aac_metrics.functional.cider_d module¶

class CIDErDScores¶

Bases: dict

cider_d : Tensor¶

cider_d(

candidates: list[str],

mult_references: list[list[str]],

return_all_scores: True = True,

*,

n: int = 4,

sigma: float = 6.0,

tokenizer: Callable[[str], list[str]] = str.split,

return_tfidf: bool = False,

scale: float = 10.0,

) → tuple[CIDErDScores, CIDErDScores][source]¶

cider_d(

candidates: list[str],

mult_references: list[list[str]],

return_all_scores: False,

*,

n: int = 4,

sigma: float = 6.0,

tokenizer: Callable[[str], list[str]] = str.split,

return_tfidf: bool = False,

scale: float = 10.0,

) → Tensor

Consensus-based Image Description Evaluation function.

• Paper: https://arxiv.org/pdf/1411.5726.pdf

Warning

This metric requires at least 2 candidates with 2 sets of references, otherwise it will raises a ValueError.

Parameters:¶

candidates: list[str]¶

The list of sentences to evaluate.

mult_references: list[list[str]]¶

The list of list of sentences used as target.

return_all_scores: True = True¶

return_all_scores: False

If True, returns a tuple containing the globals and locals scores. Otherwise returns a scalar tensor containing the main global score. defaults to True.

n: int = 4¶

Maximal number of n-grams taken into account. defaults to 4.

sigma: float = 6.0¶

Standard deviation parameter used for gaussian penalty. defaults to 6.0.

tokenizer: Callable[[str], list[str]] = str.split¶

The fast tokenizer used to split sentences into words. defaults to str.split.

return_tfidf: bool = False¶

If True, returns the list of dictionaries containing the tf-idf scores of n-grams in the sents_score output. defaults to False.

scale: float = 10.0¶

CIDEr-D score factor. defaults to 10.0.

Returns:¶

A tuple of globals and locals scores or a scalar tensor with the main global score.