paddlenlp.metrics.glue.po

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# Copyright (C) 2021, PaddleNLP
# This file is distributed under the same license as the PaddleNLP package.
# FIRST AUTHOR <EMAIL@ADDRESS>, 2022.
#
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msgid ""
msgstr ""
"Project-Id-Version: PaddleNLP \n"
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"POT-Creation-Date: 2022-03-18 21:31+0800\n"
"PO-Revision-Date: YEAR-MO-DA HO:MI+ZONE\n"
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#: ../source/paddlenlp.metrics.glue.rst:2
msgid "glue"
msgstr ""

#: of paddlenlp.metrics.glue.AccuracyAndF1:1 paddlenlp.metrics.glue.Mcc:1
#: paddlenlp.metrics.glue.MultiLabelsMetric:1
#: paddlenlp.metrics.glue.PearsonAndSpearman:1
msgid "基类：:class:`paddle.metric.metrics.Metric`"
msgstr ""

#: of paddlenlp.metrics.glue.AccuracyAndF1:1
msgid ""
"This class encapsulates Accuracy, Precision, Recall and F1 metric logic, "
"and `accumulate` function returns accuracy, precision, recall and f1. The"
" overview of all metrics could be seen at the document of `paddle.metric "
"<https://www.paddlepaddle.org.cn/documentation/docs/zh/develop/api/paddle/metric/Overview_cn.html>`_"
" for details."
msgstr ""

#: of paddlenlp.metrics.glue.AccuracyAndF1
#: paddlenlp.metrics.glue.AccuracyAndF1.compute
#: paddlenlp.metrics.glue.AccuracyAndF1.update paddlenlp.metrics.glue.Mcc
#: paddlenlp.metrics.glue.Mcc.compute paddlenlp.metrics.glue.Mcc.update
#: paddlenlp.metrics.glue.MultiLabelsMetric
#: paddlenlp.metrics.glue.MultiLabelsMetric.accumulate
#: paddlenlp.metrics.glue.MultiLabelsMetric.compute
#: paddlenlp.metrics.glue.MultiLabelsMetric.update
#: paddlenlp.metrics.glue.PearsonAndSpearman
#: paddlenlp.metrics.glue.PearsonAndSpearman.update
msgid "参数"
msgstr ""

#: of paddlenlp.metrics.glue.AccuracyAndF1:7
msgid ""
"Number of top elements to look at for computing accuracy. Defaults to "
"(1,)."
msgstr ""

#: of paddlenlp.metrics.glue.AccuracyAndF1:10
msgid "The positive label for calculating precision and recall. Defaults to 1."
msgstr ""

#: of paddlenlp.metrics.glue.AccuracyAndF1:14
msgid "String name of the metric instance. Defaults to 'acc_and_f1'."
msgstr ""

#: of paddlenlp.metrics.glue.AccuracyAndF1:18 paddlenlp.metrics.glue.Mcc:7
#: paddlenlp.metrics.glue.MultiLabelsMetric:11
#: paddlenlp.metrics.glue.PearsonAndSpearman:9
msgid "示例"
msgstr ""

#: of paddlenlp.metrics.glue.AccuracyAndF1.compute:1
#: paddlenlp.metrics.glue.MultiLabelsMetric.compute:1
msgid ""
"Accepts network's output and the labels, and calculates the top-k "
"(maximum value in topk) indices for accuracy."
msgstr ""

#: of paddlenlp.metrics.glue.AccuracyAndF1.compute:4
msgid ""
"Predicted tensor, and its dtype is float32 or float64, and has a shape of"
" [batch_size, num_classes]."
msgstr ""

#: of paddlenlp.metrics.glue.AccuracyAndF1.compute:7
msgid ""
"The ground truth tensor, and its dtype is int64, and has a shape of "
"[batch_size, 1] or [batch_size, num_classes] in one hot representation."
msgstr ""

