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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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msgstr ""
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#: ../source/paddlenlp.data.data_collator.rst:2
msgid "data\\_collator"
msgstr ""

#: of paddlenlp.data.data_collator.DataCollatorForSeq2Seq:1
#: paddlenlp.data.data_collator.DataCollatorForTokenClassification:1
#: paddlenlp.data.data_collator.DataCollatorWithPadding:1
#: paddlenlp.data.data_collator.DefaultDataCollator:1
msgid "基类：:class:`object`"
msgstr ""

#: of paddlenlp.data.data_collator.DataCollatorWithPadding:1
msgid ""
"Data collator that will dynamically pad the inputs to the longest "
"sequence in the batch."
msgstr ""

#: of paddlenlp.data.data_collator.DataCollatorForSeq2Seq
#: paddlenlp.data.data_collator.DataCollatorForTokenClassification
#: paddlenlp.data.data_collator.DataCollatorWithPadding
msgid "参数"
msgstr ""

#: of paddlenlp.data.data_collator.DataCollatorForSeq2Seq:3
#: paddlenlp.data.data_collator.DataCollatorForTokenClassification:3
#: paddlenlp.data.data_collator.DataCollatorWithPadding:3
msgid "The tokenizer used for encoding the data."
msgstr ""

#: of paddlenlp.data.data_collator.DefaultDataCollator:1
msgid ""
"Very simple data collator that simply collates batches of dict-like "
"objects and performs special handling for potential keys named:"
msgstr ""

#: of paddlenlp.data.data_collator.DefaultDataCollator:3
msgid "`label`: handles a single value (int or float) per object"
msgstr ""

#: of paddlenlp.data.data_collator.DefaultDataCollator:4
msgid "`label_ids`: handles a list of values per object"
msgstr ""

#: of paddlenlp.data.data_collator.DefaultDataCollator:5
msgid ""
"Does not do any additional preprocessing: property names of the input "
"object will be used as corresponding inputs to the model. See glue and "
"ner for example of how it's useful. This is an object (like other data "
"collators) rather than a pure function like default_data_collator. This "
"can be helpful if you need to set a return_tensors value at "
"initialization. :param return_tensors: Return Tensor or numpy array. "
":type return_tensors: `bool`"
msgstr ""

#: of paddlenlp.data.data_collator.DataCollatorForTokenClassification:1
msgid ""
"Data collator that will dynamically pad the inputs to longest sequence in"
" the batch, as well as the labels."
msgstr ""

#: of paddlenlp.data.data_collator.DataCollatorForTokenClassification:5
msgid "The id to use when padding the labels. Defaults to -100."
msgstr ""

#: of paddlenlp.data.data_collator.DataCollatorForSeq2Seq:1
msgid ""
"Data collator that will dynamically pad the inputs received, as well as "
"the labels."
msgstr ""

#: of paddlenlp.data.data_collator.DataCollatorForSeq2Seq:5
msgid ""
"The model that is being trained. If set and has the "
"*prepare_decoder_input_ids_from_labels*, use it to prepare the "
"*decoder_input_ids*  This is useful when using *label_smoothing* to avoid"
" calculating loss twice."
msgstr ""

#: of paddlenlp.data.data_collator.DataCollatorForSeq2Seq:5
msgid ""
"The model that is being trained. If set and has the "
"*prepare_decoder_input_ids_from_labels*, use it to prepare the "
"*decoder_input_ids*"
msgstr ""

#: of paddlenlp.data.data_collator.DataCollatorForSeq2Seq:8
msgid ""
"This is useful when using *label_smoothing* to avoid calculating loss "
"twice."
msgstr ""

#: of paddlenlp.data.data_collator.DataCollatorForSeq2Seq:10
msgid ""
"Select a strategy to pad the returned sequences (according to the model's"
" padding side and padding index) among:  - `True` or `'longest'`: Pad to "
"the longest sequence in the batch (or no padding if only a single "
"sequence   is provided). - `'max_length'`: Pad to a maximum length "
"specified with the argument `max_length` or to the maximum   acceptable "
"input length for the model if that argument is not provided. - `False` or"
" `'do_not_pad'` (default): No padding (i.e., can output a batch with "
"sequences of different   lengths)."
msgstr ""

#: of paddlenlp.data.data_collator.DataCollatorForSeq2Seq:10
msgid ""
"Select a strategy to pad the returned sequences (according to the model's"
" padding side and padding index) among:"
msgstr ""

#: of paddlenlp.data.data_collator.DataCollatorForSeq2Seq:13
msgid ""
"`True` or `'longest'`: Pad to the longest sequence in the batch (or no "
"padding if only a single sequence is provided)."
msgstr ""

#: of paddlenlp.data.data_collator.DataCollatorForSeq2Seq:15
msgid ""
"`'max_length'`: Pad to a maximum length specified with the argument "
"`max_length` or to the maximum acceptable input length for the model if "
"that argument is not provided."
msgstr ""

#: of paddlenlp.data.data_collator.DataCollatorForSeq2Seq:17
msgid ""
"`False` or `'do_not_pad'` (default): No padding (i.e., can output a batch"
" with sequences of different lengths)."
msgstr ""

#: of paddlenlp.data.data_collator.DataCollatorForSeq2Seq:20
msgid ""
"Maximum length of the returned list and optionally padding length (see "
"above)."
msgstr ""

#: of paddlenlp.data.data_collator.DataCollatorForSeq2Seq:22
msgid ""
"If set will pad the sequence to a multiple of the provided value.  This "
"is especially useful to enable the use of Tensor Cores on NVIDIA hardware"
" with compute capability >= 7.5 (Volta)."
msgstr ""

#: of paddlenlp.data.data_collator.DataCollatorForSeq2Seq:22
msgid "If set will pad the sequence to a multiple of the provided value."
msgstr ""

#: of paddlenlp.data.data_collator.DataCollatorForSeq2Seq:24
msgid ""
"This is especially useful to enable the use of Tensor Cores on NVIDIA "
"hardware with compute capability >= 7.5 (Volta)."
msgstr ""

#: of paddlenlp.data.data_collator.DataCollatorForSeq2Seq:27
msgid ""
"The id to use when padding the labels (-100 will be automatically ignored"
" by PyTorch loss functions)."
msgstr ""

#: of paddlenlp.data.data_collator.DataCollatorForSeq2Seq:29
msgid ""
"The type of Tensor to return. Allowable values are \"np\", \"pt\" and "
"\"tf\"."
msgstr ""

