aac_metrics.classes.sbert_sim module

class SBERTSim(

return_all_scores: bool = True,

*,

sbert_model: str | SentenceTransformer = 'paraphrase-TinyBERT-L6-v2',

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

batch_size: int | None = 32,

reset_state: bool = True,

verbose: int = 0,

)

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

Cosine-similarity of the Sentence-BERT embeddings.

• Paper: https://arxiv.org/abs/1908.10084

• Original implementation: https://github.com/blmoistawinde/fense

For more information, see sbert().

compute() → tuple[SBERTSimScores, SBERTSimScores] | 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] = -1.0

reset() → None

training: bool

update(

candidates: list[str],

mult_references: list[list[str]],

) → None