#: of paddlenlp.metrics.glue.AccuracyAndF1.accumulate
#: paddlenlp.metrics.glue.AccuracyAndF1.compute
#: paddlenlp.metrics.glue.AccuracyAndF1.name
#: paddlenlp.metrics.glue.Mcc.accumulate paddlenlp.metrics.glue.Mcc.compute
#: paddlenlp.metrics.glue.Mcc.name
#: paddlenlp.metrics.glue.MultiLabelsMetric.accumulate
#: paddlenlp.metrics.glue.MultiLabelsMetric.compute
#: paddlenlp.metrics.glue.MultiLabelsMetric.name
#: paddlenlp.metrics.glue.PearsonAndSpearman.accumulate
#: paddlenlp.metrics.glue.PearsonAndSpearman.name
msgid "返回"
msgstr ""

#: of paddlenlp.metrics.glue.AccuracyAndF1.compute:12
msgid ""
"Correct mask, each element indicates whether the prediction equals to the"
" label. Its' a tensor with a data type of float32 and has a shape of "
"[batch_size, topk]."
msgstr ""

#: of paddlenlp.metrics.glue.AccuracyAndF1.accumulate
#: paddlenlp.metrics.glue.AccuracyAndF1.compute
#: paddlenlp.metrics.glue.AccuracyAndF1.name
#: paddlenlp.metrics.glue.Mcc.accumulate paddlenlp.metrics.glue.Mcc.compute
#: paddlenlp.metrics.glue.Mcc.name
#: paddlenlp.metrics.glue.MultiLabelsMetric.accumulate
#: paddlenlp.metrics.glue.MultiLabelsMetric.compute
#: paddlenlp.metrics.glue.MultiLabelsMetric.name
#: paddlenlp.metrics.glue.PearsonAndSpearman.accumulate
#: paddlenlp.metrics.glue.PearsonAndSpearman.name
msgid "返回类型"
msgstr ""

#: of paddlenlp.metrics.glue.AccuracyAndF1.update:1
#: paddlenlp.metrics.glue.MultiLabelsMetric.update:1
msgid ""
"Updates the metrics states (accuracy, precision and recall), in order to "
"calculate accumulated accuracy, precision and recall of all instances."
msgstr ""

#: of paddlenlp.metrics.glue.AccuracyAndF1.update:4
msgid ""
"Correct mask for calculating accuracy, and it's a tensor with shape "
"[batch_size, topk] and has a dtype of float32."
msgstr ""

#: of paddlenlp.metrics.glue.AccuracyAndF1.accumulate:1
#: paddlenlp.metrics.glue.Mcc.accumulate:1
#: paddlenlp.metrics.glue.MultiLabelsMetric.accumulate:1
#: paddlenlp.metrics.glue.PearsonAndSpearman.accumulate:1
msgid "Calculates and returns the accumulated metric."
msgstr ""

#: of paddlenlp.metrics.glue.AccuracyAndF1.accumulate:3
msgid ""
"The accumulated metric. A tuple of shape (acc, precision, recall, f1, "
"average_of_acc_and_f1)  With the fields:  - `acc` (numpy.float64):     "
"The accumulated accuracy. - `precision` (numpy.float64):     The "
"accumulated precision. - `recall` (numpy.float64):     The accumulated "
"recall. - `f1` (numpy.float64):     The accumulated f1. - "
"`average_of_acc_and_f1` (numpy.float64):     The average of accumulated "
"accuracy and f1."
msgstr ""

#: of paddlenlp.metrics.glue.AccuracyAndF1.accumulate:3
msgid ""
"The accumulated metric. A tuple of shape (acc, precision, recall, f1, "
"average_of_acc_and_f1)"
msgstr ""

#: of paddlenlp.metrics.glue.AccuracyAndF1.accumulate:6
#: paddlenlp.metrics.glue.MultiLabelsMetric.accumulate:27
#: paddlenlp.metrics.glue.PearsonAndSpearman.accumulate:6
msgid "With the fields:"
msgstr ""

