paddlenlp.data.collate.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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#: ../source/paddlenlp.data.collate.rst:2
msgid "collate"
msgstr ""

#: of paddlenlp.data.collate.Dict:1 paddlenlp.data.collate.Pad:1
#: paddlenlp.data.collate.Stack:1 paddlenlp.data.collate.Tuple:1
msgid "基类：:class:`object`"
msgstr ""

#: of paddlenlp.data.collate.Stack:1
msgid ""
"Stacks the input data samples to construct the batch. The N input samples"
" must have the same shape/length and will be stacked to construct a "
"batch."
msgstr ""

#: of paddlenlp.data.collate.Dict paddlenlp.data.collate.Dict.__call__
#: paddlenlp.data.collate.Pad paddlenlp.data.collate.Pad.__call__
#: paddlenlp.data.collate.Stack paddlenlp.data.collate.Stack.__call__
#: paddlenlp.data.collate.Tuple paddlenlp.data.collate.Tuple.__call__
msgid "参数"
msgstr ""

#: of paddlenlp.data.collate.Stack:4
msgid ""
"The axis in the result data along which the input data are stacked. "
"Default: 0."
msgstr ""

#: of paddlenlp.data.collate.Stack:7
msgid ""
"The value type of the output. If it is set to None, the type of input "
"data is used. Default: None."
msgstr ""

#: of paddlenlp.data.collate.Stack.__call__:1
msgid "Batchifies the input data by stacking."
msgstr ""

#: of paddlenlp.data.collate.Pad.__call__:6
#: paddlenlp.data.collate.Stack.__call__:3
msgid ""
"The input data samples. It is a list. Each element is a numpy.ndarray or "
"list."
msgstr ""

#: of paddlenlp.data.collate.Dict.__call__ paddlenlp.data.collate.Pad.__call__
#: paddlenlp.data.collate.Stack.__call__ paddlenlp.data.collate.Tuple.__call__
msgid "返回"
msgstr ""

#: of paddlenlp.data.collate.Stack.__call__:7
msgid "Stacked batch data."
msgstr ""

#: of paddlenlp.data.collate.Dict.__call__ paddlenlp.data.collate.Pad.__call__
#: paddlenlp.data.collate.Stack.__call__ paddlenlp.data.collate.Tuple.__call__
msgid "返回类型"
msgstr ""

#: of paddlenlp.data.collate.Dict.__call__:14
#: paddlenlp.data.collate.Pad.__call__:18
#: paddlenlp.data.collate.Stack.__call__:11
#: paddlenlp.data.collate.Tuple.__call__:14
msgid "示例"
msgstr ""

#: of paddlenlp.data.collate.Pad:1
msgid "Pads the input data samples to the largest length at `axis`."
msgstr ""

#: of paddlenlp.data.collate.Pad:3
msgid "The padding value. Default: 0."
msgstr ""

#: of paddlenlp.data.collate.Pad:5
msgid ""
"The axis to pad the arrays. The arrays will be padded to the largest "
"length at `axis`. For example, assume the input arrays have shape (10, 8,"
" 5), (6, 8, 5), (3, 8, 5) and the axis is 0. Each input will be padded "
"into (10, 8, 5) and then stacked to form the final output, which has "
"shape (3, 10, 8, 5). Default: 0."
msgstr ""

#: of paddlenlp.data.collate.Pad:12
msgid ""
"If it is bool, indicate whether to return the valid length in the output,"
" and the data type of returned length is int32 if True. If it is "
"numpy.dtype, indicate the data type of returned length. Default: None."
msgstr ""

#: of paddlenlp.data.collate.Pad:17
msgid ""
"The value type of the output. If it is set to None, the input data type "
"is used. Default: None."
msgstr ""

#: of paddlenlp.data.collate.Pad:20
msgid ""
"Whether the padding direction is right-side. If True, it indicates we pad"
" to the right side, while False indicates we pad to the left side. "
"Default: True."
msgstr ""

#: of paddlenlp.data.collate.Pad.__call__:1
msgid ""
"Batchifies the input data by padding. The input will be padded to the "
"largest dimension at `axis` and then stacked to form the final output. In"
" addition, the function will output the original dimensions at the `axis`"
" if `ret_length` is not None or False."
msgstr ""

#: of paddlenlp.data.collate.Pad.__call__:10
msgid ""
"If `ret_length` is False, it is a numpy.ndarray representing the padded "
"batch data and the shape is (N, …). Otherwise, it is a tuple, besides the"
" padded batch data, the tuple also includes a numpy.ndarray representing "
"original length at `axis` of all input samples, which shaped `(N,)`."
msgstr ""

#: of paddlenlp.data.collate.Dict:1 paddlenlp.data.collate.Tuple:1
msgid ""
"Wraps multiple batchify functions together. The input functions will be "
"applied to the corresponding input fields."
msgstr ""

#: of paddlenlp.data.collate.Tuple:4
msgid ""
"Each sample should be a list or tuple containing multiple fields. The "
"i'th batchify function stored in Tuple will be applied on the i'th field."
msgstr ""

#: of paddlenlp.data.collate.Tuple:7
msgid ""
"For example, when data sample is (nd_data, label), you can wrap two "
"batchify functions using `Tuple(DataBatchify, LabelBatchify)` to batchify"
" nd_data and label correspondingly."
msgstr ""

#: of paddlenlp.data.collate.Tuple:11
msgid ""
"The batchify functions to wrap. It is a callable function or a list/tuple"
" of callable functions."
msgstr ""

#: of paddlenlp.data.collate.Tuple:14
msgid "The additional batchify functions to wrap."
msgstr ""

#: of paddlenlp.data.collate.Tuple.__call__:1
msgid ""
"Batchifies data samples by applying each function on the corresponding "
"data field, and each data field is produced by stacking the field data of"
" samples."
msgstr ""

#: of paddlenlp.data.collate.Tuple.__call__:5
msgid ""
"The samples to batchfy. Each sample in list/tuple should contain `N` "
"fields."
msgstr ""

#: of paddlenlp.data.collate.Dict.__call__:9
#: paddlenlp.data.collate.Tuple.__call__:9
msgid "A tuple composed of results from all including batchifying functions."
msgstr ""

#: of paddlenlp.data.collate.Dict:4
msgid ""
"Each sample should be a dict containing multiple fields. Each batchify "
"function with key stored in `Dict` will be applied on the field which has"
" the same key."
msgstr ""

#: of paddlenlp.data.collate.Dict:8
msgid ""
"For example, when data sample is {'tokens': tokens, 'labels': labels}, "
"you can wrap two batchify functions using `Dict({'tokens': DataBatchify, "
"'labels': LabelBatchify})` to batchify tokens and labels correspondingly."
msgstr ""

#: of paddlenlp.data.collate.Dict:13
msgid ""
"The batchify functions to wrap. It is a dict, which values is callable "
"functions."
msgstr ""

#: of paddlenlp.data.collate.Dict.__call__:1
msgid ""
"Batchifies data samples by applying each function on the corresponding "
"data field, and each data field is produced by stacking the field data "
"with the same key as batchify functions of all samples."
msgstr ""

#: of paddlenlp.data.collate.Dict.__call__:5
msgid ""
"The samples to batchfy. Each sample in list/tuple is a dict with `N` key-"
"values."
msgstr ""