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| # Copyright 2020 The HuggingFace Evaluate 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. | |
| """ XNLI benchmark metric. """ | |
| import datasets | |
| import evaluate | |
| _CITATION = """\ | |
| @InProceedings{conneau2018xnli, | |
| author = "Conneau, Alexis | |
| and Rinott, Ruty | |
| and Lample, Guillaume | |
| and Williams, Adina | |
| and Bowman, Samuel R. | |
| and Schwenk, Holger | |
| and Stoyanov, Veselin", | |
| title = "XNLI: Evaluating Cross-lingual Sentence Representations", | |
| booktitle = "Proceedings of the 2018 Conference on Empirical Methods | |
| in Natural Language Processing", | |
| year = "2018", | |
| publisher = "Association for Computational Linguistics", | |
| location = "Brussels, Belgium", | |
| } | |
| """ | |
| _DESCRIPTION = """\ | |
| XNLI is a subset of a few thousand examples from MNLI which has been translated | |
| into a 14 different languages (some low-ish resource). As with MNLI, the goal is | |
| to predict textual entailment (does sentence A imply/contradict/neither sentence | |
| B) and is a classification task (given two sentences, predict one of three | |
| labels). | |
| """ | |
| _KWARGS_DESCRIPTION = """ | |
| Computes XNLI score which is just simple accuracy. | |
| Args: | |
| predictions: Predicted labels. | |
| references: Ground truth labels. | |
| Returns: | |
| 'accuracy': accuracy | |
| Examples: | |
| >>> predictions = [0, 1] | |
| >>> references = [0, 1] | |
| >>> xnli_metric = evaluate.load("xnli") | |
| >>> results = xnli_metric.compute(predictions=predictions, references=references) | |
| >>> print(results) | |
| {'accuracy': 1.0} | |
| """ | |
| def simple_accuracy(preds, labels): | |
| return (preds == labels).mean() | |
| class Xnli(evaluate.Metric): | |
| def _info(self): | |
| return evaluate.MetricInfo( | |
| description=_DESCRIPTION, | |
| citation=_CITATION, | |
| inputs_description=_KWARGS_DESCRIPTION, | |
| features=datasets.Features( | |
| { | |
| "predictions": datasets.Value("int64" if self.config_name != "sts-b" else "float32"), | |
| "references": datasets.Value("int64" if self.config_name != "sts-b" else "float32"), | |
| } | |
| ), | |
| codebase_urls=[], | |
| reference_urls=[], | |
| format="numpy", | |
| ) | |
| def _compute(self, predictions, references): | |
| return {"accuracy": simple_accuracy(predictions, references)} | |