P",{"data-svelte-h":!0}),p(d)!=="svelte-ft0bna"&&(d.textContent=w),v=o(c),u(m.$$.fragment,c)},m(c,T){l(c,d,T),l(c,v,T),g(m,c,T),$=!0},p:Q,i(c){$||(h(m.$$.fragment,c),$=!0)},o(c){_(m.$$.fragment,c),$=!1},d(c){c&&(t(d),t(v)),x(m,c)}}}function mn(U){let d,w="Then, if we call the processor as:",v,m,$;return m=new ye({props:{code:"aW1hZ2VzJTIwJTNEJTIwJTVCLi4uJTVEJTBBcHJvY2Vzc29yKCUyMldoYXQlMjBpcyUyMGNvbW1vbiUyMGluJTIwdGhlc2UlMjBpbWFnZXMlM0YlMjIlMkMlMjBpbWFnZXMlMkMlMjBhcmdfdmFsdWVfMSUyQyUyMGFyZ192YWx1ZV8yKQ==",highlighted:`images = [...]
processor(<span class="hljs-string">&quot;What is common in these images?&quot;</span>, images, arg_value_1, arg_value_2)`,wrap:!1}}),{c(){d=n("p"),d.textContent=w,v=s(),f(m.$$.fragment)},l(c){d=i(c,"P",{"data-svelte-h":!0}),p(d)!=="svelte-k5ldhr"&&(d.textContent=w),v=o(c),u(m.$$.fragment,c)},m(c,T){l(c,d,T),l(c,v,T),g(m,c,T),$=!0},p:Q,i(c){$||(h(m.$$.fragment,c),$=!0)},o(c){_(m.$$.fragment,c),$=!1},d(c){c&&(t(d),t(v)),x(m,c)}}}function fn(U){let d,w="Then, this method will return:",v,m,$;return m=new ye({props:{code:"JTdCJTBBJTIwJTIwJTIwJTIwJTIyYXJnX25hbWVfMSUyMiUzQSUyMGFyZ192YWx1ZV8xJTJDJTBBJTIwJTIwJTIwJTIwJTIyYXJnX25hbWVfMiUyMiUzQSUyMGFyZ192YWx1ZV8yJTJDJTBBJTdE",highlighted:`{
    <span class="hljs-string">&quot;arg_name_1&quot;</span>: arg_value_1,
    <span class="hljs-string">&quot;arg_name_2&quot;</span>: arg_value_2,
}`,wrap:!1}}),{c(){d=n("p"),d.textContent=w,v=s(),f(m.$$.fragment)},l(c){d=i(c,"P",{"data-svelte-h":!0}),p(d)!=="svelte-wadzyw"&&(d.textContent=w),v=o(c),u(m.$$.fragment,c)},m(c,T){l(c,d,T),l(c,v,T),g(m,c,T),$=!0},p:Q,i(c){$||(h(m.$$.fragment,c),$=!0)},o(c){_(m.$$.fragment,c),$=!1},d(c){c&&(t(d),t(v)),x(m,c)}}}function un(U){let d,w="Examples:",v,m,$;return m=new ye({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;google-bert/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(){d=n("p"),d.textContent=w,v=s(),f(m.$$.fragment)},l(c){d=i(c,"P",{"data-svelte-h":!0}),p(d)!=="svelte-kvfsh7"&&(d.textContent=w),v=o(c),u(m.$$.fragment,c)},m(c,T){l(c,d,T),l(c,v,T),g(m,c,T),$=!0},p:Q,i(c){$||(h(m.$$.fragment,c),$=!0)},o(c){_(m.$$.fragment,c),$=!1},d(c){c&&(t(d),t(v)),x(m,c)}}}function gn(U){let d,w="This API is experimental and may have some slight breaking changes in the next releases.";return{c(){d=n("p"),d.textContent=w},l(v){d=i(v,"P",{"data-svelte-h":!0}),p(d)!=="svelte-15rpg4"&&(d.textContent=w)},m(v,m){l(v,d,m)},p:Q,d(v){v&&t(d)}}}function hn(U){let d,w=`This class method is simply calling <a href="/docs/transformers/main/zh/main_classes/feature_extractor#transformers.FeatureExtractionMixin.save_pretrained">save_pretrained()</a> and
<a href="/docs/transformers/main/zh/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.save_pretrained">save_pretrained()</a>. Please refer to the docstrings of the
methods above for more information.`;return{c(){d=n("p"),d.innerHTML=w},l(v){d=i(v,"P",{"data-svelte-h":!0}),p(d)!=="svelte-6mch0o"&&(d.innerHTML=w)},m(v,m){l(v,d,m)},p:Q,d(v){v&&t(d)}}}function _n(U){let d,w="Examples:",v,m,$;return m=new ye({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(){d=n("p"),d.textContent=w,v=s(),f(m.$$.fragment)},l(c){d=i(c,"P",{"data-svelte-h":!0}),p(d)!=="svelte-kvfsh7"&&(d.textContent=w),v=o(c),u(m.$$.fragment,c)},m(c,T){l(c,d,T),l(c,v,T),g(m,c,T),$=!0},p:Q,i(c){$||(h(m.$$.fragment,c),$=!0)},o(c){_(m.$$.fragment,c),$=!1},d(c){c&&(t(d),t(v)),x(m,c)}}}function xn(U){let d,w="Example:",v,m,$;return m=new ye({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(){d=n("p"),d.textContent=w,v=s(),f(m.$$.fragment)},l(c){d=i(c,"P",{"data-svelte-h":!0}),p(d)!=="svelte-11lpom8"&&(d.textContent=w),v=o(c),u(m.$$.fragment,c)},m(c,T){l(c,d,T),l(c,v,T),g(m,c,T),$=!0},p:Q,i(c){$||(h(m.