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# FIRST AUTHOR <EMAIL@ADDRESS>, 2022.
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msgstr ""
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#: ../source/paddlenlp.transformers.electra.modeling.rst:2
msgid "modeling"
msgstr ""

#: of paddlenlp.transformers.electra.modeling.ElectraDiscriminator:1
#: paddlenlp.transformers.electra.modeling.ElectraForMultipleChoice:1
#: paddlenlp.transformers.electra.modeling.ElectraForQuestionAnswering:1
#: paddlenlp.transformers.electra.modeling.ElectraForSequenceClassification:1
#: paddlenlp.transformers.electra.modeling.ElectraForTokenClassification:1
#: paddlenlp.transformers.electra.modeling.ElectraForTotalPretraining:1
#: paddlenlp.transformers.electra.modeling.ElectraGenerator:1
#: paddlenlp.transformers.electra.modeling.ElectraModel:1
#: paddlenlp.transformers.electra.modeling.ErnieHealthDiscriminator:1
msgid "基类：:class:`paddlenlp.transformers.electra.modeling.ElectraPretrainedModel`"
msgstr ""

#: of paddlenlp.transformers.electra.modeling.ElectraModel:1
msgid "The bare Electra Model transformer outputting raw hidden-states."
msgstr ""

#: of paddlenlp.transformers.electra.modeling.ElectraModel:3
msgid ""
"This model inherits from "
":class:`~paddlenlp.transformers.model_utils.PretrainedModel`. Refer to "
"the superclass documentation for the generic methods."
msgstr ""

#: of paddlenlp.transformers.electra.modeling.ElectraModel:6
msgid ""
"This model is also a Paddle `paddle.nn.Layer "
"<https://www.paddlepaddle.org.cn/documentation "
"/docs/en/api/paddle/fluid/dygraph/layers/Layer_en.html>`__ subclass. Use "
"it as a regular Paddle Layer and refer to the Paddle documentation for "
"all matter related to general usage and behavior."
msgstr ""

#: of paddlenlp.transformers.electra.modeling.ElectraClassificationHead
#: paddlenlp.transformers.electra.modeling.ElectraClassificationHead.forward
#: paddlenlp.transformers.electra.modeling.ElectraDiscriminator
#: paddlenlp.transformers.electra.modeling.ElectraDiscriminator.forward
#: paddlenlp.transformers.electra.modeling.ElectraForMultipleChoice
#: paddlenlp.transformers.electra.modeling.ElectraForMultipleChoice.forward
#: paddlenlp.transformers.electra.modeling.ElectraForQuestionAnswering
#: paddlenlp.transformers.electra.modeling.ElectraForQuestionAnswering.forward
#: paddlenlp.transformers.electra.modeling.ElectraForSequenceClassification
#: paddlenlp.transformers.electra.modeling.ElectraForSequenceClassification.forward
#: paddlenlp.transformers.electra.modeling.ElectraForTokenClassification
#: paddlenlp.transformers.electra.modeling.ElectraForTokenClassification.forward
#: paddlenlp.transformers.electra.modeling.ElectraForTotalPretraining
#: paddlenlp.transformers.electra.modeling.ElectraForTotalPretraining.forward
#: paddlenlp.transformers.electra.modeling.ElectraGenerator
#: paddlenlp.transformers.electra.modeling.ElectraGenerator.forward
#: paddlenlp.transformers.electra.modeling.ElectraModel
#: paddlenlp.transformers.electra.modeling.ElectraModel.forward
#: paddlenlp.transformers.electra.modeling.ElectraPretrainingCriterion
#: paddlenlp.transformers.electra.modeling.ElectraPretrainingCriterion.forward
#: paddlenlp.transformers.electra.modeling.ErnieHealthDiscriminator
#: paddlenlp.transformers.electra.modeling.ErnieHealthDiscriminator.forward
#: paddlenlp.transformers.electra.modeling.ErnieHealthForTotalPretraining
#: paddlenlp.transformers.electra.modeling.ErnieHealthForTotalPretraining.forward
#: paddlenlp.transformers.electra.modeling.ErnieHealthPretrainingCriterion
#: paddlenlp.transformers.electra.modeling.ErnieHealthPretrainingCriterion.forward
msgid "参数"
msgstr ""

