aac_metrics.classes.fer module

class FER(
return_all_scores: bool = True,
*,
echecker: str | BERTFlatClassifier = 'echecker_clotho_audiocaps_base',
error_threshold: float = 0.9,
device: str | device | None = 'cuda_if_available',
batch_size: int | None = 32,
reset_state: bool = True,
return_probs: bool = False,
verbose: int = 0,
)[source]

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

Return Fluency Error Rate (FER) detected by a pre-trained BERT model.

For more information, see fer().

compute() → tuple[FERScores, FERScores] | 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] = False
is_differentiable : ClassVar[bool | None] = False
max_value : ClassVar[float] = 1.0
min_value : ClassVar[float] = -1.0
reset() → None[source]
training : bool
update(
candidates: list[str],
*args,
**kwargs,
) → None[source]