$$.fragment,c),$=!0)},o(c){_(m.$$.fragment,c),$=!1},d(c){c&&(t(d),t(v)),x(m,c)}}}function vn(U){let d,w,v,m,$,c,T,Wo="在 Transformers 库中，processors可以有两种不同的含义：",Cr,Me,Ro='<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>',Pr,we,kr,Te,Qo="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）。",Ir,Ce,Yo="这些processors继承自以下实现保存和加载功能的基类：",Jr,b,Pe,Js,Zt,Ao="This is a mixin used to provide saving/loading functionality for all processor classes.",Us,j,ke,Ls,Et,Oo=`Similar to the <code>apply_chat_template</code> method on tokenizers, this method applies a Jinja template to input
conversations to turn them into a single tokenizable string.`,qs,Vt,Ko=`The input is expected to be in the following format, where each message content is a list consisting of text and
optionally image or video inputs. One can also provide an image, video, URL or local path which will be used to form
<code>pixel_values</code> when <code>return_dict=True</code>. If not provided, one will get only the formatted text, optionally tokenized text.`,js,Dt,ea=`conversation = [
{
“role”: “user”,
“content”: [
{“type”: “image”, “image”: “https://www.ilankelman.org/stopsigns/australia.jpg”},
{“type”: “text”, “text”: “Please describe this image in detail.”},
],
},
]`,Xs,Y,Ie,zs,Ht,ta="Instantiates a type of <code>~processing_utils.ProcessingMixin</code> from a Python dictionary of parameters.",Zs,V,Je,Es,Bt,ra="Instantiate a processor associated with a pretrained model.",Vs,A,Ds,O,Ue,Hs,St,sa=`From a <code>pretrained_model_name_or_path</code>, resolve to a dictionary of parameters, to be used for instantiating a
processor of type <code>~processing_utils.ProcessingMixin</code> using <code>from_args_and_dict</code>.`,Bs,K,Le,Ss,Nt,oa="Post-process the output of a vlm to decode the text.",Ns,k,qe,Fs,Ft,aa=`Matches optional positional arguments to their corresponding names in <code>optional_call_args</code>
in the processor class in the order they are passed to the processor call.`,Gs,Gt,na=`Note that this should only be used in the <code>__call__</code> method of the processors with special
arguments. Special arguments are arguments that aren’t <code>text</code>, <code>images</code>, <code>audio</code>, nor <code>videos</code>
but also aren’t passed to the tokenizer, image processor, etc. Examples of such processors are:`,Ws,Wt,ia="<li><code>CLIPSegProcessor</code></li> <li><code>LayoutLMv2Processor</code></li> <li><code>OwlViTProcessor</code></li>",Rs,Rt,la=`Also note that passing by position to the processor call is now deprecated and will be disallowed
in future versions. We only have this for backward compatibility.`,Qs,Qt,ca=`Example:
Suppose that the processor class has <code>optional_call_args = [&quot;arg_name_1&quot;, &quot;arg_name_2&quot;]</code>.`,Ys,ee,As,te,Os,re,Ks,eo,D,je,to,Yt,da="Upload the processor files to the 🤗 Model Hub.",ro,se,so,H,Xe,oo,At,pa=`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>.`,ao,oe,no,B,ze,io,Ot,ma=`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.`,lo,ae,co,ne,Ze,po,Kt,fa="Serializes this instance to a Python dictionary.",mo,ie,Ee,fo,er,ua="Save this instance to a JSON file.",uo,le,Ve,go,tr,ga="Serializes this instance to a JSON string.",Ur,De,Lr,He,ha='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。',qr,J,Be,ho,rr,_a="Base class for data converters for sequence classification data sets.",_o,ce,Se,xo,sr,xa='Gets a collection of <a href="/docs/transformers/main/zh/main_classes/processors#transformers.InputExample">InputExample</a> for the dev set.',vo,de,Ne,$o,or,va="Gets an example from a dict with tensorflow tensors.",bo,pe,Fe,yo,ar,$a="Gets the list of labels for this data set.",Mo,me,Ge,wo,nr,ba='Gets a collection of <a href="/docs/transformers/main/zh/main_classes/processors#transformers.InputExample">InputExample</a> for the test set.',To,fe,We,Co,ir,ya='Gets a collection of <a href="/docs/transformers/main/zh/main_classes/processors#transformers.InputExample">InputExample</a> for the train set.',Po,ue,Re,ko,lr,Ma=`Some tensorflow_datasets datasets are not formatted the same way the GLUE datasets are. This method converts