#: of paddlenlp.transformers.electra.modeling.ElectraModel:10
msgid ""
"Vocabulary size of `inputs_ids` in `ElectraModel`. Also is the vocab size"
" of token embedding matrix. Defines the number of different tokens that "
"can be represented by the `inputs_ids` passed when calling "
"`ElectraModel`."
msgstr ""

#: of paddlenlp.transformers.electra.modeling.ElectraClassificationHead:3
#: paddlenlp.transformers.electra.modeling.ElectraModel:13
msgid "Dimensionality of the embedding layer."
msgstr ""

#: of paddlenlp.transformers.electra.modeling.ElectraModel:15
msgid "Dimensionality of the encoder layer and pooler layer."
msgstr ""

#: of paddlenlp.transformers.electra.modeling.ElectraModel:17
msgid "Number of hidden layers in the Transformer encoder."
msgstr ""

#: of paddlenlp.transformers.electra.modeling.ElectraModel:19
msgid ""
"Number of attention heads for each attention layer in the Transformer "
"encoder."
msgstr ""

#: of paddlenlp.transformers.electra.modeling.ElectraModel:21
msgid ""
"Dimensionality of the feed-forward (ff) layer in the encoder. Input "
"tensors to ff layers are firstly projected from `hidden_size` to "
"`intermediate_size`, and then projected back to `hidden_size`. Typically "
"`intermediate_size` is larger than `hidden_size`."
msgstr ""

#: of paddlenlp.transformers.electra.modeling.ElectraModel:25
msgid ""
"The non-linear activation function in the feed-forward layer. "
"``\"gelu\"``, ``\"relu\"`` and any other paddle supported activation "
"functions are supported."
msgstr ""

#: of paddlenlp.transformers.electra.modeling.ElectraModel:29
msgid ""
"The dropout probability for all fully connected layers in the embeddings "
"and encoder."
msgstr ""

#: of paddlenlp.transformers.electra.modeling.ElectraModel:31
msgid ""
"The dropout probability used in MultiHeadAttention in all encoder layers "
"to drop some attention target."
msgstr ""

#: of paddlenlp.transformers.electra.modeling.ElectraModel:33
msgid ""
"The maximum value of the dimensionality of position encoding, which "
"dictates the maximum supported length of an input sequence."
msgstr ""

#: of paddlenlp.transformers.electra.modeling.ElectraModel:36
msgid "The vocabulary size of `token_type_ids`."
msgstr ""

#: of paddlenlp.transformers.electra.modeling.ElectraModel:38
msgid ""
"The standard deviation of the normal initializer.  .. note::     A "
"normal_initializer initializes weight matrices as normal distributions."
"     See :meth:`ElectraPretrainedModel.init_weights()` for how weights "
"are initialized in `ElectraModel`."
msgstr ""

#: of paddlenlp.transformers.electra.modeling.ElectraModel:38
msgid "The standard deviation of the normal initializer."
msgstr ""

#: of paddlenlp.transformers.electra.modeling.ElectraModel:41
msgid ""
"A normal_initializer initializes weight matrices as normal distributions."
" See :meth:`ElectraPretrainedModel.init_weights()` for how weights are "
"initialized in `ElectraModel`."
msgstr ""

#: of paddlenlp.transformers.electra.modeling.ElectraModel:44
msgid "The index of padding token in the token vocabulary."
msgstr ""

#: of paddlenlp.transformers.electra.modeling.ElectraModel.forward:1
msgid ""
"The ElectraModel forward method, overrides the `__call__()` special "
"method."
msgstr ""

#: of paddlenlp.transformers.electra.modeling.ElectraModel.forward:3
msgid ""
"Indices of input sequence tokens in the vocabulary. They are numerical "
"representations of tokens that build the input sequence. Its data type "
"should be `int64` and it has a shape of [batch_size, sequence_length]."
msgstr ""

