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| # Copyright 2020 The HuggingFace Datasets Authors. | |
| # | |
| # Licensed under the Apache License, Version 2.0 (the "License"); | |
| # you may not use this file except in compliance with the License. | |
| # You may obtain a copy of the License at | |
| # | |
| # http://www.apache.org/licenses/LICENSE-2.0 | |
| # | |
| # Unless required by applicable law or agreed to in writing, software | |
| # distributed under the License is distributed on an "AS IS" BASIS, | |
| # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. | |
| # See the License for the specific language governing permissions and | |
| # limitations under the License. | |
| """ ROUGE metric from Google Research github repo. """ | |
| # The dependencies in https://github.com/google-research/google-research/blob/master/rouge/requirements.txt | |
| import absl # Here to have a nice missing dependency error message early on | |
| import nltk # Here to have a nice missing dependency error message early on | |
| import numpy # Here to have a nice missing dependency error message early on | |
| import six # Here to have a nice missing dependency error message early on | |
| from rouge_score import rouge_scorer, scoring | |
| import datasets | |
| _CITATION = """\ | |
| @inproceedings{lin-2004-rouge, | |
| title = "{ROUGE}: A Package for Automatic Evaluation of Summaries", | |
| author = "Lin, Chin-Yew", | |
| booktitle = "Text Summarization Branches Out", | |
| month = jul, | |
| year = "2004", | |
| address = "Barcelona, Spain", | |
| publisher = "Association for Computational Linguistics", | |
| url = "https://www.aclweb.org/anthology/W04-1013", | |
| pages = "74--81", | |
| } | |
| """ | |
| _DESCRIPTION = """\ | |
| ROUGE, or Recall-Oriented Understudy for Gisting Evaluation, is a set of metrics and a software package used for | |
| evaluating automatic summarization and machine translation software in natural language processing. | |
| The metrics compare an automatically produced summary or translation against a reference or a set of references (human-produced) summary or translation. | |
| Note that ROUGE is case insensitive, meaning that upper case letters are treated the same way as lower case letters. | |
| This metrics is a wrapper around Google Research reimplementation of ROUGE: | |
| https://github.com/google-research/google-research/tree/master/rouge | |
| """ | |
| _KWARGS_DESCRIPTION = """ | |
| Calculates average rouge scores for a list of hypotheses and references | |
| Args: | |
| predictions: list of predictions to score. Each predictions | |
| should be a string with tokens separated by spaces. | |
| references: list of reference for each prediction. Each | |
| reference should be a string with tokens separated by spaces. | |
| rouge_types: A list of rouge types to calculate. | |
| Valid names: | |
| `"rouge{n}"` (e.g. `"rouge1"`, `"rouge2"`) where: {n} is the n-gram based scoring, | |
| `"rougeL"`: Longest common subsequence based scoring. | |
| `"rougeLSum"`: rougeLsum splits text using `"\n"`. | |
| See details in https://github.com/huggingface/datasets/issues/617 | |
| use_stemmer: Bool indicating whether Porter stemmer should be used to strip word suffixes. | |
| use_agregator: Return aggregates if this is set to True | |
| Returns: | |
| rouge1: rouge_1 (precision, recall, f1), | |
| rouge2: rouge_2 (precision, recall, f1), | |
| rougeL: rouge_l (precision, recall, f1), | |
| rougeLsum: rouge_lsum (precision, recall, f1) | |
| Examples: | |
| >>> rouge = datasets.load_metric('rouge') | |
| >>> predictions = ["hello there", "general kenobi"] | |
| >>> references = ["hello there", "general kenobi"] | |
| >>> results = rouge.compute(predictions=predictions, references=references) | |
| >>> print(list(results.keys())) | |
| ['rouge1', 'rouge2', 'rougeL', 'rougeLsum'] | |
| >>> print(results["rouge1"]) | |
| AggregateScore(low=Score(precision=1.0, recall=1.0, fmeasure=1.0), mid=Score(precision=1.0, recall=1.0, fmeasure=1.0), high=Score(precision=1.0, recall=1.0, fmeasure=1.0)) | |
| >>> print(results["rouge1"].mid.fmeasure) | |
| 1.0 | |
| """ | |
| class Rouge(datasets.Metric): | |
| def _info(self): | |
| return datasets.MetricInfo( | |
| description=_DESCRIPTION, | |
| citation=_CITATION, | |
| inputs_description=_KWARGS_DESCRIPTION, | |
| features=datasets.Features( | |
| { | |
| "predictions": datasets.Value("string", id="sequence"), | |
| "references": datasets.Value("string", id="sequence"), | |
| } | |
| ), | |
| codebase_urls=["https://github.com/google-research/google-research/tree/master/rouge"], | |
| reference_urls=[ | |
| "https://en.wikipedia.org/wiki/ROUGE_(metric)", | |
| "https://github.com/google-research/google-research/tree/master/rouge", | |
| ], | |
| ) | |
| def _compute(self, predictions, references, rouge_types=None, use_agregator=True, use_stemmer=False): | |
| if rouge_types is None: | |
| rouge_types = ["rouge1", "rouge2", "rougeL", "rougeLsum"] | |
| scorer = rouge_scorer.RougeScorer(rouge_types=rouge_types, use_stemmer=use_stemmer) | |
| if use_agregator: | |
| aggregator = scoring.BootstrapAggregator() | |
| else: | |
| scores = [] | |
| for ref, pred in zip(references, predictions): | |
| score = scorer.score(ref, pred) | |
| if use_agregator: | |
| aggregator.add_scores(score) | |
| else: | |
| scores.append(score) | |
| if use_agregator: | |
| result = aggregator.aggregate() | |
| else: | |
| result = {} | |
| for key in scores[0]: | |
| result[key] = list(score[key] for score in scores) | |
| return result | |