examples to the correct format.`,jr,X,Qe,Io,cr,wa="A single training/test example for simple sequence classification.",Jo,ge,Ye,Uo,dr,Ta="Serializes this instance to a JSON string.",Xr,z,Ae,Lo,pr,Ca="A single set of features of data. Property names are the same names as the corresponding inputs to a model.",qo,he,Oe,jo,mr,Pa="Serializes this instance to a JSON string.",zr,Ke,Zr,et,ka='<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。',Er,tt,Ia="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。",Vr,rt,Ja="这些processor是：",Dr,st,Ua="<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>",Hr,ot,La='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。',Br,F,at,Xo,fr,qa="Loads a data file into a list of <code>InputFeatures</code>",Sr,nt,Nr,it,ja='<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。',Fr,lt,Xa='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。',Gr,ct,za="该库提供了加载XNLI数据的processor：",Wr,dt,Za="<li><code>~data.processors.utils.XnliProcessor</code></li>",Rr,pt,Ea="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。",Qr,mt,Va='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。',Yr,ft,Ar,ut,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。',Or,gt,Ha="该库为两个版本各自提供了一个processor：",Kr,ht,es,_t,Ba="这两个processor是：",ts,xt,Sa="<li><code>~data.processors.utils.SquadV1Processor</code></li> <li><code>~data.processors.utils.SquadV2Processor</code></li>",rs,vt,Na="它们都继承自抽象类 <code>~data.processors.utils.SquadProcessor</code>。",ss,q,$t,zo,ur,Fa=`Processor for the SQuAD data set. overridden by SquadV1Processor and SquadV2Processor, used by the version 1.1 and
version 2.0 of SQuAD, respectively.`,Zo,_e,bt,Eo,gr,Ga="Returns the evaluation example from the data directory.",Vo,S,yt,Do,hr,Wa="Creates a list of <code>SquadExample</code> using a TFDS dataset.",Ho,xe,Bo,ve,Mt,So,_r,Ra="Returns the training examples from the data directory.",os,wt,Qa="此外，可以使用以下方法将 SQuAD 示例转换为可用作模型输入的 <code>~data.processors.utils.SquadFeatures</code>。",as,Z,Tt,No,xr,Ya=`Converts a list of examples into a list of features that can be directly given as input to a model. It is
model-dependant and takes advantage of many of the tokenizer’s features to create the model’s inputs.`,Fo,$e,ns,Ct,Aa="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。",is,Pt,ls,kt,Oa="以下是使用processor以及使用数据文件的转换方法的示例：",cs,It,ds,Jt,Ka="使用 <em>tensorflow_datasets</em> 就像使用数据文件一样简单：",ps,Ut,ms,Lt,en='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。',fs,qt,us,Tr,gs;return $=new be({props:{title:"Processors",local:"processors",headingTag:"h1"}}),we=new be({props:{title:"多模态processors",local:"transformers.ProcessorMixin",headingTag:"h2"}}),Pe=new P({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#L431"}}),ke=new P({props:{name:"apply_chat_template",anchor:"transformers.ProcessorMixin.apply_chat_template",parameters:[{name:"conversation",val:": typing.Union[typing.List[typing.Dict[str, str]], typing.List[typing.List[typing.Dict[str, str]]]]"},{name:"chat_template",val:": typing.Optional[str] = None"},{name:"**kwargs",val:": typing_extensions.Unpack[transformers.processing_utils.AllKwargsForChatTemplate]"}],parametersDescription:[{anchor:"transformers.ProcessorMixin.apply_chat_template.conversation",description:`<strong>conversation</strong> (<code>Union[List[Dict, [str, str]], List[List[Dict[str, str]]]]</code>) &#x2014;