#: of paddlenlp.metrics.glue.AccuracyAndF1.accumulate:8
msgid "`acc` (numpy.float64):"
msgstr ""

#: of paddlenlp.metrics.glue.AccuracyAndF1.accumulate:9
msgid "The accumulated accuracy."
msgstr ""

#: of paddlenlp.metrics.glue.AccuracyAndF1.accumulate:10
msgid "`precision` (numpy.float64):"
msgstr ""

#: of paddlenlp.metrics.glue.AccuracyAndF1.accumulate:11
#: paddlenlp.metrics.glue.MultiLabelsMetric.accumulate:30
msgid "The accumulated precision."
msgstr ""

#: of paddlenlp.metrics.glue.AccuracyAndF1.accumulate:12
msgid "`recall` (numpy.float64):"
msgstr ""

#: of paddlenlp.metrics.glue.AccuracyAndF1.accumulate:13
#: paddlenlp.metrics.glue.MultiLabelsMetric.accumulate:32
msgid "The accumulated recall."
msgstr ""

#: of paddlenlp.metrics.glue.AccuracyAndF1.accumulate:14
msgid "`f1` (numpy.float64):"
msgstr ""

#: of paddlenlp.metrics.glue.AccuracyAndF1.accumulate:15
#: paddlenlp.metrics.glue.MultiLabelsMetric.accumulate:34
msgid "The accumulated f1."
msgstr ""

#: of paddlenlp.metrics.glue.AccuracyAndF1.accumulate:16
msgid "`average_of_acc_and_f1` (numpy.float64):"
msgstr ""

#: of paddlenlp.metrics.glue.AccuracyAndF1.accumulate:17
msgid "The average of accumulated accuracy and f1."
msgstr ""

#: of paddlenlp.metrics.glue.AccuracyAndF1.reset:1
#: paddlenlp.metrics.glue.Mcc.reset:1
#: paddlenlp.metrics.glue.PearsonAndSpearman.reset:1
msgid "Resets all metric states."
msgstr ""

#: of paddlenlp.metrics.glue.AccuracyAndF1.name:1
#: paddlenlp.metrics.glue.Mcc.name:1
#: paddlenlp.metrics.glue.MultiLabelsMetric.name:1
#: paddlenlp.metrics.glue.PearsonAndSpearman.name:1
msgid "Returns name of the metric instance."
msgstr ""

#: of paddlenlp.metrics.glue.AccuracyAndF1.name:3
#: paddlenlp.metrics.glue.Mcc.name:3
#: paddlenlp.metrics.glue.MultiLabelsMetric.name:3
#: paddlenlp.metrics.glue.PearsonAndSpearman.name:3
msgid "The name of the metric instance."
msgstr ""

#: of paddlenlp.metrics.glue.Mcc:1
msgid ""
"This class calculates `Matthews correlation coefficient "
"<https://en.wikipedia.org/wiki/Matthews_correlation_coefficient>`_ ."
msgstr ""

#: of paddlenlp.metrics.glue.Mcc:3
msgid "String name of the metric instance. Defaults to 'mcc'."
msgstr ""

#: of paddlenlp.metrics.glue.Mcc.compute:1
msgid ""
"Processes the pred tensor, and returns the indices of the maximum of each"
" sample."
msgstr ""

#: of paddlenlp.metrics.glue.Mcc.compute:4
msgid ""
"The predicted value is a Tensor with dtype float32 or float64. Shape is "
"[batch_size, 1]."
msgstr ""

#: of paddlenlp.metrics.glue.Mcc.compute:7
msgid ""
"The ground truth value is Tensor with dtype int64, and its shape is "
"[batch_size, 1]."
msgstr ""

#: of paddlenlp.metrics.glue.Mcc.compute:11
msgid ""
"A tuple of preds and label. Each shape is [batch_size, 1], with dtype "
"float32 or float64."
msgstr ""

