Transformers documentation

通用工具

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通用工具

此页面列出了在utils.py文件中找到的所有Transformers通用实用函数。

其中大多数仅在您研究库中的通用代码时才有用。

Enums和namedtuples(命名元组)

class transformers.utils.ExplicitEnum

< >

( value names = None module = None qualname = None type = None start = 1 )

Enum with more explicit error message for missing values.

class transformers.utils.PaddingStrategy

< >

( value names = None module = None qualname = None type = None start = 1 )

Possible values for the padding argument in PreTrainedTokenizerBase.call(). Useful for tab-completion in an IDE.

class transformers.TensorType

< >

( value names = None module = None qualname = None type = None start = 1 )

Possible values for the return_tensors argument in PreTrainedTokenizerBase.call(). Useful for tab-completion in an IDE.

特殊的装饰函数

transformers.add_start_docstrings

< >

( *docstr )

transformers.utils.add_start_docstrings_to_model_forward

< >

( *docstr )

transformers.add_end_docstrings

< >

( *docstr )

transformers.utils.add_code_sample_docstrings

< >

( *docstr processor_class = None checkpoint = None output_type = None config_class = None mask = '[MASK]' qa_target_start_index = 14 qa_target_end_index = 15 model_cls = None modality = None expected_output = None expected_loss = None real_checkpoint = None revision = None )

transformers.utils.replace_return_docstrings

< >

( output_type = None config_class = None )

特殊的属性

class transformers.utils.cached_property

< >

( fget = None fset = None fdel = None doc = None )

Descriptor that mimics @property but caches output in member variable.

From tensorflow_datasets

Built-in in functools from Python 3.8.

其他实用程序

class transformers.utils._LazyModule

< >

( name: str module_file: str import_structure: typing.Dict[typing.FrozenSet[str], typing.Dict[str, typing.Set[str]]] module_spec: ModuleSpec = None extra_objects: typing.Dict[str, object] = None )

Module class that surfaces all objects but only performs associated imports when the objects are requested.

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