#: of paddlenlp.transformers.electra.modeling.ElectraModel.forward:7
msgid ""
"Segment token indices to indicate different portions of the inputs. "
"Selected in the range ``[0, type_vocab_size - 1]``. If `type_vocab_size` "
"is 2, which means the inputs have two portions. Indices can either be 0 "
"or 1:  - 0 corresponds to a *sentence A* token, - 1 corresponds to a "
"*sentence B* token.  Its data type should be `int64` and it has a shape "
"of [batch_size, sequence_length]. Defaults to `None`, which means we "
"don't add segment embeddings."
msgstr ""

#: of paddlenlp.transformers.electra.modeling.ElectraModel.forward:7
msgid ""
"Segment token indices to indicate different portions of the inputs. "
"Selected in the range ``[0, type_vocab_size - 1]``. If `type_vocab_size` "
"is 2, which means the inputs have two portions. Indices can either be 0 "
"or 1:"
msgstr ""

#: of paddlenlp.transformers.electra.modeling.ElectraModel.forward:12
msgid "0 corresponds to a *sentence A* token,"
msgstr ""

#: of paddlenlp.transformers.electra.modeling.ElectraModel.forward:13
msgid "1 corresponds to a *sentence B* token."
msgstr ""

#: of paddlenlp.transformers.electra.modeling.ElectraModel.forward:15
msgid ""
"Its data type should be `int64` and it has a shape of [batch_size, "
"sequence_length]. Defaults to `None`, which means we don't add segment "
"embeddings."
msgstr ""

#: of paddlenlp.transformers.electra.modeling.ElectraModel.forward:18
msgid ""
"Indices of positions of each input sequence tokens in the position "
"embeddings. Selected in the range ``[0, max_position_embeddings - 1]``. "
"Shape as `(batch_size, num_tokens)` and dtype as int64. Defaults to "
"`None`."
msgstr ""

#: of paddlenlp.transformers.electra.modeling.ElectraModel.forward:22
msgid ""
"Mask used in multi-head attention to avoid performing attention on to "
"some unwanted positions, usually the paddings or the subsequent "
"positions. Its data type can be int, float and bool. When the data type "
"is bool, the `masked` tokens have `False` values and the others have "
"`True` values. When the data type is int, the `masked` tokens have `0` "
"values and the others have `1` values. When the data type is float, the "
"`masked` tokens have `-INF` values and the others have `0` values. It is "
"a tensor with shape broadcasted to `[batch_size, num_attention_heads, "
"sequence_length, sequence_length]`. Defaults to `None`, which means "
"nothing needed to be prevented attention to."
msgstr ""

#: of paddlenlp.transformers.electra.modeling.ElectraClassificationHead.forward
#: paddlenlp.transformers.electra.modeling.ElectraDiscriminator.forward
#: paddlenlp.transformers.electra.modeling.ElectraForMultipleChoice.forward
#: paddlenlp.transformers.electra.modeling.ElectraForQuestionAnswering.forward
#: paddlenlp.transformers.electra.modeling.ElectraForSequenceClassification.forward
#: paddlenlp.transformers.electra.modeling.ElectraForTokenClassification.forward
#: paddlenlp.transformers.electra.modeling.ElectraForTotalPretraining.forward
#: paddlenlp.transformers.electra.modeling.ElectraGenerator.forward
#: paddlenlp.transformers.electra.modeling.ElectraModel.forward
#: paddlenlp.transformers.electra.modeling.ElectraPretrainingCriterion.forward
#: paddlenlp.transformers.electra.modeling.ErnieHealthDiscriminator.forward
#: paddlenlp.transformers.electra.modeling.ErnieHealthForTotalPretraining.forward
#: paddlenlp.transformers.electra.modeling.ErnieHealthForTotalPretraining.sample_negatives_from_softmax
#: paddlenlp.transformers.electra.modeling.ErnieHealthPretrainingCriterion.forward
msgid "返回"
msgstr ""

#: of paddlenlp.transformers.electra.modeling.ElectraModel.forward:32
msgid ""
"Returns tensor `encoder_outputs`, which is the output at the last layer "
"of the model. Its data type should be float32 and has a shape of "
"[batch_size, sequence_length, hidden_size]."
msgstr ""