#: of paddlenlp.metrics.glue.Mcc.update:1
msgid ""
"Calculates states, i.e. the number of true positive, false positive, true"
" negative and false negative samples."
msgstr ""

#: of paddlenlp.metrics.glue.Mcc.update:4
msgid ""
"Tuple of predicted value and the ground truth label, with dtype float32 "
"or float64. Each shape is [batch_size, 1]."
msgstr ""

#: of paddlenlp.metrics.glue.Mcc.accumulate:3
msgid ""
"Returns the accumulated metric, a tuple of shape (mcc,), `mcc` is the "
"accumulated mcc and its data type is float64."
msgstr ""

#: of paddlenlp.metrics.glue.PearsonAndSpearman:1
#, python-format
msgid ""
"The class calculates `Pearson correlation coefficient "
"<https://en.wikipedia.org/wiki/Pearson_correlation_coefficient>`_ and "
"`Spearman's rank correlation coefficient "
"<https://en.wikipedia.org/wiki/Spearman%27s_rank_correlation_coefficient>`_"
" ."
msgstr ""

#: of paddlenlp.metrics.glue.PearsonAndSpearman:5
msgid "String name of the metric instance. Defaults to 'pearson_and_spearman'."
msgstr ""

#: of paddlenlp.metrics.glue.PearsonAndSpearman.update:1
msgid ""
"Ensures the type of preds and labels is numpy.ndarray and reshapes them "
"into [-1, 1]."
msgstr ""

#: of paddlenlp.metrics.glue.PearsonAndSpearman.update:4
msgid ""
"Tuple or list of predicted value and the ground truth label. Its data "
"type should be float32 or float64 and its shape is [batch_size, d0, ..., "
"dN]."
msgstr ""

#: of paddlenlp.metrics.glue.PearsonAndSpearman.accumulate:3
msgid ""
"Returns the accumulated metric, a tuple of (pearson, spearman, "
"the_average_of_pearson_and_spearman).  With the fields:  - `pearson` "
"(numpy.float64):     The accumulated pearson.  - `spearman` "
"(numpy.float64):     The accumulated spearman.  - "
"`the_average_of_pearson_and_spearman` (numpy.float64):     The average of"
" accumulated pearson and spearman correlation     coefficient."
msgstr ""

#: of paddlenlp.metrics.glue.PearsonAndSpearman.accumulate:3
msgid ""
"Returns the accumulated metric, a tuple of (pearson, spearman, "
"the_average_of_pearson_and_spearman)."
msgstr ""

#: of paddlenlp.metrics.glue.PearsonAndSpearman.accumulate:9
msgid "`pearson` (numpy.float64):"
msgstr ""

#: of paddlenlp.metrics.glue.PearsonAndSpearman.accumulate:9
msgid "The accumulated pearson."
msgstr ""

#: of paddlenlp.metrics.glue.PearsonAndSpearman.accumulate:12
msgid "`spearman` (numpy.float64):"
msgstr ""

#: of paddlenlp.metrics.glue.PearsonAndSpearman.accumulate:12
msgid "The accumulated spearman."
msgstr ""

#: of paddlenlp.metrics.glue.PearsonAndSpearman.accumulate:15
msgid "`the_average_of_pearson_and_spearman` (numpy.float64):"
msgstr ""

#: of paddlenlp.metrics.glue.PearsonAndSpearman.accumulate:15
msgid "The average of accumulated pearson and spearman correlation coefficient."
msgstr ""

#: of paddlenlp.metrics.glue.MultiLabelsMetric:1
msgid ""
"This class encapsulates Accuracy, Precision, Recall and F1 metric logic "
"in multi-labels setting (also the binary setting). Some codes are taken "
"and modified from sklearn.metrics ."
msgstr ""

#: of paddlenlp.metrics.glue.MultiLabelsMetric:5
msgid "The total number of labels which is usually the number of classes"
msgstr ""

#: of paddlenlp.metrics.glue.MultiLabelsMetric:7
msgid "String name of the metric instance. Defaults to 'multi_labels_metric'."
msgstr ""

