aac_metrics.classes.vocab module

class Vocab(return_all_scores: bool = True, *, seed: None | int | ~torch._C.Generator = 1234, tokenizer: ~typing.Callable[[str], list[str]] = <method 'split' of 'str' objects>, dtype: ~torch.dtype = torch.float64, pop_strategy: ~typing.Literal['max', 'min'] | int = 'max', verbose: int = 0)

Bases: AACMetric[tuple[VocabScores, VocabScores] | Tensor]

VocabStats class.

For more information, see vocab().

compute() → tuple[VocabScores, VocabScores] | Tensor

full_state_update: ClassVar[bool | None] = False

get_output_names() → tuple[str, ...]

higher_is_better: ClassVar[bool | None] = None

is_differentiable: ClassVar[bool | None] = False

max_value: ClassVar[float] = inf

min_value: ClassVar[float] = 0.0

reset() → None

training: bool

update(

candidates: list[str],

mult_references: list[list[str]] | None = None,

) → None