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__init__.py
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Fri, May 17, 00:39

__init__.py
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# coding=utf-8
# Copyright 2018 The Google AI Language Team Authors and The HuggingFace Inc. team.
# Copyright (c) 2018, NVIDIA CORPORATION. All rights reserved.
#
# 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.
import warnings
from ...file_utils import is_sklearn_available, requires_sklearn
if is_sklearn_available():
from sklearn.metrics import f1_score, matthews_corrcoef
from scipy.stats import pearsonr, spearmanr
DEPRECATION_WARNING = (
"This metric will be removed from the library soon, metrics should be handled with the 🤗 Datasets "
"library. You can have a look at this example script for pointers: "
"https://github.com/huggingface/transformers/blob/master/examples/text-classification/run_glue.py"
)
def simple_accuracy(preds, labels):
warnings.warn(DEPRECATION_WARNING, FutureWarning)
requires_sklearn(simple_accuracy)
return (preds == labels).mean()
def acc_and_f1(preds, labels):
warnings.warn(DEPRECATION_WARNING, FutureWarning)
requires_sklearn(acc_and_f1)
acc = simple_accuracy(preds, labels)
f1 = f1_score(y_true=labels, y_pred=preds)
return {
"acc": acc,
"f1": f1,
"acc_and_f1": (acc + f1) / 2,
}
def pearson_and_spearman(preds, labels):
warnings.warn(DEPRECATION_WARNING, FutureWarning)
requires_sklearn(pearson_and_spearman)
pearson_corr = pearsonr(preds, labels)[0]
spearman_corr = spearmanr(preds, labels)[0]
return {
"pearson": pearson_corr,
"spearmanr": spearman_corr,
"corr": (pearson_corr + spearman_corr) / 2,
}
def glue_compute_metrics(task_name, preds, labels):
warnings.warn(DEPRECATION_WARNING, FutureWarning)
requires_sklearn(glue_compute_metrics)
assert len(preds) == len(labels), f"Predictions and labels have mismatched lengths {len(preds)} and {len(labels)}"
if task_name == "cola":
return {"mcc": matthews_corrcoef(labels, preds)}
elif task_name == "sst-2":
return {"acc": simple_accuracy(preds, labels)}
elif task_name == "mrpc":
return acc_and_f1(preds, labels)
elif task_name == "sts-b":
return pearson_and_spearman(preds, labels)
elif task_name == "qqp":
return acc_and_f1(preds, labels)
elif task_name == "mnli":
return {"mnli/acc": simple_accuracy(preds, labels)}
elif task_name == "mnli-mm":
return {"mnli-mm/acc": simple_accuracy(preds, labels)}
elif task_name == "qnli":
return {"acc": simple_accuracy(preds, labels)}
elif task_name == "rte":
return {"acc": simple_accuracy(preds, labels)}
elif task_name == "wnli":
return {"acc": simple_accuracy(preds, labels)}
elif task_name == "hans":
return {"acc": simple_accuracy(preds, labels)}
else:
raise KeyError(task_name)
def xnli_compute_metrics(task_name, preds, labels):
warnings.warn(DEPRECATION_WARNING, FutureWarning)
requires_sklearn(xnli_compute_metrics)
assert len(preds) == len(labels), f"Predictions and labels have mismatched lengths {len(preds)} and {len(labels)}"
if task_name == "xnli":
return {"acc": simple_accuracy(preds, labels)}
else:
raise KeyError(task_name)

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