#: of paddlenlp.metrics.glue.MultiLabelsMetric:43
msgid ""
"Note: When zero_division is encountered (details as followed), the "
"corresponding metrics will be set to 0.0"
msgstr ""

#: of paddlenlp.metrics.glue.MultiLabelsMetric:40
msgid ""
"precision is zero_division if there are no positive predictions recall is"
" zero_division if there are no positive labels fscore is zero_division if"
" all labels AND predictions are negative"
msgstr ""

#: of paddlenlp.metrics.glue.MultiLabelsMetric.update:4
msgid "the tuple returned from `compute` function"
msgstr ""

#: of paddlenlp.metrics.glue.MultiLabelsMetric.accumulate:9
msgid "Only report results for the class specified by pos_label."
msgstr ""

#: of paddlenlp.metrics.glue.MultiLabelsMetric.accumulate:10
msgid ""
"Calculate metrics globally by counting the total true positives, false "
"negatives and false positives."
msgstr ""

#: of paddlenlp.metrics.glue.MultiLabelsMetric.accumulate:12
msgid ""
"Calculate metrics for each label, and find their unweighted mean. This "
"does not take label imbalance into account."
msgstr ""

#: of paddlenlp.metrics.glue.MultiLabelsMetric.accumulate:14
msgid ""
"Calculate metrics for each label, and find their average weighted by "
"support (the number of true instances for each label). This alters "
"`macro` to account for label imbalance; it can result in an F-score that "
"is not between precision and recall."
msgstr ""

#: of paddlenlp.metrics.glue.MultiLabelsMetric.accumulate:18
msgid ""
"The positive label for calculating precision and recall in binary "
"settings. Noted: Only when `average='binary'`, this arguments will be "
"used. Otherwise, it will be ignored. Defaults to 1."
msgstr ""

#: of paddlenlp.metrics.glue.MultiLabelsMetric.accumulate:24
msgid ""
"The accumulated metric. A tuple of shape (precision, recall, f1)     With"
" the fields:      - `precision` (numpy.float64 or numpy.ndarray if "
"average=None):         The accumulated precision.     - `recall` "
"(numpy.float64 or numpy.ndarray if average=None):         The accumulated"
" recall.     - `f1` (numpy.float64 or numpy.ndarray if average=None):"
"         The accumulated f1."
msgstr ""

#: of paddlenlp.metrics.glue.MultiLabelsMetric.accumulate:33
msgid "The accumulated metric. A tuple of shape (precision, recall, f1)"
msgstr ""

#: of paddlenlp.metrics.glue.MultiLabelsMetric.accumulate:29
msgid "`precision` (numpy.float64 or numpy.ndarray if average=None):"
msgstr ""

#: of paddlenlp.metrics.glue.MultiLabelsMetric.accumulate:31
msgid "`recall` (numpy.float64 or numpy.ndarray if average=None):"
msgstr ""

#: of paddlenlp.metrics.glue.MultiLabelsMetric.accumulate:33
msgid "`f1` (numpy.float64 or numpy.ndarray if average=None):"
msgstr ""

#: of paddlenlp.metrics.glue.MultiLabelsMetric.compute:4
msgid ""
"Predicted tensor, and its dtype is float32 or float64, and has a shape of"
" [batch_size, *, num_labels]."
msgstr ""

#: of paddlenlp.metrics.glue.MultiLabelsMetric.compute:7
msgid ""
"The ground truth tensor, and its dtype is int64, and has a shape of "
"[batch_size, *] or [batch_size, *, num_labels] in one hot representation."
msgstr ""

#: of paddlenlp.metrics.glue.MultiLabelsMetric.compute:12
msgid ""
"it contains two Tensor of shape [*, 1]. The tuple should be passed to "
"`update` function."
msgstr ""

#: of paddlenlp.metrics.glue.MultiLabelsMetric.reset:1
msgid "Reset states and result"
msgstr ""