aac_metrics.classes.spider_fl module

class SPIDErFL(

return_all_scores: bool = True,

*,

n: int = 4,

sigma: float = 6.0,

cache_path: str | Path | None = None,

java_path: str | Path | None = None,

tmp_path: str | Path | None = None,

n_threads: int | None = None,

java_max_memory: str = '8G',

timeout: None | int | Iterable[int] = None,

echecker: str | BERTFlatClassifier = 'echecker_clotho_audiocaps_base',

echecker_tokenizer: AutoTokenizer | None = None,

error_threshold: float = 0.9,

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

batch_size: int | None = 32,

reset_state: bool = True,

return_probs: bool = True,

penalty: float = 0.9,

verbose: int = 0,

)

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

SPIDErFL class.

For more information, see spider_fl().

compute() → tuple[SPIDErFLScores, SPIDErFLScores] | 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] = 5.5

min_value: ClassVar[float] = 0.0

reset() → None

training: bool

update(

candidates: list[str],

mult_references: list[list[str]],

) → None