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,

)[source]¶

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

SPIDErFL class.

For more information, see spider_fl().

compute() → tuple[SPIDErFLScores, SPIDErFLScores] | Tensor[source]¶

extra_repr() → str[source]¶

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, ...][source]¶

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[source]¶

training : bool¶

update(

candidates: list[str],

mult_references: list[list[str]],

) → None[source]¶