import{s as Jt,n as Qt,o as Ut}from"../chunks/scheduler.9991993c.js";import{S as Nt,i as Rt,g as o,s as a,r as d,A as Bt,h as s,f as r,c as n,j as v,u as c,x as l,k as $,y as t,a as p,v as u,d as f,t as h,w as g}from"../chunks/index.7fc9a5e7.js";import{D as _}from"../chunks/Docstring.8180f571.js";import{H as Ke}from"../chunks/Heading.e3de321f.js";function Zt(Pt){let F,Fe,De,Ce,A,ye,I,Dt="此页面列出了库为pipelines提供的所有实用程序功能。",Te,V,wt="其中大多数只有在您研究库中模型的代码时才有用。",Le,W,He,C,q,Xe,re,Ft='Base interface for handling arguments for each <a href="/docs/transformers/v4.39.1/zh/main_classes/pipelines#transformers.Pipeline">Pipeline</a>.',Ee,y,z,Ye,ae,Ct=`Handles arguments for zero-shot for text classification by turning each possible label into an NLI
premise/hypothesis pair.`,ke,P,j,et,ne,yt=`QuestionAnsweringPipeline requires the user to provide multiple arguments (i.e. question &amp; context) to be mapped to
internal <code>SquadExample</code>.`,tt,oe,Tt=`QuestionAnsweringArgumentHandler manages all the possible to create a <code>SquadExample</code> from the command-line
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Where to look for the input data.`,name:"input_path"},{anchor:"transformers.PipelineDataFormat.from_str.column",description:`<strong>column</strong> (<code>str</code>, <em>optional</em>) &#x2014;
The column to read.`,name:"column"},{anchor:"transformers.PipelineDataFormat.from_str.overwrite",description:`<strong>overwrite</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
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<p>The proper data format.</p>
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<p><a
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`}}),N=new _({props:{name:"save",anchor:"transformers.PipelineDataFormat.save",parameters:[{name:"data",val:": Union"}],parametersDescription:[{anchor:"transformers.PipelineDataFormat.save.data",description:"<strong>data</strong> (<code>dict</code> or list of <code>dict</code>) &#x2014; The data to store.",name:"data"}],source:"https://github.com/huggingface/transformers/blob/v4.39.1/src/transformers/pipelines/base.py#L494"}}),R=new _({props:{name:"save_binary",anchor:"transformers.PipelineDataFormat.save_binary",parameters:[{name:"data",val:": Union"}],parametersDescription:[{anchor:"transformers.PipelineDataFormat.save_binary.data",description:"<strong>data</strong> (<code>dict</code> or list of <code>dict</code>) &#x2014; The data to store.",name:"data"}],source:"https://github.com/huggingface/transformers/blob/v4.39.1/src/transformers/pipelines/base.py#L504",returnDescription:`<script context="module">export const metadata = 'undefined';<\/script>


<p>Path where the data has been saved.</p>
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<p><code>str</code></p>
`}}),B=new _({props:{name:"class transformers.CsvPipelineDataFormat",anchor:"transformers.CsvPipelineDataFormat",parameters:[{name:"output_path",val:": Optional"},{name:"input_path",val:": Optional"},{name:"column",val:": Optional"},{name:"overwrite",val:" = False"}],parametersDescription:[{anchor:"transformers.CsvPipelineDataFormat.output_path",description:"<strong>output_path</strong> (<code>str</code>) &#x2014; Where to save the outgoing data.",name:"output_path"},{anchor:"transformers.CsvPipelineDataFormat.input_path",description:"<strong>input_path</strong> (<code>str</code>) &#x2014; Where to look for the input data.",name:"input_path"},{anchor:"transformers.CsvPipelineDataFormat.column",description:"<strong>column</strong> (<code>str</code>) &#x2014; The column to read.",name:"column"},{anchor:"transformers.CsvPipelineDataFormat.overwrite",description:`<strong>overwrite</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
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