The conversation to format.`,name:"conversation"},{anchor:"transformers.ProcessorMixin.apply_chat_template.chat_template",description:`<strong>chat_template</strong> (<code>Optional[str]</code>, <em>optional</em>) &#x2014;
The Jinja template to use for formatting the conversation. If not provided, the tokenizer&#x2019;s
chat template is used.`,name:"chat_template"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/processing_utils.py#L1229"}}),Ie=new P({props:{name:"from_args_and_dict",anchor:"transformers.ProcessorMixin.from_args_and_dict",parameters:[{name:"args",val:""},{name:"processor_dict",val:": typing.Dict[str, typing.Any]"},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.ProcessorMixin.from_args_and_dict.processor_dict",description:`<strong>processor_dict</strong> (<code>Dict[str, Any]</code>) &#x2014;
Dictionary that will be used to instantiate the processor object. Such a dictionary can be
retrieved from a pretrained checkpoint by leveraging the
<code>~processing_utils.ProcessingMixin.to_dict</code> method.`,name:"processor_dict"},{anchor:"transformers.ProcessorMixin.from_args_and_dict.kwargs",description:`<strong>kwargs</strong> (<code>Dict[str, Any]</code>) &#x2014;
Additional parameters from which to initialize the processor object.`,name:"kwargs"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/processing_utils.py#L839",returnDescription:`<script context="module">export const metadata = 'undefined';<\/script>


<p>The processor object instantiated from those
parameters.</p>
`,returnType:`<script context="module">export const metadata = 'undefined';<\/script>


<p><code>~processing_utils.ProcessingMixin</code></p>
`}}),Je=new P({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[str, bool, 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.</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>.</li>
</ul>`,name:"pretrained_model_name_or_path"},{anchor:"transformers.ProcessorMixin.from_pretrained.*kwargs",description:`*<strong>*kwargs</strong> &#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>.`,name:"*kwargs"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/processing_utils.py#L1011"}}),A=new Go({props:{$$slots:{default:[dn]},$$scope:{ctx:U}}}),Ue=new P({props:{name:"get_processor_dict",anchor:"transformers.ProcessorMixin.get_processor_dict",parameters:[{name:"pretrained_model_name_or_path",val:": typing.Union[str, os.PathLike]"},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.ProcessorMixin.get_processor_dict.pretrained_model_name_or_path",description:`<strong>pretrained_model_name_or_path</strong> (<code>str</code> or <code>os.PathLike</code>) &#x2014;
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In case the relevant files are located inside a subfolder of the model repo on huggingface.co, you can
specify the folder name here.`,name:"subfolder"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/processing_utils.py#L659",returnDescription:`<script context="module">export const metadata = 'undefined';<\/script>


<p>The dictionary(ies) that will be used to instantiate the processor object.</p>
`,returnType:`<script context="module">export const metadata = 'undefined';<\/script>


<p><code>Tuple[Dict, Dict]</code></p>
`}}),Le=new P({props:{name:"post_process_image_text_to_text",anchor:"transformers.ProcessorMixin.post_process_image_text_to_text",parameters:[{name:"generated_outputs",val:""},{name:"skip_special_tokens",val:" = True"},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.ProcessorMixin.post_process_image_text_to_text.generated_outputs",description:`<strong>generated_outputs</strong> (<code>torch.Tensor</code> or <code>np.ndarray</code>) &#x2014;
