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: 'en' | 'cz' | 'de' | 'es' | 'fr' = 'en',

use_shell: bool | None = None,

params: Iterable[float] | None = None,

weights: Iterable[float] | None = None,

verbose: int = 0,

)[source]¶

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[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] = 1.0¶

min_value : ClassVar[float] = 0.0¶

reset() → None[source]¶

training : bool¶

update(

candidates: list[str],

mult_references: list[list[str]],

) → None[source]¶