ROUGE Score¶

Module Interface¶

class torchmetrics.text.rouge.ROUGEScore(use_stemmer=False, normalizer=None, tokenizer=None, accumulate='best', rouge_keys=('rouge1', 'rouge2', 'rougeL', 'rougeLsum'), **kwargs)[source]

Calculate Rouge Score, used for automatic summarization.

This implementation should imitate the behaviour of the rouge-score package Python ROUGE Implementation

As input to forward and update the metric accepts the following input:

preds (Sequence): An iterable of predicted sentences or a single predicted sentence
target (Sequence): An iterable of target sentences or an iterable of interables of target sentences or a single target sentence

As output of forward and compute the metric returns the following output:

rouge (Dict): A dictionary of tensor rouge scores for each input str rouge key

Parameters

use_stemmer¶ (bool) – Use Porter stemmer to strip word suffixes to improve matching.
normalizer¶ (Optional[Callable[[str], str]]) – A user’s own normalizer function. If this is None, replacing any non-alpha-numeric characters with spaces is default. This function must take a str and return a str.
tokenizer¶ (Optional[Callable[[str], Sequence[str]]]) – A user’s own tokenizer function. If this is None, spliting by spaces is default This function must take a str and return Sequence[str]
accumulate¶ (Literal[‘avg’, ‘best’]) –
Useful in case of multi-reference rouge score.
- avg takes the avg of all references with respect to predictions
- best takes the best fmeasure score obtained between prediction and multiple corresponding references.
rouge_keys¶ (Union[str, Tuple[str, ...]]) – A list of rouge types to calculate. Keys that are allowed are rougeL, rougeLsum, and rouge1 through rouge9.
kwargs¶ (Any) – Additional keyword arguments, see Advanced metric settings for more info.

Example

>>> from torchmetrics.text.rouge import ROUGEScore
>>> preds = "My name is John"
>>> target = "Is your name John"
>>> rouge = ROUGEScore()
>>> from pprint import pprint
>>> pprint(rouge(preds, target))
{'rouge1_fmeasure': tensor(0.7500),
 'rouge1_precision': tensor(0.7500),
 'rouge1_recall': tensor(0.7500),
 'rouge2_fmeasure': tensor(0.),
 'rouge2_precision': tensor(0.),
 'rouge2_recall': tensor(0.),
 'rougeL_fmeasure': tensor(0.5000),
 'rougeL_precision': tensor(0.5000),
 'rougeL_recall': tensor(0.5000),
 'rougeLsum_fmeasure': tensor(0.5000),
 'rougeLsum_precision': tensor(0.5000),
 'rougeLsum_recall': tensor(0.5000)}

Raises

ValueError – If the python packages nltk is not installed.
ValueError – If any of the rouge_keys does not belong to the allowed set of keys.

Initializes internal Module state, shared by both nn.Module and ScriptModule.

Functional Interface¶

torchmetrics.functional.text.rouge.rouge_score(preds, target, accumulate='best', use_stemmer=False, normalizer=None, tokenizer=None, rouge_keys=('rouge1', 'rouge2', 'rougeL', 'rougeLsum'))[source]

Calculate Calculate Rouge Score , used for automatic summarization.

Parameters

preds¶ (Union[str, Sequence[str]]) – An iterable of predicted sentences or a single predicted sentence.
target¶ (Union[str, Sequence[str], Sequence[Sequence[str]]]) – An iterable of iterables of target sentences or an iterable of target sentences or a single target sentence.
accumulate¶ (Literal[‘avg’, ‘best’]) –
Useful incase of multi-reference rouge score.
- avg takes the avg of all references with respect to predictions
- best takes the best fmeasure score obtained between prediction and multiple corresponding references.
use_stemmer¶ (bool) – Use Porter stemmer to strip word suffixes to improve matching.
normalizer¶ (Optional[Callable[[str], str]]) – A user’s own normalizer function. If this is None, replacing any non-alpha-numeric characters with spaces is default. This function must take a str and return a str.
tokenizer¶ (Optional[Callable[[str], Sequence[str]]]) – A user’s own tokenizer function. If this is None, spliting by spaces is default This function must take a str and return Sequence[str]
rouge_keys¶ (Union[str, Tuple[str, ...]]) – A list of rouge types to calculate. Keys that are allowed are rougeL, rougeLsum, and rouge1 through rouge9.

Return type

Dict[str, Tensor]

Returns

Python dictionary of rouge scores for each input rouge key.

Example

>>> from torchmetrics.functional.text.rouge import rouge_score
>>> preds = "My name is John"
>>> target = "Is your name John"
>>> from pprint import pprint
>>> pprint(rouge_score(preds, target))
{'rouge1_fmeasure': tensor(0.7500),
 'rouge1_precision': tensor(0.7500),
 'rouge1_recall': tensor(0.7500),
 'rouge2_fmeasure': tensor(0.),
 'rouge2_precision': tensor(0.),
 'rouge2_recall': tensor(0.),
 'rougeL_fmeasure': tensor(0.5000),
 'rougeL_precision': tensor(0.5000),
 'rougeL_recall': tensor(0.5000),
 'rougeLsum_fmeasure': tensor(0.5000),
 'rougeLsum_precision': tensor(0.5000),
 'rougeLsum_recall': tensor(0.5000)}

Raises

ModuleNotFoundError – If the python package nltk is not installed.
ValueError – If any of the rouge_keys does not belong to the allowed set of keys.

References

[1] ROUGE: A Package for Automatic Evaluation of Summaries by Chin-Yew Lin. https://aclanthology.org/W04-1013/