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or <code>(sequence_length,)</code>.`,name:"generated_outputs"},{anchor:"transformers.ProcessorMixin.post_process_image_text_to_text.skip_special_tokens",description:`<strong>skip_special_tokens</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>True</code>) &#x2014;
Whether or not to remove special tokens in the output. Argument passed to the tokenizer&#x2019;s <code>batch_decode</code> method.`,name:"skip_special_tokens"},{anchor:"transformers.ProcessorMixin.post_process_image_text_to_text.*kwargs",description:`*<strong>*kwargs</strong> &#x2014;
Additional arguments to be passed to the tokenizer&#x2019;s <code>batch_decode method</code>.`,name:"*kwargs"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/processing_utils.py#L1395",returnDescription:`<script context="module">export const metadata = 'undefined';<\/script>


<p>The decoded text.</p>
`,returnType:`<script context="module">export const metadata = 'undefined';<\/script>


<p><code>List[str]</code></p>
`}}),qe=new P({props:{name:"prepare_and_validate_optional_call_args",anchor:"transformers.ProcessorMixin.prepare_and_validate_optional_call_args",parameters:[{name:"*args",val:""}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/processing_utils.py#L1137"}}),ee=new wr({props:{anchor:"transformers.ProcessorMixin.prepare_and_validate_optional_call_args.example",$$slots:{default:[pn]},$$scope:{ctx:U}}}),te=new wr({props:{anchor:"transformers.ProcessorMixin.prepare_and_validate_optional_call_args.example-2",$$slots:{default:[mn]},$$scope:{ctx:U}}}),re=new wr({props:{anchor:"transformers.ProcessorMixin.prepare_and_validate_optional_call_args.example-3",$$slots:{default:[fn]},$$scope:{ctx:U}}}),je=new P({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:"tags",val:": typing.Optional[typing.List[str]] = None"},{name:"**deprecated_kwargs",val:""}],parametersDescription:[{anchor:"transformers.ProcessorMixin.push_to_hub.repo_id",description:`<strong>repo_id</strong> (<code>str</code>) &#x2014;
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Whether or not to use a temporary directory to store the files saved before they are pushed to the Hub.
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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 to make the repo private. If <code>None</code> (default), the repo will be public unless the organization&#x2019;s default is private. This value is ignored if the repo already exists.`,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"},{anchor:"transformers.ProcessorMixin.push_to_hub.tags",description:`<strong>tags</strong> (<code>List[str]</code>, <em>optional</em>) &#x2014;
List of tags to push on the Hub.`,name:"tags"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/utils/hub.py#L767"}}),se=new wr({props:{anchor:"transformers.ProcessorMixin.push_to_hub.example",$$slots:{default:[un]},$$scope:{ctx:U}}}),Xe=new P({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#L1075"}}),oe=new Go({props:{warning:!0,$$slots:{default:[gn]},$$scope:{ctx:U}}}),ze=new P({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/model#transformers.utils.PushToHubMixin.push_to_hub">push_to_hub()</a> method.`,name:"kwargs"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/processing_utils.py#L551"}}),ae=new Go({props:{$$slots:{default:[hn]},$$scope:{ctx:U}}}),Ze=new P({props:{name:"to_dict",anchor:"transformers.ProcessorMixin.to_dict",parameters:[],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/processing_utils.py#L484",returnDescription:`<script context="module">export const metadata = 'undefined';<\/script>


<p>Dictionary of all the attributes that make up this processor instance.</p>
