Buckets:
| import{s as Ut,n as Nt,o as Rt}from"../chunks/scheduler.bdbef820.js";import{S as Bt,i as Zt,g as s,s as n,r as d,A as Gt,h as i,f as r,c as a,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 g,w as h}from"../chunks/index.33f81d56.js";import{D as _}from"../chunks/Docstring.64554317.js";import{H as Ye,E as Kt}from"../chunks/EditOnGithub.a9246e21.js";function Xt(yt){let w,Fe,ye,Ce,S,Te,I,wt="이 페이지는 라이브러리에서 파이프라인을 위해 제공하는 모든 유틸리티 함수들을 나열합니다.",Le,V,Ft="이 함수들 대부분은 라이브러리 내 모델의 코드를 연구할 때만 유용합니다.",He,W,Ee,F,q,et,ne,Ct="Base interface for handling arguments for each <code>Pipeline</code>.",ke,C,j,tt,ae,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 & context) to be mapped to | |
| internal <code>SquadExample</code>.`,nt,se,Ht=`QuestionAnsweringArgumentHandler manages all the possible to create a <code>SquadExample</code> from the command-line | |
| supplied arguments.`,Ae,z,Oe,m,Q,at,ie,Et=`Base class for all the pipeline supported data format both for reading and writing. Supported data formats | |
| currently includes:`,ot,pe,kt="<li>JSON</li> <li>CSV</li> <li>stdin/stdout (pipe)</li>",st,le,Mt=`<code>PipelineDataFormat</code> also includes some utilities to work with multi-columns like mapping from datasets columns to | |
| pipelines keyword arguments through the <code>dataset_kwarg_1=dataset_column_1</code> format.`,it,L,U,pt,me,At='Creates an instance of the right subclass of <a href="/docs/transformers/pr_36049/ko/internal/pipelines_utils#transformers.PipelineDataFormat">PipelineDataFormat</a> depending on <code>format</code>.',lt,H,N,mt,de,Ot='Save the provided data object with the representation for the current <a href="/docs/transformers/pr_36049/ko/internal/pipelines_utils#transformers.PipelineDataFormat">PipelineDataFormat</a>.',dt,E,R,ct,ce,St="Save the provided data object as a pickle-formatted binary data on the disk.",Se,D,B,ut,ue,It="Support for pipelines using CSV data format.",ft,k,Z,gt,fe,Vt='Save the provided data object with the representation for the current <a href="/docs/transformers/pr_36049/ko/internal/pipelines_utils#transformers.PipelineDataFormat">PipelineDataFormat</a>.',Ie,y,G,ht,ge,Wt="Support for pipelines using JSON file format.",vt,M,K,$t,he,qt="Save the provided data object in a json file.",Ve,x,X,bt,ve,jt="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,A,Y,Pt,be,zt="Print the data.",We,ee,qe,T,te,Dt,_e,Qt="Raised by a <code>Pipeline</code> when handling <strong>call</strong>.",je,re,Je,we,ze;return S=new Ye({props:{title:"파이프라인을 위한 유틸리티",local:"utilities-for-pipelines",headingTag:"h1"}}),W=new Ye({props:{title:"인자 처리",local:"transformers.pipelines.ArgumentHandler ][ transformers.pipelines.ArgumentHandler",headingTag:"h2"}}),q=new _({props:{name:"class transformers.pipelines.ArgumentHandler",anchor:"transformers.pipelines.ArgumentHandler",parameters:[],source:"https://github.com/huggingface/transformers/blob/vr_36049/src/transformers/pipelines/base.py#L502"}}),j=new _({props:{name:"class transformers.pipelines.ZeroShotClassificationArgumentHandler",anchor:"transformers.pipelines.ZeroShotClassificationArgumentHandler",parameters:[],source:"https://github.com/huggingface/transformers/blob/vr_36049/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_36049/src/transformers/pipelines/question_answering.py#L150"}}),z=new Ye({props:{title:"데이터 형식",local:"transformers.PipelineDataFormat ][ transformers.PipelineDataFormat",headingTag:"h2"}}),Q=new _({props:{name:"class transformers.PipelineDataFormat",anchor:"transformers.PipelineDataFormat",parameters:[{name:"output_path",val:": typing.Optional[str]"},{name:"input_path",val:": typing.Optional[str]"},{name:"column",val:": typing.Optional[str]"},{name:"overwrite",val:": bool = False"}],parametersDescription:[{anchor:"transformers.PipelineDataFormat.output_path",description:"<strong>output_path</strong> (<code>str</code>) — Where to save the outgoing data.",name:"output_path"},{anchor:"transformers.PipelineDataFormat.input_path",description:"<strong>input_path</strong> (<code>str</code>) — Where to look for the input data.",name:"input_path"},{anchor:"transformers.PipelineDataFormat.column",description:"<strong>column</strong> (<code>str</code>) — 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>) — | |
