#
파이프라인을 위한 유틸리티

이 페이지는 라이브러리에서 파이프라인을 위해 제공하는 모든 유틸리티 함수들을 나열합니다.

이 함수들 대부분은 라이브러리 내 모델의 코드를 연구할 때만 유용합니다.

##
인자 처리

###
class transformers.pipelines.ArgumentHandler

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( )

Base interface for handling arguments for each `Pipeline`.

###
class transformers.pipelines.ZeroShotClassificationArgumentHandler

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( )

Handles arguments for zero-shot for text classification by turning each possible label into an NLI premise/hypothesis pair.

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class transformers.pipelines.QuestionAnsweringArgumentHandler

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( )

QuestionAnsweringPipeline requires the user to provide multiple arguments (i.e. question & context) to be mapped to internal `SquadExample`.

QuestionAnsweringArgumentHandler manages all the possible to create a `SquadExample` from the command-line supplied arguments.

##
데이터 형식

###
class transformers.PipelineDataFormat

< source >

( output_path: typing.Optional[str] input_path: typing.Optional[str] column: typing.Optional[str] overwrite: bool = False )

Parameters

- output_path (`str`) — Where to save the outgoing data.
- input_path (`str`) — Where to look for the input data.
- column (`str`) — The column to read.
- overwrite (`bool`, optional, defaults to `False`) — Whether or not to overwrite the `output_path`.

Base class for all the pipeline supported data format both for reading and writing. Supported data formats currently includes:

- JSON
- CSV
- stdin/stdout (pipe)

`PipelineDataFormat` also includes some utilities to work with multi-columns like mapping from datasets columns to pipelines keyword arguments through the `dataset_kwarg_1=dataset_column_1` format.

####
from_str

< source >

( format: str output_path: typing.Optional[str] input_path: typing.Optional[str] column: typing.Optional[str] overwrite = False ) →
PipelineDataFormat

Parameters

- format (`str`) — The format of the desired pipeline. Acceptable values are `"json"`, `"csv"` or `"pipe"`.
- output_path (`str`, optional) — Where to save the outgoing data.
- input_path (`str`, optional) — Where to look for the input data.
- column (`str`, optional) — The column to read.
- overwrite (`bool`, optional, defaults to `False`) — Whether or not to overwrite the `output_path`.

Returns

PipelineDataFormat

The proper data format.

Creates an instance of the right subclass of PipelineDataFormat depending on `format`.

####
save

< source >

( data: typing.Union[dict, typing.List[dict]] )

Parameters

- data (`dict` or list of `dict`) — The data to store.

Save the provided data object with the representation for the current PipelineDataFormat.

####
save_binary

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( data: typing.Union[dict, typing.List[dict]] ) →
`str`

Parameters

- data (`dict` or list of `dict`) — The data to store.

Returns

`str`

Path where the data has been saved.

Save the provided data object as a pickle-formatted binary data on the disk.

###
class transformers.CsvPipelineDataFormat

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( output_path: typing.Optional[str] input_path: typing.Optional[str] column: typing.Optional[str] overwrite = False )

Parameters

- output_path (`str`) — Where to save the outgoing data.
- input_path (`str`) — Where to look for the input data.
- column (`str`) — The column to read.
- overwrite (`bool`, optional, defaults to `False`) — Whether or not to overwrite the `output_path`.

Support for pipelines using CSV data format.

####
save

< source >

( data: typing.List[dict] )

Parameters

- data (`List[dict]`) — The data to store.

Save the provided data object with the representation for the current PipelineDataFormat.

###
class transformers.JsonPipelineDataFormat

< source >

( output_path: typing.Optional[str] input_path: typing.Optional[str] column: typing.Optional[str] overwrite = False )

Parameters

- output_path (`str`) — Where to save the outgoing data.
- input_path (`str`) — Where to look for the input data.
- column (`str`) — The column to read.
- overwrite (`bool`, optional, defaults to `False`) — Whether or not to overwrite the `output_path`.

Support for pipelines using JSON file format.

####
save

< source >

( data: dict )

Parameters

- data (`dict`) — The data to store.

Save the provided data object in a json file.

###
class transformers.PipedPipelineDataFormat

< source >

( output_path: typing.Optional[str] input_path: typing.Optional[str] column: typing.Optional[str] overwrite: bool = False )

Parameters

- output_path (`str`) — Where to save the outgoing data.
- input_path (`str`) — Where to look for the input data.
- column (`str`) — The column to read.
- overwrite (`bool`, optional, defaults to `False`) — Whether or not to overwrite the `output_path`.

Read data from piped input to the python process. For multi columns data, columns should separated by

If columns are provided, then the output will be a dictionary with {column_x: value_x}

####
save

< source >

( data: dict )

Parameters

- data (`dict`) — The data to store.

Print the data.

##
유틸리티

###
class transformers.pipelines.PipelineException

< source >

( task: str model: str reason: str )

Parameters

- task (`str`) — The task of the pipeline.
- model (`str`) — The model used by the pipeline.
- reason (`str`) — The error message to display.

Raised by a `Pipeline` when handling call.

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