Create glue.py

glue.py

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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.
+""" GLUE benchmark metric. """
+
+import datasets
+from scipy.stats import pearsonr, spearmanr
+from sklearn.metrics import f1_score, matthews_corrcoef
+
+import evaluate
+
+
+_CITATION = """\
+@inproceedings{wang2019glue,
+  title={{GLUE}: A Multi-Task Benchmark and Analysis Platform for Natural Language Understanding},
+  author={Wang, Alex and Singh, Amanpreet and Michael, Julian and Hill, Felix and Levy, Omer and Bowman, Samuel R.},
+  note={In the Proceedings of ICLR.},
+  year={2019}
+}
+"""
+
+_DESCRIPTION = """\
+GLUE, the General Language Understanding Evaluation benchmark
+(https://gluebenchmark.com/) is a collection of resources for training,
+evaluating, and analyzing natural language understanding systems.
+"""
+
+_KWARGS_DESCRIPTION = """
+Compute GLUE evaluation metric associated to each GLUE dataset.
+Args:
+    predictions: list of predictions to score.
+        Each translation should be tokenized into a list of tokens.
+    references: list of lists of references for each translation.
+        Each reference should be tokenized into a list of tokens.
+Returns: depending on the GLUE subset, one or several of:
+    "accuracy": Accuracy
+    "f1": F1 score
+    "pearson": Pearson Correlation
+    "spearmanr": Spearman Correlation
+    "matthews_correlation": Matthew Correlation
+Examples:
+    >>> glue_metric = evaluate.load('glue', 'sst2')  # 'sst2' or any of ["mnli", "mnli_mismatched", "mnli_matched", "qnli", "rte", "wnli", "hans"]
+    >>> references = [0, 1]
+    >>> predictions = [0, 1]
+    >>> results = glue_metric.compute(predictions=predictions, references=references)
+    >>> print(results)
+    {'accuracy': 1.0}
+    >>> glue_metric = evaluate.load('glue', 'mrpc')  # 'mrpc' or 'qqp'
+    >>> references = [0, 1]
+    >>> predictions = [0, 1]
+    >>> results = glue_metric.compute(predictions=predictions, references=references)
+    >>> print(results)
+    {'accuracy': 1.0, 'f1': 1.0}
+    >>> glue_metric = evaluate.load('glue', 'stsb')
+    >>> references = [0., 1., 2., 3., 4., 5.]
+    >>> predictions = [0., 1., 2., 3., 4., 5.]
+    >>> results = glue_metric.compute(predictions=predictions, references=references)
+    >>> print({"pearson": round(results["pearson"], 2), "spearmanr": round(results["spearmanr"], 2)})
+    {'pearson': 1.0, 'spearmanr': 1.0}
+    >>> glue_metric = evaluate.load('glue', 'cola')
+    >>> references = [0, 1]
+    >>> predictions = [0, 1]
+    >>> results = glue_metric.compute(predictions=predictions, references=references)
+    >>> print(results)
+    {'matthews_correlation': 1.0}
+"""
+
+
+def simple_accuracy(preds, labels):
+    return float((preds == labels).mean())
+
+
+def acc_and_f1(preds, labels):
+    acc = simple_accuracy(preds, labels)
+    f1 = float(f1_score(y_true=labels, y_pred=preds))
+    return {
+        "accuracy": acc,
+        "f1": f1,
+    }
+
+
+def pearson_and_spearman(preds, labels):
+    pearson_corr = float(pearsonr(preds, labels)[0])
+    spearman_corr = float(spearmanr(preds, labels)[0])
+    return {
+        "pearson": pearson_corr,
+        "spearmanr": spearman_corr,
+    }
+
+
+@evaluate.utils.file_utils.add_start_docstrings(_DESCRIPTION, _KWARGS_DESCRIPTION)
+class Glue(evaluate.Metric):
+    def _info(self):
+        if self.config_name not in [
+            "sst2",
+            "mnli",
+            "mnli_mismatched",
+            "mnli_matched",
+            "cola",
+            "stsb",
+            "mrpc",
+            "qqp",
+            "qnli",
+            "rte",
+            "wnli",
+            "hans",
+        ]:
+            raise KeyError(
+                "You should supply a configuration name selected in "
+                '["sst2", "mnli", "mnli_mismatched", "mnli_matched", '
+                '"cola", "stsb", "mrpc", "qqp", "qnli", "rte", "wnli", "hans"]'
+            )
+        return evaluate.MetricInfo(
+            description=_DESCRIPTION,
+            citation=_CITATION,
+            inputs_description=_KWARGS_DESCRIPTION,
+            features=datasets.Features(
+                {
+                    "predictions": datasets.Value("int64" if self.config_name != "stsb" else "float32"),
+                    "references": datasets.Value("int64" if self.config_name != "stsb" else "float32"),
+                }
+            ),
+            codebase_urls=[],
+            reference_urls=[],
+            format="numpy",
+        )
+
+    def _compute(self, predictions, references, config_name=None):
+        self.config_name = config_name
+        if self.config_name == "cola":
+            return {"matthews_correlation": matthews_corrcoef(references, predictions)}
+        elif self.config_name == "stsb":
+            return pearson_and_spearman(predictions, references)
+        elif self.config_name in ["mrpc", "qqp"]:
+            return acc_and_f1(predictions, references)
+        elif self.config_name in ["sst2", "mnli", "mnli_mismatched", "mnli_matched", "qnli", "rte", "wnli", "hans"]:
+            return {"accuracy": simple_accuracy(predictions, references)}
+        else:
+            raise KeyError(
+                "You should supply a configuration name selected in "
+                '["sst2", "mnli", "mnli_mismatched", "mnli_matched", '
+                '"cola", "stsb", "mrpc", "qqp", "qnli", "rte", "wnli", "hans"]'
+            )