`,returnType:`<script context="module">export const metadata = 'undefined';<\/script>


<p><code>Dict[str, Any]</code></p>
`}}),Ee=new P({props:{name:"to_json_file",anchor:"transformers.ProcessorMixin.to_json_file",parameters:[{name:"json_file_path",val:": typing.Union[str, os.PathLike]"}],parametersDescription:[{anchor:"transformers.ProcessorMixin.to_json_file.json_file_path",description:`<strong>json_file_path</strong> (<code>str</code> or <code>os.PathLike</code>) &#x2014;
Path to the JSON file in which this processor instance&#x2019;s parameters will be saved.`,name:"json_file_path"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/processing_utils.py#L535"}}),Ve=new P({props:{name:"to_json_string",anchor:"transformers.ProcessorMixin.to_json_string",parameters:[],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/processing_utils.py#L524",returnDescription:`<script context="module">export const metadata = 'undefined';<\/script>


<p>String containing all the attributes that make up this feature_extractor instance in JSON format.</p>
`,returnType:`<script context="module">export const metadata = 'undefined';<\/script>


<p><code>str</code></p>
`}}),De=new be({props:{title:"已弃用的processors",local:"transformers.DataProcessor",headingTag:"h2"}}),Be=new P({props:{name:"class transformers.DataProcessor",anchor:"transformers.DataProcessor",parameters:[],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/data/processors/utils.py#L80"}}),Se=new P({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"}}),Ne=new P({props:{name:"get_example_from_tensor_dict",anchor:"transformers.DataProcessor.get_example_from_tensor_dict",parameters:[{name:"tensor_dict",val:""}],parametersDescription:[{anchor:"transformers.DataProcessor.get_example_from_tensor_dict.tensor_dict",description:`<strong>tensor_dict</strong> &#x2014; Keys and values should match the corresponding Glue
tensorflow_dataset examples.`,name:"tensor_dict"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/data/processors/utils.py#L83"}}),Fe=new P({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"}}),Ge=new P({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"}}),We=new P({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"}}),Re=new P({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"}}),Qe=new P({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"}],parametersDescription:[{anchor:"transformers.InputExample.guid",description:"<strong>guid</strong> &#x2014; Unique id for the example.",name:"guid"},{anchor:"transformers.InputExample.text_a",description:`<strong>text_a</strong> &#x2014; string. The untokenized text of the first sequence. For single
sequence tasks, only this sequence must be specified.`,name:"text_a"},{anchor:"transformers.InputExample.text_b",description:`<strong>text_b</strong> &#x2014; (Optional) string. The untokenized text of the second sequence.
Only must be specified for sequence pair tasks.`,name:"text_b"},{anchor:"transformers.InputExample.label",description:`<strong>label</strong> &#x2014; (Optional) string. The label of the example. This should be
specified for train and dev examples, but not for test examples.`,name:"label"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/data/processors/utils.py#L29"}}),Ye=new P({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"}}),Ae=new P({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"}],parametersDescription:[{anchor:"transformers.InputFeatures.input_ids",description:"<strong>input_ids</strong> &#x2014; Indices of input sequence tokens in the vocabulary.",name:"input_ids"},{anchor:"transformers.InputFeatures.attention_mask",description:`<strong>attention_mask</strong> &#x2014; Mask to avoid performing attention on padding token indices.
