datasets

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# Copyright 2020 The HuggingFace Datasets Authors.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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#     http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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"""GLUE benchmark metric."""
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from scipy.stats import pearsonr, spearmanr
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from sklearn.metrics import f1_score, matthews_corrcoef
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import datasets
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_CITATION = """\
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@inproceedings{wang2019glue,
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  title={{GLUE}: A Multi-Task Benchmark and Analysis Platform for Natural Language Understanding},
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  author={Wang, Alex and Singh, Amanpreet and Michael, Julian and Hill, Felix and Levy, Omer and Bowman, Samuel R.},
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  note={In the Proceedings of ICLR.},
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  year={2019}
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}
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"""
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_DESCRIPTION = """\
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GLUE, the General Language Understanding Evaluation benchmark
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(https://gluebenchmark.com/) is a collection of resources for training,
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evaluating, and analyzing natural language understanding systems.
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"""
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_KWARGS_DESCRIPTION = """
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Compute GLUE evaluation metric associated to each GLUE dataset.
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Args:
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    predictions: list of predictions to score.
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        Each translation should be tokenized into a list of tokens.
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    references: list of lists of references for each translation.
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        Each reference should be tokenized into a list of tokens.
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Returns: depending on the GLUE subset, one or several of:
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    "accuracy": Accuracy
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    "f1": F1 score
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    "pearson": Pearson Correlation
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    "spearmanr": Spearman Correlation
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    "matthews_correlation": Matthew Correlation
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Examples:
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    >>> glue_metric = datasets.load_metric('glue', 'sst2')  # 'sst2' or any of ["mnli", "mnli_mismatched", "mnli_matched", "qnli", "rte", "wnli", "hans"]
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    >>> references = [0, 1]
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    >>> predictions = [0, 1]
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    >>> results = glue_metric.compute(predictions=predictions, references=references)
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    >>> print(results)
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    {'accuracy': 1.0}
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    >>> glue_metric = datasets.load_metric('glue', 'mrpc')  # 'mrpc' or 'qqp'
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    >>> references = [0, 1]
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    >>> predictions = [0, 1]
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    >>> results = glue_metric.compute(predictions=predictions, references=references)
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    >>> print(results)
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    {'accuracy': 1.0, 'f1': 1.0}
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    >>> glue_metric = datasets.load_metric('glue', 'stsb')
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    >>> references = [0., 1., 2., 3., 4., 5.]
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    >>> predictions = [0., 1., 2., 3., 4., 5.]
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    >>> results = glue_metric.compute(predictions=predictions, references=references)
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    >>> print({"pearson": round(results["pearson"], 2), "spearmanr": round(results["spearmanr"], 2)})
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    {'pearson': 1.0, 'spearmanr': 1.0}
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    >>> glue_metric = datasets.load_metric('glue', 'cola')
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    >>> references = [0, 1]
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    >>> predictions = [0, 1]
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    >>> results = glue_metric.compute(predictions=predictions, references=references)
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    >>> print(results)
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    {'matthews_correlation': 1.0}
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"""
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def simple_accuracy(preds, labels):
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    return float((preds == labels).mean())
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def acc_and_f1(preds, labels):
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    acc = simple_accuracy(preds, labels)
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    f1 = float(f1_score(y_true=labels, y_pred=preds))
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    return {
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        "accuracy": acc,
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        "f1": f1,
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    }
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def pearson_and_spearman(preds, labels):
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    pearson_corr = float(pearsonr(preds, labels)[0])
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    spearman_corr = float(spearmanr(preds, labels)[0])
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    return {
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        "pearson": pearson_corr,
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        "spearmanr": spearman_corr,
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    }
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@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION, _KWARGS_DESCRIPTION)
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class Glue(datasets.Metric):
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    def _info(self):
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        if self.config_name not in [
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            "sst2",
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            "mnli",
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            "mnli_mismatched",
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            "mnli_matched",
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            "cola",
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            "stsb",
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            "mrpc",
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            "qqp",
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            "qnli",
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            "rte",
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            "wnli",
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            "hans",
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        ]:
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            raise KeyError(
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                "You should supply a configuration name selected in "
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                '["sst2", "mnli", "mnli_mismatched", "mnli_matched", '
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                '"cola", "stsb", "mrpc", "qqp", "qnli", "rte", "wnli", "hans"]'
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            )
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        return datasets.MetricInfo(
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            description=_DESCRIPTION,
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            citation=_CITATION,
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            inputs_description=_KWARGS_DESCRIPTION,
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            features=datasets.Features(
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                {
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                    "predictions": datasets.Value("int64" if self.config_name != "stsb" else "float32"),
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                    "references": datasets.Value("int64" if self.config_name != "stsb" else "float32"),
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                }
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            ),
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            codebase_urls=[],
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            reference_urls=[],
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            format="numpy",
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        )
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    def _compute(self, predictions, references):
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        if self.config_name == "cola":
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            return {"matthews_correlation": matthews_corrcoef(references, predictions)}
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        elif self.config_name == "stsb":
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            return pearson_and_spearman(predictions, references)
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        elif self.config_name in ["mrpc", "qqp"]:
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            return acc_and_f1(predictions, references)
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        elif self.config_name in ["sst2", "mnli", "mnli_mismatched", "mnli_matched", "qnli", "rte", "wnli", "hans"]:
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            return {"accuracy": simple_accuracy(predictions, references)}
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        else:
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            raise KeyError(
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                "You should supply a configuration name selected in "
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                '["sst2", "mnli", "mnli_mismatched", "mnli_matched", '
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                '"cola", "stsb", "mrpc", "qqp", "qnli", "rte", "wnli", "hans"]'
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            )
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