transformers/pr_33576/ja/_app/immutable/nodes/25.00a2517c.js

import{s as Qt,n as Nt,o as Rt}from"../chunks/scheduler.9bc65507.js";import{S as Bt,i as Zt,g as s,s as a,r as d,A as Gt,h as i,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.707bf1b6.js";import{D as _}from"../chunks/Docstring.570ddb64.js";import{H as Ye,E as Kt}from"../chunks/EditOnGithub.922df6ba.js";function Xt(wt){let F,Ce,we,ye,O,Te,S,Ft="このページには、ライブラリがパイプラインに提供するすべてのユーティリティ関数がリストされます。",Le,I,Ct="これらのほとんどは、ライブラリ内のモデルのコードを研究する場合にのみ役に立ちます。",He,V,Ee,C,W,et,ae,yt='Base interface for handling arguments for each <a href="/docs/transformers/pr_33576/ja/main_classes/pipelines#transformers.Pipeline">Pipeline</a>.',ke,y,q,tt,ne,Tt=`Handles arguments for zero-shot for text classification by turning each possible label into an NLI
premise/hypothesis pair.`,Me,P,J,rt,oe,Lt=`QuestionAnsweringPipeline requires the user to provide multiple arguments (i.e. question &amp; context) to be mapped to
internal <code>SquadExample</code>.`,at,se,Ht=`QuestionAnsweringArgumentHandler manages all the possible to create a <code>SquadExample</code> from the command-line
supplied arguments.`,je,z,Ae,m,U,nt,ie,Et=`Base class for all the pipeline supported data format both for reading and writing. Supported data formats
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pipelines keyword arguments through the <code>dataset_kwarg_1=dataset_column_1</code> format.`,it,L,Q,pt,me,jt='Creates an instance of the right subclass of <a href="/docs/transformers/pr_33576/ja/internal/pipelines_utils#transformers.PipelineDataFormat">PipelineDataFormat</a> depending on <code>format</code>.',lt,H,N,mt,de,At='Save the provided data object with the representation for the current <a href="/docs/transformers/pr_33576/ja/internal/pipelines_utils#transformers.PipelineDataFormat">PipelineDataFormat</a>.',dt,E,R,ct,ce,Ot="Save the provided data object as a pickle-formatted binary data on the disk.",Oe,D,B,ut,ue,St="Support for pipelines using CSV data format.",ft,k,Z,ht,fe,It='Save the provided data object with the representation for the current <a href="/docs/transformers/pr_33576/ja/internal/pipelines_utils#transformers.PipelineDataFormat">PipelineDataFormat</a>.',Se,w,G,gt,he,Vt="Support for pipelines using JSON file format.",vt,M,K,$t,ge,Wt="Save the provided data object in a json file.",Ie,x,X,bt,ve,qt="Read data from piped input to the python process. For multi columns data, columns should separated by",_t,$e,Jt="If columns are provided, then the output will be a dictionary with {column_x: value_x}",xt,j,Y,Pt,be,zt="Print the data.",Ve,ee,We,T,te,Dt,_e,Ut='Raised by a <a href="/docs/transformers/pr_33576/ja/main_classes/pipelines#transformers.Pipeline">Pipeline</a> when handling <strong>call</strong>.',qe,re,Je,Fe,ze;return O=new Ye({props:{title:"パイプライン用のユーティリティ",local:"パイプライン用のユーティリティ",headingTag:"h1"}}),V=new Ye({props:{title:"Argument handling",local:"transformers.pipelines.ArgumentHandler",headingTag:"h2"}}),W=new _({props:{name:"class transformers.pipelines.ArgumentHandler",anchor:"transformers.pipelines.ArgumentHandler",parameters:[],source:"https://github.com/huggingface/transformers/blob/vr_33576/src/transformers/pipelines/base.py#L444"}}),q=new _({props:{name:"class transformers.pipelines.ZeroShotClassificationArgumentHandler",anchor:"transformers.pipelines.ZeroShotClassificationArgumentHandler",parameters:[],source:"https://github.com/huggingface/transformers/blob/vr_33576/src/transformers/pipelines/zero_shot_classification.py#L14"}}),J=new _({props:{name:"class transformers.pipelines.QuestionAnsweringArgumentHandler",anchor:"transformers.pipelines.QuestionAnsweringArgumentHandler",parameters:[],source:"https://github.com/huggingface/transformers/blob/vr_33576/src/transformers/pipelines/question_answering.py#L150"}}),z=new Ye({props:{title:"Data format",local:"transformers.PipelineDataFormat",headingTag:"h2"}}),U=new _({props:{name:"class transformers.PipelineDataFormat",anchor:"transformers.PipelineDataFormat",parameters:[{name:"output_path",val:": Optional"},{name:"input_path",val:": Optional"},{name:"column",val:": Optional"},{name:"overwrite",val:": bool = False"}],parametersDescription:[{anchor:"transformers.PipelineDataFormat.output_path",description:"<strong>output_path</strong> (<code>str</code>) &#x2014; Where to save the outgoing data.",name:"output_path"},{anchor:"transformers.PipelineDataFormat.input_path",description:"<strong>input_path</strong> (<code>str</code>) &#x2014; Where to look for the input data.",name:"input_path"},{anchor:"transformers.PipelineDataFormat.column",description:"<strong>column</strong> (<code>str</code>) &#x2014; The column to read.",name:"column"},{anchor:"transformers.PipelineDataFormat.overwrite",description:`<strong>overwrite</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to overwrite the <code>output_path</code>.`,name:"overwrite"}],source:"https://github.com/huggingface/transformers/blob/vr_33576/src/transformers/pipelines/base.py#L454"}}),Q=new _({props:{name:"from_str",anchor:"transformers.PipelineDataFormat.from_str",parameters:[{name:"format",val:": str"},{name:"output_path",val:": Optional"},{name:"input_path",val:": Optional"},{name:"column",val:": Optional"},{name:"overwrite",val:" = False"}],parametersDescription:[{anchor:"transformers.PipelineDataFormat.from_str.format",description:`<strong>format</strong> (<code>str</code>) &#x2014;
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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>
`,returnType:`<script context="module">export const metadata = 'undefined';<\/script>


<p><a
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<p><code>str</code></p>
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