aac_metrics.classes.meteor module

class METEOR(

return_all_scores: bool = True,

*,

cache_path: str | Path | None = None,

java_path: str | Path | None = None,

java_max_memory: str = '2G',

language: Literal['en', 'cz', 'de', 'es', 'fr'] = 'en',

use_shell: bool | None = None,

params: Iterable[float] | None = None,

weights: Iterable[float] | None = None,

verbose: int = 0,

)

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

Metric for Evaluation of Translation with Explicit ORdering metric class.

• Paper: https://dl.acm.org/doi/pdf/10.5555/1626355.1626389

• Documentation: https://www.cs.cmu.edu/~alavie/METEOR/README.html

For more information, see meteor().

compute() → tuple[METEORScores, METEORScores] | 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