aac_metrics.classes.bert_score_mrefs module

class BERTScoreMRefs(

return_all_scores: bool = True,

*,

model: str | Module = 'roberta-large',

device: str | device | None = 'cuda_if_available',

batch_size: int | None = 32,

num_threads: int = 0,

max_length: int = 64,

reset_state: bool = True,

idf: bool = False,

reduction: Literal['mean', 'max', 'min'] | Callable[[...], Tensor] = 'max',

filter_nan: bool = True,

verbose: int = 0,

)

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

BERTScore metric which supports multiple references.

The implementation is based on the bert_score implementation of torchmetrics.

• Paper: https://arxiv.org/pdf/1904.09675.pdf

For more information, see bert_score_mrefs().

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

extra_repr() → str

Return the extra representation of the module.

To print customized extra information, you should re-implement this method in your own modules. Both single-line and multi-line strings are acceptable.

full_state_update: ClassVar[bool | None] = False

get_output_names() → tuple[str, ...]

higher_is_better: ClassVar[bool | None] = True

is_differentiable: ClassVar[bool | None] = False

max_value: ClassVar[float] = 1.0

min_value: ClassVar[float] = 0.0

reset() → None

training: bool

update(

candidates: list[str],

mult_references: list[list[str]],

) → None