tfm.nlp.tasks.MaskedLMConfig

The model config.

Inherits From: TaskConfig, Config, ParamsDict

tfm.nlp.tasks.MaskedLMConfig(
    default_params: dataclasses.InitVar[Optional[Mapping[str, Any]]] = None,
    restrictions: dataclasses.InitVar[Optional[List[str]]] = None,
    init_checkpoint: str = '',
    model: bert.PretrainerConfig = dataclasses.field(default_factory=lambda : bert.PretrainerConfig(cls_heads=\n    [bert.ClsHeadConfig(inner_dim=768, num_classes=2, dropout_rate=0.1,\n    name='next_sentence')])),
    train_data: tfm.core.config_definitions.DataConfig = dataclasses.field(default_factory=cfg.DataConfig),
    validation_data: tfm.core.config_definitions.DataConfig = dataclasses.field(default_factory=cfg.DataConfig),
    name: Optional[str] = None,
    differential_privacy_config: Optional[tfm.core.base_task.DifferentialPrivacyConfig] = None,
    allow_image_summary: bool = False,
    scale_loss: bool = False
)

BUILDER

default_params Dataclass field restrictions Dataclass field init_checkpoint Dataclass field model Dataclass field train_data Dataclass field validation_data Dataclass field name Dataclass field differential_privacy_config Dataclass field allow_image_summary Dataclass field scale_loss Dataclass field

Methods

as_dict

View source

as_dict()

Returns a dict representation of params_dict.ParamsDict.

For the nested params_dict.ParamsDict, a nested dict will be returned.

from_args

View source

@classmethod
from_args(
    *args, **kwargs
)

Builds a config from the given list of arguments.

from_json

View source

@classmethod
from_json(
    file_path: str
)

Wrapper for from_yaml.

from_yaml

View source

@classmethod
from_yaml(
    file_path: str
)

get

View source

get(
    key, value=None
)

Accesses through built-in dictionary get method.

lock

View source

lock()

Makes the ParamsDict immutable.

override

View source

override(
    override_params, is_strict=True
)

Override the ParamsDict with a set of given params.

Args

override_params a dict or a ParamsDict specifying the parameters to be overridden. is_strict a boolean specifying whether override is strict or not. If True, keys in override_params must be present in the ParamsDict. If False, keys in override_params can be different from what is currently defined in the ParamsDict. In this case, the ParamsDict will be extended to include the new keys.

replace

View source

replace(
    **kwargs
)

Overrides/returns a unlocked copy with the current config unchanged.

validate

View source

validate()

Validate the parameters consistency based on the restrictions.

This method validates the internal consistency using the pre-defined list of restrictions. A restriction is defined as a string which specifies a binary operation. The supported binary operations are {'==', '!=', '<', '<=', '>', '>='}. Note that the meaning of these operators are consistent with the underlying Python immplementation. Users should make sure the define restrictions on their type make sense.

For example, for a ParamsDict like the following

a:
  a1: 1
  a2: 2
b:
  bb:
    bb1: 10
    bb2: 20
  ccc:
    a1: 1
    a3: 3

one can define two restrictions like this ['a.a1 == b.ccc.a1', 'a.a2 <= b.bb.bb2']

What it enforces are

  • a.a1 = 1 == b.ccc.a1 = 1
  • a.a2 = 2 <= b.bb.bb2 = 20

Raises

KeyError if any of the following happens (1) any of parameters in any of restrictions is not defined in ParamsDict, (2) any inconsistency violating the restriction is found. ValueError if the restriction defined in the string is not supported.

__contains__

View source

__contains__(
    key
)

Implements the membership test operator.

__eq__

__eq__(
    other
)

IMMUTABLE_TYPES (<class 'str'>, <class 'int'>, <class 'float'>, <class 'bool'>, <class 'NoneType'>) RESERVED_ATTR ['_locked', '_restrictions'] SEQUENCE_TYPES (<class 'list'>, <class 'tuple'>) allow_image_summary False default_params None differential_privacy_config None init_checkpoint '' model None name None restrictions None scale_loss False