types

All data types used by Parameter, Optimizer, AdalComponent, and Trainer.

Classes

EvaluationResult([score, feedback])	A single evaluation of task pipeline response to a score in range [0, 1].
ParameterType(value[, names, module, ...])	Enum for the type of parameter to compute the loss with, and to inform the optimizer.
PromptData(id, name, data[, requires_opt])
TrainerResult(steps, val_scores, ...)
TrainerStepResult([step, val_score, ...])
TrainerValidateStats([max_score, min_score, ...])	A single evaluation of task pipeline response to a score in range [0, 1].

class ParameterType(value, names=None, *, module=None, qualname=None, type=None, start=1, boundary=None)

Bases: Enum

Enum for the type of parameter to compute the loss with, and to inform the optimizer.

The meaning of reach tuple is: 1. First element: the name of the parameter. 2. Second element: the description of the parameter. 3. Third element: whether the parameter is trainable.

To access each element, use the following: 1. name: ParameterType.PROMPT.value 2. description: ParameterType.PROMPT.description 3. trainable: ParameterType.PROMPT.default_trainable

PROMPT = 'prompt'

DEMOS = 'demos'

INPUT = 'input'

OUTPUT = 'output'

HYPERPARAM = 'hyperparam'

GENERATOR_OUTPUT = 'generator_output'

RETRIEVER_OUTPUT = 'retriever_output'

LOSS_OUTPUT = 'loss'

SUM_OUTPUT = 'sum'

NONE = 'none'

class EvaluationResult(score: float = 0.0, feedback: str = '')

Bases: DataClass

A single evaluation of task pipeline response to a score in range [0, 1].

score: float = 0.0

feedback: str = ''

class PromptData(id: str, name: str, data: str, requires_opt: bool = True)

Bases: object

id: str

name: str

data: str

requires_opt: bool = True

class TrainerStepResult(step: int = 0, val_score: float = None, test_score: float = None, attempted_val_score: float = None, prompt: List[optim.types.PromptData] = None)

Bases: DataClass

step: int = 0

val_score: float = None

test_score: float = None

attempted_val_score: float = None

prompt: List[PromptData] = None

class TrainerValidateStats(max_score: float = 0.0, min_score: float = 0.0, mean_of_score: float = 0.0, std_of_score: float = 0.0)

Bases: object

A single evaluation of task pipeline response to a score in range [0, 1].

max_score: float = 0.0

min_score: float = 0.0

mean_of_score: float = 0.0

std_of_score: float = 0.0

class TrainerResult(steps: List[int] = <factory>, val_scores: List[float] = <factory>, test_scores: List[float] = <factory>, step_results: List[optim.types.TrainerStepResult] = <factory>, effective_measure: Dict[str, Dict] = <factory>, validate_stats: optim.types.TrainerValidateStats = None, time_stamp: str = <factory>, total_time: float = 0.0, test_score: float = None, trainer_state: Dict[str, Any] = None)

Bases: DataClass

steps: List[int]

val_scores: List[float]

test_scores: List[float]

step_results: List[TrainerStepResult]

effective_measure: Dict[str, Dict]

validate_stats: TrainerValidateStats = None

time_stamp: str

total_time: float = 0.0

test_score: float = None

trainer_state: Dict[str, Any] = None