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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):
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"]'
)
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