Mask values selected in <code>[0, 1]</code>: Usually <code>1</code> for tokens that are NOT MASKED, <code>0</code> for MASKED (padded)
tokens.`,name:"attention_mask"},{anchor:"transformers.InputFeatures.token_type_ids",description:`<strong>token_type_ids</strong> &#x2014; (Optional) Segment token indices to indicate first and second
portions of the inputs. Only some models use them.`,name:"token_type_ids"},{anchor:"transformers.InputFeatures.label",description:`<strong>label</strong> &#x2014; (Optional) Label corresponding to the input. Int for classification problems,
float for regression problems.`,name:"label"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/data/processors/utils.py#L54"}}),Oe=new P({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"}}),Ke=new be({props:{title:"GLUE",local:"transformers.glue_convert_examples_to_features",headingTag:"h2"}}),at=new P({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"}],parametersDescription:[{anchor:"transformers.glue_convert_examples_to_features.examples",description:"<strong>examples</strong> &#x2014; List of <code>InputExamples</code> or <code>tf.data.Dataset</code> containing the examples.",name:"examples"},{anchor:"transformers.glue_convert_examples_to_features.tokenizer",description:"<strong>tokenizer</strong> &#x2014; Instance of a tokenizer that will tokenize the examples",name:"tokenizer"},{anchor:"transformers.glue_convert_examples_to_features.max_length",description:"<strong>max_length</strong> &#x2014; Maximum example length. Defaults to the tokenizer&#x2019;s max_len",name:"max_length"},{anchor:"transformers.glue_convert_examples_to_features.task",description:"<strong>task</strong> &#x2014; GLUE task",name:"task"},{anchor:"transformers.glue_convert_examples_to_features.label_list",description:"<strong>label_list</strong> &#x2014; List of labels. Can be obtained from the processor using the <code>processor.get_labels()</code> method",name:"label_list"},{anchor:"transformers.glue_convert_examples_to_features.output_mode",description:"<strong>output_mode</strong> &#x2014; String indicating the output mode. Either <code>regression</code> or <code>classification</code>",name:"output_mode"}],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>
`}}),nt=new be({props:{title:"XNLI",local:"xnli",headingTag:"h2"}}),ft=new be({props:{title:"SQuAD",local:"squad",headingTag:"h2"}}),ht=new be({props:{title:"Processors",local:"transformers.data.processors.squad.SquadProcessor",headingTag:"h3"}}),$t=new P({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"}}),bt=new P({props:{name:"get_dev_examples",anchor:"transformers.data.processors.squad.SquadProcessor.get_dev_examples",parameters:[{name:"data_dir",val:""},{name:"filename",val:" = None"}],parametersDescription:[{anchor:"transformers.data.processors.squad.SquadProcessor.get_dev_examples.data_dir",description:"<strong>data_dir</strong> &#x2014; Directory containing the data files used for training and evaluating.",name:"data_dir"},{anchor:"transformers.data.processors.squad.SquadProcessor.get_dev_examples.filename",description:`<strong>filename</strong> &#x2014; None by default, specify this if the evaluation file has a different name than the original one
which is <code>dev-v1.1.json</code> and <code>dev-v2.0.json</code> for squad versions 1.1 and 2.0 respectively.`,name:"filename"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/data/processors/squad.py#L629"}}),yt=new P({props:{name:"get_examples_from_dataset",anchor:"transformers.data.processors.squad.SquadProcessor.get_examples_from_dataset",parameters:[{name:"dataset",val:""},{name:"evaluate",val:" = False"}],parametersDescription:[{anchor:"transformers.data.processors.squad.SquadProcessor.get_examples_from_dataset.dataset",description:"<strong>dataset</strong> &#x2014; The tfds dataset loaded from <em>tensorflow_datasets.load(&#x201C;squad&#x201D;)</em>",name:"dataset"},{anchor:"transformers.data.processors.squad.SquadProcessor.get_examples_from_dataset.evaluate",description:"<strong>evaluate</strong> &#x2014; Boolean specifying if in evaluation mode or in training mode",name:"evaluate"}],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>
