tfm.nlp.tasks.TaggingTask

Task object for tagging (e.g., NER or POS).

Inherits From: Task

tfm.nlp.tasks.TaggingTask(
    params, logging_dir: Optional[str] = None, name: Optional[str] = None
)

Args
params	the task configuration instance, which can be any of dataclass, ConfigDict, namedtuple, etc.
logging_dir	a string pointing to where the model, summaries etc. will be saved. You can also write additional stuff in this directory.
name	the task name.

Attributes
logging_dir
task_config

Methods

aggregate_logs

View source

aggregate_logs(
    state=None, step_outputs=None
)

Aggregates over logs returned from a validation step.

build_inputs

View source

build_inputs(
    params: tfm.core.config_definitions.DataConfig,
    input_context=None
)

Returns tf.data.Dataset for sentence_prediction task.

build_losses

View source

build_losses(
    labels, model_outputs, aux_losses=None
) -> tf.Tensor

Standard interface to compute losses.

Args
labels	optional label tensors.
model_outputs	a nested structure of output tensors.
aux_losses	auxiliary loss tensors, i.e. losses in keras.Model.

Returns
The total loss tensor.

build_metrics

View source

build_metrics(
    training: bool = True
)

Gets streaming metrics for training/validation.

build_model

View source

build_model()

[Optional] Creates model architecture.

Returns
A model instance.

create_optimizer

View source

@classmethod
create_optimizer(
    optimizer_config: tfm.optimization.OptimizationConfig,
    runtime_config: Optional[tfm.core.base_task.RuntimeConfig] = None,
    dp_config: Optional[tfm.core.base_task.DifferentialPrivacyConfig] = None
)

Creates an TF optimizer from configurations.

Args
optimizer_config	the parameters of the Optimization settings.
runtime_config	the parameters of the runtime.
dp_config	the parameter of differential privacy.

Returns
A tf.optimizers.Optimizer object.

inference_step

View source

inference_step(
    inputs, model: tf.keras.Model
)

Performs the forward step.

initialize

View source

initialize(
    model: tf.keras.Model
)

[Optional] A callback function used as CheckpointManager's init_fn.

This function will be called when no checkpoint is found for the model. If there is a checkpoint, the checkpoint will be loaded and this function will not be called. You can use this callback function to load a pretrained checkpoint, saved under a directory other than the model_dir.

Args
model	The keras.Model built or used by this task.

process_compiled_metrics

View source

process_compiled_metrics(
    compiled_metrics, labels, model_outputs
)

Process and update compiled_metrics.

call when using compile/fit API.

Args
compiled_metrics	the compiled metrics (model.compiled_metrics).
labels	a tensor or a nested structure of tensors.
model_outputs	a tensor or a nested structure of tensors. For example, output of the keras model built by self.build_model.

process_metrics

View source

process_metrics(
    metrics, labels, model_outputs, **kwargs
)

Process and update metrics.

Called when using custom training loop API.

Args
metrics	a nested structure of metrics objects. The return of function self.build_metrics.
labels	a tensor or a nested structure of tensors.
model_outputs	a tensor or a nested structure of tensors. For example, output of the keras model built by self.build_model.
**kwargs	other args.

reduce_aggregated_logs

View source

reduce_aggregated_logs(
    aggregated_logs, global_step=None
)

Reduces aggregated logs over validation steps.

train_step

View source

train_step(
    inputs,
    model: tf.keras.Model,
    optimizer: tf.keras.optimizers.Optimizer,
    metrics=None
)

Does forward and backward.

With distribution strategies, this method runs on devices.

Args
inputs	a dictionary of input tensors.
model	the model, forward pass definition.
optimizer	the optimizer for this training step.
metrics	a nested structure of metrics objects.

Returns
A dictionary of logs.

validation_step

View source

validation_step(
    inputs, model: tf.keras.Model, metrics=None
)

Validatation step.

Args
inputs	a dictionary of input tensors.
model	the keras.Model.
metrics	a nested structure of metrics objects.

Returns
A dictionary of logs.

Class Variables
loss	'loss'