CSS-LM

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# coding=utf-8
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# Copyright 2018 The Google AI Language Team Authors and The HuggingFace Inc. team.
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# Copyright (c) 2018, NVIDIA CORPORATION.  All rights reserved.
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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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try:
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    from scipy.stats import pearsonr, spearmanr
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    from sklearn.metrics import matthews_corrcoef, f1_score
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    _has_sklearn = True
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except (AttributeError, ImportError):
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    _has_sklearn = False
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def is_sklearn_available():
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    return _has_sklearn
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if _has_sklearn:
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    def simple_accuracy(preds, labels):
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        return (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 = f1_score(y_true=labels, y_pred=preds)
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        return {
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            "acc": acc,
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            "f1": f1,
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            "acc_and_f1": (acc + f1) / 2,
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        }
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    def pearson_and_spearman(preds, labels):
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        pearson_corr = pearsonr(preds, labels)[0]
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        spearman_corr = 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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            "corr": (pearson_corr + spearman_corr) / 2,
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        }
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    def glue_compute_metrics(task_name, preds, labels):
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        assert len(preds) == len(labels)
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        if task_name == "cola":
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            return {"mcc": matthews_corrcoef(labels, preds)}
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        elif task_name == "sst-2":
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            return {"acc": simple_accuracy(preds, labels)}
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        elif task_name == "mrpc":
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            return acc_and_f1(preds, labels)
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        elif task_name == "sts-b":
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            return pearson_and_spearman(preds, labels)
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        elif task_name == "qqp":
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            return acc_and_f1(preds, labels)
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        elif task_name == "mnli":
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            return {"mnli/acc": simple_accuracy(preds, labels)}
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        elif task_name == "mnli-mm":
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            return {"mnli-mm/acc": simple_accuracy(preds, labels)}
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        elif task_name == "qnli":
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            return {"acc": simple_accuracy(preds, labels)}
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        elif task_name == "rte":
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            return {"acc": simple_accuracy(preds, labels)}
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        elif task_name == "wnli":
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            return {"acc": simple_accuracy(preds, labels)}
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        elif task_name == "hans":
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            return {"acc": simple_accuracy(preds, labels)}
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        else:
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            raise KeyError(task_name)
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    def xnli_compute_metrics(task_name, preds, labels):
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        assert len(preds) == len(labels)
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        if task_name == "xnli":
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            return {"acc": simple_accuracy(preds, labels)}
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        else:
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            raise KeyError(task_name)
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