| Whether or not to overwrite the <code>output_path</code>.`,name:"overwrite"}],source:"https://github.com/huggingface/transformers/blob/vr_36049/src/transformers/pipelines/base.py#L512"}}),U=new _({props:{name:"from_str",anchor:"transformers.PipelineDataFormat.from_str",parameters:[{name:"format",val:": str"},{name:"output_path",val:": typing.Optional[str]"},{name:"input_path",val:": typing.Optional[str]"},{name:"column",val:": typing.Optional[str]"},{name:"overwrite",val:" = False"}],parametersDescription:[{anchor:"transformers.PipelineDataFormat.from_str.format",description:`<strong>format</strong> (<code>str</code>) — | |
| The format of the desired pipeline. Acceptable values are <code>"json"</code>, <code>"csv"</code> or <code>"pipe"</code>.`,name:"format"},{anchor:"transformers.PipelineDataFormat.from_str.output_path",description:`<strong>output_path</strong> (<code>str</code>, <em>optional</em>) — | |
| Where to save the outgoing data.`,name:"output_path"},{anchor:"transformers.PipelineDataFormat.from_str.input_path",description:`<strong>input_path</strong> (<code>str</code>, <em>optional</em>) — | |
| 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>) — | |
| 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>) — | |
| Whether or not to overwrite the <code>output_path</code>.`,name:"overwrite"}],source:"https://github.com/huggingface/transformers/blob/vr_36049/src/transformers/pipelines/base.py#L589",returnDescription:`<script context="module">export const metadata = 'undefined';<\/script> | |
| <p>The proper data format.</p> | |
| `,returnType:`<script context="module">export const metadata = 'undefined';<\/script> | |
| <p><a | |
| href="/docs/transformers/pr_36049/ko/internal/pipelines_utils#transformers.PipelineDataFormat" | |
| >PipelineDataFormat</a></p> | |
| `}}),N=new _({props:{name:"save",anchor:"transformers.PipelineDataFormat.save",parameters:[{name:"data",val:": typing.Union[dict, typing.List[dict]]"}],parametersDescription:[{anchor:"transformers.PipelineDataFormat.save.data",description:"<strong>data</strong> (<code>dict</code> or list of <code>dict</code>) — The data to store.",name:"data"}],source:"https://github.com/huggingface/transformers/blob/vr_36049/src/transformers/pipelines/base.py#L561"}}),R=new _({props:{name:"save_binary",anchor:"transformers.PipelineDataFormat.save_binary",parameters:[{name:"data",val:": typing.Union[dict, typing.List[dict]]"}],parametersDescription:[{anchor:"transformers.PipelineDataFormat.save_binary.data",description:"<strong>data</strong> (<code>dict</code> or list of <code>dict</code>) — The data to store.",name:"data"}],source:"https://github.com/huggingface/transformers/blob/vr_36049/src/transformers/pipelines/base.py#L571",returnDescription:`<script context="module">export const metadata = 'undefined';<\/script> | |
| <p>Path where the data has been saved.</p> | |
| `,returnType:`<script context="module">export const metadata = 'undefined';<\/script> | |
| <p><code>str</code></p> | |
| `}}),B=new _({props:{name:"class transformers.CsvPipelineDataFormat",anchor:"transformers.CsvPipelineDataFormat",parameters:[{name:"output_path",val:": typing.Optional[str]"},{name:"input_path",val:": typing.Optional[str]"},{name:"column",val:": typing.Optional[str]"},{name:"overwrite",val:" = False"}],parametersDescription:[{anchor:"transformers.CsvPipelineDataFormat.output_path",description:"<strong>output_path</strong> (<code>str</code>) — Where to save the outgoing data.",name:"output_path"},{anchor:"transformers.CsvPipelineDataFormat.input_path",description:"<strong>input_path</strong> (<code>str</code>) — Where to look for the input data.",name:"input_path"},{anchor:"transformers.CsvPipelineDataFormat.column",description:"<strong>column</strong> (<code>str</code>) — 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>) — | |
