mediapipe_model_maker.text_classifier.BertHParams

The hyperparameters for a Bert Classifier.

Inherits From: BaseHParams

mediapipe_model_maker.text_classifier.BertHParams(
    learning_rate: float = 3e-05,
    batch_size: int = 48,
    epochs: int = 2,
    steps_per_epoch: Optional[int] = None,
    class_weights: Optional[Mapping[int, float]] = None,
    shuffle: bool = False,
    repeat: bool = False,
    export_dir: str = tempfile.mkdtemp(),
    distribution_strategy: str = 'off',
    num_gpus: int = 0,
    tpu: str = '',
    end_learning_rate: float = 0.0,
    optimizer: mediapipe_model_maker.text_classifier.BertOptimizer = mediapipe_model_maker.text_classifier.BertHParams.optimizer,
    weight_decay: float = 0.01,
    desired_precisions: Sequence[float] = dataclasses.field(default_factory=list),
    desired_recalls: Sequence[float] = dataclasses.field(default_factory=list),
    gamma: float = 2.0,
    tokenizer: mediapipe_model_maker.text_classifier.SupportedBertTokenizers = mediapipe_model_maker.text_classifier.BertHParams.tokenizer,
    checkpoint_frequency: int = 0
)

Attributes
learning_rate	Learning rate to use for gradient descent training.
end_learning_rate	End learning rate for linear decay. Defaults to 0.
batch_size	Batch size for training. Defaults to 48.
epochs	Number of training iterations over the dataset. Defaults to 2.
optimizer	Optimizer to use for training. Supported values are defined in BertOptimizer enum: ADAMW and LAMB.
weight_decay	Weight decay of the optimizer. Defaults to 0.01.
desired_precisions	If specified, adds a RecallAtPrecision metric per desired_precisions[i] entry which tracks the recall given the constraint on precision. Only supported for binary classification.
desired_recalls	If specified, adds a PrecisionAtRecall metric per desired_recalls[i] entry which tracks the precision given the constraint on recall. Only supported for binary classification.
gamma	Gamma parameter for focal loss. To use cross entropy loss, set this value to 0. Defaults to 2.0.
tokenizer	Tokenizer to use for preprocessing. Must be one of the enum options of SupportedBertTokenizers. Defaults to FULL_TOKENIZER.
checkpoint_frequency	Frequency(in epochs) of saving checkpoints during training. Defaults to 0 which does not save training checkpoints.
steps_per_epoch	Dataclass field
class_weights	Dataclass field
shuffle	Dataclass field
repeat	Dataclass field
export_dir	Dataclass field
distribution_strategy	Dataclass field
num_gpus	Dataclass field
tpu	Dataclass field

Methods

get_strategy

View source

get_strategy()

__eq__

__eq__(
    other
)

Class Variables
batch_size	48
checkpoint_frequency	0
class_weights	None
distribution_strategy	'off'
end_learning_rate	0.0
epochs	2
export_dir	'/tmpfs/tmp/tmpnt_h4p9w'
gamma	2.0
learning_rate	3e-05
num_gpus	0
optimizer	<BertOptimizer.ADAMW: 'adamw'>
repeat	False
shuffle	False
steps_per_epoch	None
tokenizer	<SupportedBertTokenizers.FULL_TOKENIZER: 'fulltokenizer'>
tpu	''
weight_decay	0.01