Spaces:
Running
Running
| # Copyright 2020 The HuggingFace Evaluate Authors. | |
| # Modified in 2026 by SyntaxSpirits: scores with rouge-score-rs instead of rouge-score. | |
| # | |
| # 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 computed with rouge-score-rs, a Rust implementation of Google's rouge-score. """ | |
| import datasets | |
| import numpy # Here to have a nice missing dependency error message early on | |
| import rouge_score_rs | |
| from packaging import version | |
| from rouge_score_rs import rouge_scorer, scoring | |
| import evaluate | |
| if version.parse(rouge_score_rs.__version__) < version.parse("0.1.1"): | |
| raise ImportError("This metric requires rouge-score-rs>=0.1.1: pip install -U 'rouge-score-rs[aggregate]>=0.1.1'") | |
| _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. | |
| With the default tokenizer, ROUGE is case insensitive, meaning that upper case letters are treated the same way as | |
| lower case letters. | |
| This metric uses rouge-score-rs (https://github.com/SyntaxSpirits/rougers), a Rust implementation of | |
| Google Research's reimplementation of ROUGE (https://github.com/google-research/google-research/tree/master/rouge). | |
| It gives the same scores as the `rouge` metric for string inputs and tokenizers that return lists of strings, | |
| and computes them faster. | |
| """ | |
| _KWARGS_DESCRIPTION = """ | |
| Calculates ROUGE F1 scores for a list of predictions and references. | |
| Args: | |
| predictions: list of predictions to score, as strings. | |
| references: list with one reference string per prediction, or a list of reference strings per prediction. | |
| With several references, the one with the highest F1 is used for each ROUGE type. | |
| rouge_types: A list of rouge types to calculate. | |
| Valid names: | |
| `"rouge{n}"` for n from 1 to 9 (e.g. `"rouge1"`, `"rouge2"`): 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 the default tokenizer should apply the Porter stemmer. | |
| use_aggregator: If True, return for each ROUGE type the median of 1,000 bootstrap means of the | |
| per-prediction scores (pass `seed` to `evaluate.load` for reproducible values); if False, return | |
| one score per prediction. | |
| tokenizer: Optional callable splitting a string into a list of string tokens. Lowercasing and | |
| stemming are only done by the default tokenizer. | |
| Returns: | |
| rouge1: rouge_1 (f1), | |
| rouge2: rouge_2 (f1), | |
| rougeL: rouge_l (f1), | |
| rougeLsum: rouge_lsum (f1) | |
| Examples: | |
| >>> rouge = evaluate.load('SyntaxSpirits/rouge') | |
| >>> predictions = ["hello there", "general kenobi"] | |
| >>> references = ["hello there", "general kenobi"] | |
| >>> results = rouge.compute(predictions=predictions, references=references) | |
| >>> print(results) | |
| {'rouge1': 1.0, 'rouge2': 1.0, 'rougeL': 1.0, 'rougeLsum': 1.0} | |
| """ | |
| class Tokenizer: | |
| """Helper class to wrap a callable into a class with a `tokenize` method as used by rouge-score.""" | |
| def __init__(self, tokenizer_func): | |
| self.tokenizer_func = tokenizer_func | |
| def tokenize(self, text): | |
| return self.tokenizer_func(text) | |
| class Rouge(evaluate.Metric): | |
| def _info(self): | |
| return evaluate.MetricInfo( | |
| description=_DESCRIPTION, | |
| citation=_CITATION, | |
| inputs_description=_KWARGS_DESCRIPTION, | |
| features=[ | |
| datasets.Features( | |
| { | |
| "predictions": datasets.Value("string", id="sequence"), | |
| "references": datasets.Sequence(datasets.Value("string", id="sequence")), | |
| } | |
| ), | |
| datasets.Features( | |
| { | |
| "predictions": datasets.Value("string", id="sequence"), | |
| "references": datasets.Value("string", id="sequence"), | |
| } | |
| ), | |
| ], | |
| codebase_urls=[ | |
| "https://huggingface.co/spaces/SyntaxSpirits/rouge", | |
| "https://github.com/SyntaxSpirits/rougers", | |
| "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_aggregator=True, use_stemmer=False, tokenizer=None | |
| ): | |
| if rouge_types is None: | |
| rouge_types = ["rouge1", "rouge2", "rougeL", "rougeLsum"] | |
| multi_ref = isinstance(references[0], list) | |
| if tokenizer is not None: | |
| tokenizer = Tokenizer(tokenizer) | |
| scorer = rouge_scorer.RougeScorer(rouge_types=rouge_types, use_stemmer=use_stemmer, tokenizer=tokenizer) | |
| if use_aggregator: | |
| aggregator = scoring.BootstrapAggregator() | |
| else: | |
| scores = [] | |
| if multi_ref: | |
| all_scores = (scorer.score_multi(ref, pred) for ref, pred in zip(references, predictions)) | |
| else: | |
| all_scores = scorer.score_batch(references, predictions) | |
| for score in all_scores: | |
| if use_aggregator: | |
| aggregator.add_scores(score) | |
| else: | |
| scores.append(score) | |
| if use_aggregator: | |
| result = aggregator.aggregate() | |
| for key in result: | |
| result[key] = result[key].mid.fmeasure | |
| else: | |
| result = {} | |
| for key in scores[0]: | |
| result[key] = list(score[key].fmeasure for score in scores) | |
| return result | |