`}}),xe=new wr({props:{anchor:"transformers.data.processors.squad.SquadProcessor.get_examples_from_dataset.example",$$slots:{default:[_n]},$$scope:{ctx:U}}}),Mt=new P({props:{name:"get_train_examples",anchor:"transformers.data.processors.squad.SquadProcessor.get_train_examples",parameters:[{name:"data_dir",val:""},{name:"filename",val:" = None"}],parametersDescription:[{anchor:"transformers.data.processors.squad.SquadProcessor.get_train_examples.data_dir",description:"<strong>data_dir</strong> &#x2014; Directory containing the data files used for training and evaluating.",name:"data_dir"},{anchor:"transformers.data.processors.squad.SquadProcessor.get_train_examples.filename",description:`<strong>filename</strong> &#x2014; None by default, specify this if the training file has a different name than the original one
which is <code>train-v1.1.json</code> and <code>train-v2.0.json</code> for squad versions 1.1 and 2.0 respectively.`,name:"filename"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/data/processors/squad.py#L607"}}),Tt=new P({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"}],parametersDescription:[{anchor:"transformers.squad_convert_examples_to_features.examples",description:"<strong>examples</strong> &#x2014; list of <code>SquadExample</code>",name:"examples"},{anchor:"transformers.squad_convert_examples_to_features.tokenizer",description:'<strong>tokenizer</strong> &#x2014; an instance of a child of <a href="/docs/transformers/main/zh/main_classes/tokenizer#transformers.PreTrainedTokenizer">PreTrainedTokenizer</a>',name:"tokenizer"},{anchor:"transformers.squad_convert_examples_to_features.max_seq_length",description:"<strong>max_seq_length</strong> &#x2014; The maximum sequence length of the inputs.",name:"max_seq_length"},{anchor:"transformers.squad_convert_examples_to_features.doc_stride",description:"<strong>doc_stride</strong> &#x2014; The stride used when the context is too large and is split across several features.",name:"doc_stride"},{anchor:"transformers.squad_convert_examples_to_features.max_query_length",description:"<strong>max_query_length</strong> &#x2014; The maximum length of the query.",name:"max_query_length"},{anchor:"transformers.squad_convert_examples_to_features.is_training",description:"<strong>is_training</strong> &#x2014; whether to create features for model evaluation or model training.",name:"is_training"},{anchor:"transformers.squad_convert_examples_to_features.padding_strategy",description:"<strong>padding_strategy</strong> &#x2014; Default to &#x201C;max_length&#x201D;. Which padding strategy to use",name:"padding_strategy"},{anchor:"transformers.squad_convert_examples_to_features.return_dataset",description:`<strong>return_dataset</strong> &#x2014; Default False. Either &#x2018;pt&#x2019; or &#x2018;tf&#x2019;.
if &#x2018;pt&#x2019;: returns a torch.data.TensorDataset, if &#x2018;tf&#x2019;: returns a tf.data.Dataset`,name:"return_dataset"},{anchor:"transformers.squad_convert_examples_to_features.threads",description:"<strong>threads</strong> &#x2014; multiple processing threads.",name:"threads"}],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>
`}}),$e=new wr({props:{anchor:"transformers.squad_convert_examples_to_features.example",$$slots:{default:[xn]},$$scope:{ctx:U}}}),Pt=new be({props:{title:"Example使用",local:"example使用",headingTag:"h3"}}),It=new ye({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}}),Ut=new ye({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}}),qt=new cn({props:{source:"https://github.com/huggingface/transformers/blob/main/docs/source/zh/main_classes/processors.md"}}),{c(){d=n("meta"),w=s(),v=n("p"),m=s(),f($.$$.fragment),c=s(),T=n("p"),T.textContent=Wo,Cr=s(),Me=n("ul"),Me.innerHTML=Ro,Pr=s(),f(we.$$.fragment),kr=s(),Te=n("p"),Te.textContent=Qo,Ir=s(),Ce=n("p"),Ce.textContent=Yo,Jr=s(),b=n("div"),f(Pe.$$.fragment),Js=s(),Zt=n("p"),Zt.textContent=Ao,Us=s(),j=n("div"),f(ke.$$.fragment),Ls=s(),Et=n("p"),Et.innerHTML=Oo,qs=s(),Vt=n("p"),Vt.innerHTML=Ko,js=s(),Dt=n("p"),Dt.textContent=ea,Xs=s(),Y=n("div"),f(Ie.$$.fragment),zs=s(),Ht=n("p"),Ht.innerHTML=ta,Zs=s(),V=n("div"),f(Je.$$.fragment),Es=s(),Bt=n("p"),Bt.textContent=ra,Vs=s(),f(A.$$.fragment),Ds=s(),O=n("div"),f(Ue.$$.fragment),Hs=s(),St=n("p"),St.innerHTML=sa,Bs=s(),K=n("div"),f(Le.$$.fragment),Ss=s(),Nt=n("p"),Nt.textContent=oa,Ns=s(),k=n("div"),f(qe.$$.fragment),Fs=s(),Ft=n("p"),Ft.innerHTML=aa,Gs=s(),Gt=n("p"),Gt.innerHTML=na,Ws=s(),Wt=n("ul"),Wt.innerHTML=ia,Rs=s(),Rt=n("p"),Rt.textContent=la,Qs=s(),Qt=n("p"),Qt.innerHTML=ca,Ys=s(),f(ee.$$.fragment),As=s(),f(te.$$.fragment),Os=s(),f(re.$$.fragment),Ks=an("\nwhich 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