| Whether or not to overwrite the <code>output_path</code>.`,name:"overwrite"}],source:"https://github.com/huggingface/transformers/blob/vr_36049/src/transformers/pipelines/base.py#L625"}}),Z=new _({props:{name:"save",anchor:"transformers.CsvPipelineDataFormat.save",parameters:[{name:"data",val:": typing.List[dict]"}],parametersDescription:[{anchor:"transformers.CsvPipelineDataFormat.save.data",description:"<strong>data</strong> (<code>List[dict]</code>) — The data to store.",name:"data"}],source:"https://github.com/huggingface/transformers/blob/vr_36049/src/transformers/pipelines/base.py#L655"}}),G=new _({props:{name:"class transformers.JsonPipelineDataFormat",anchor:"transformers.JsonPipelineDataFormat",parameters:[{name:"output_path",val:": typing.Optional[str]"},{name:"input_path",val:": typing.Optional[str]"},{name:"column",val:": typing.Optional[str]"},{name:"overwrite",val:" = False"}],parametersDescription:[{anchor:"transformers.JsonPipelineDataFormat.output_path",description:"<strong>output_path</strong> (<code>str</code>) — Where to save the outgoing data.",name:"output_path"},{anchor:"transformers.JsonPipelineDataFormat.input_path",description:"<strong>input_path</strong> (<code>str</code>) — Where to look for the input data.",name:"input_path"},{anchor:"transformers.JsonPipelineDataFormat.column",description:"<strong>column</strong> (<code>str</code>) — The column to read.",name:"column"},{anchor:"transformers.JsonPipelineDataFormat.overwrite",description:`<strong>overwrite</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) — | |
| Whether or not to overwrite the <code>output_path</code>.`,name:"overwrite"}],source:"https://github.com/huggingface/transformers/blob/vr_36049/src/transformers/pipelines/base.py#L669"}}),K=new _({props:{name:"save",anchor:"transformers.JsonPipelineDataFormat.save",parameters:[{name:"data",val:": dict"}],parametersDescription:[{anchor:"transformers.JsonPipelineDataFormat.save.data",description:"<strong>data</strong> (<code>dict</code>) — The data to store.",name:"data"}],source:"https://github.com/huggingface/transformers/blob/vr_36049/src/transformers/pipelines/base.py#L700"}}),X=new _({props:{name:"class transformers.PipedPipelineDataFormat",anchor:"transformers.PipedPipelineDataFormat",parameters:[{name:"output_path",val:": typing.Optional[str]"},{name:"input_path",val:": typing.Optional[str]"},{name:"column",val:": typing.Optional[str]"},{name:"overwrite",val:": bool = False"}],parametersDescription:[{anchor:"transformers.PipedPipelineDataFormat.output_path",description:"<strong>output_path</strong> (<code>str</code>) — Where to save the outgoing data.",name:"output_path"},{anchor:"transformers.PipedPipelineDataFormat.input_path",description:"<strong>input_path</strong> (<code>str</code>) — Where to look for the input data.",name:"input_path"},{anchor:"transformers.PipedPipelineDataFormat.column",description:"<strong>column</strong> (<code>str</code>) — The column to read.",name:"column"},{anchor:"transformers.PipedPipelineDataFormat.overwrite",description:`<strong>overwrite</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) — | |
| Whether or not to overwrite the <code>output_path</code>.`,name:"overwrite"}],source:"https://github.com/huggingface/transformers/blob/vr_36049/src/transformers/pipelines/base.py#L711"}}),Y=new _({props:{name:"save",anchor:"transformers.PipedPipelineDataFormat.save",parameters:[{name:"data",val:": dict"}],parametersDescription:[{anchor:"transformers.PipedPipelineDataFormat.save.data",description:"<strong>data</strong> (<code>dict</code>) — The data to store.",name:"data"}],source:"https://github.com/huggingface/transformers/blob/vr_36049/src/transformers/pipelines/base.py#L740"}}),ee=new Ye({props:{title:"유틸리티",local:"transformers.pipelines.PipelineException ][ transformers.pipelines.PipelineException",headingTag:"h2"}}),te=new _({props:{name:"class transformers.pipelines.PipelineException",anchor:"transformers.pipelines.PipelineException",parameters:[{name:"task",val:": str"},{name:"model",val:": str"},{name:"reason",val:": 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Xet Storage Details
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- de58139d721eb1151cee892fd6437a96851961c84801a1598026fe4546b80520
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