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: bool = True, *, n: int = 4, sigma: float = 6.0, tokenizer: ~typing.Callable[[str], list[str]] = <method 'split' of 'str' objects>, return_tfidf: bool = False, scale: float = 10.0) → tuple[CIDErDScores, CIDErDScores] | 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 – The list of sentences to evaluate.

• mult_references – The list of list of sentences used as target.

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

• sigma – Standard deviation parameter used for gaussian penalty. defaults to 6.0.

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

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

• scale – 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.