#: of paddlenlp.transformers.electra.modeling.ElectraClassificationHead.forward
#: paddlenlp.transformers.electra.modeling.ElectraDiscriminator.forward
#: paddlenlp.transformers.electra.modeling.ElectraForMultipleChoice.forward
#: paddlenlp.transformers.electra.modeling.ElectraForQuestionAnswering.forward
#: paddlenlp.transformers.electra.modeling.ElectraForSequenceClassification.forward
#: paddlenlp.transformers.electra.modeling.ElectraForTokenClassification.forward
#: paddlenlp.transformers.electra.modeling.ElectraForTotalPretraining.forward
#: paddlenlp.transformers.electra.modeling.ElectraGenerator.forward
#: paddlenlp.transformers.electra.modeling.ElectraModel.forward
#: paddlenlp.transformers.electra.modeling.ElectraPretrainingCriterion.forward
#: paddlenlp.transformers.electra.modeling.ErnieHealthDiscriminator.forward
#: paddlenlp.transformers.electra.modeling.ErnieHealthForTotalPretraining.forward
#: paddlenlp.transformers.electra.modeling.ErnieHealthForTotalPretraining.sample_negatives_from_softmax
#: paddlenlp.transformers.electra.modeling.ErnieHealthPretrainingCriterion.forward
msgid "返回类型"
msgstr ""

#: of paddlenlp.transformers.electra.modeling.ElectraDiscriminator.forward:16
#: paddlenlp.transformers.electra.modeling.ElectraForMultipleChoice.forward:17
#: paddlenlp.transformers.electra.modeling.ElectraForQuestionAnswering.forward:26
#: paddlenlp.transformers.electra.modeling.ElectraForSequenceClassification.forward:17
#: paddlenlp.transformers.electra.modeling.ElectraForTokenClassification.forward:17
#: paddlenlp.transformers.electra.modeling.ElectraGenerator.forward:15
#: paddlenlp.transformers.electra.modeling.ElectraModel.forward:37
msgid "示例"
msgstr ""

#: of paddlenlp.transformers.electra.modeling.ElectraPretrainedModel:1
msgid "基类：:class:`paddlenlp.transformers.model_utils.PretrainedModel`"
msgstr ""

#: of paddlenlp.transformers.electra.modeling.ElectraPretrainedModel:1
msgid ""
"An abstract class for pretrained Electra models. It provides Electra "
"related `model_config_file`, `pretrained_init_configuration`, "
"`resource_files_names`, `pretrained_resource_files_map`, "
"`base_model_prefix` for downloading and loading pretrained models. See "
":class:`~paddlenlp.transformers.model_utils.PretrainedModel` for more "
"details."
msgstr ""

#: of
#: paddlenlp.transformers.electra.modeling.ElectraPretrainedModel.init_weights:1
msgid "Initializes and tie weights if needed."
msgstr ""

#: of
#: paddlenlp.transformers.electra.modeling.ElectraPretrainedModel.tie_weights:1
msgid "Tie the weights between the input embeddings and the output embeddings."
msgstr ""

#: of paddlenlp.transformers.electra.modeling.ElectraForTotalPretraining:1
msgid "Electra Model for pretraining tasks."
msgstr ""

#: of paddlenlp.transformers.electra.modeling.ElectraForTotalPretraining:3
#: paddlenlp.transformers.electra.modeling.ErnieHealthForTotalPretraining:3
msgid "An instance of :class:`ElectraGenerator`."
msgstr ""

#: of paddlenlp.transformers.electra.modeling.ElectraForTotalPretraining:5
msgid "An instance of :class:`ElectraDiscriminator`."
msgstr ""

#: of
#: paddlenlp.transformers.electra.modeling.ElectraForTotalPretraining.forward:1
#: paddlenlp.transformers.electra.modeling.ErnieHealthForTotalPretraining.forward:1
msgid ""
"The ElectraForPretraining forward method, overrides the __call__() "
"special method."
msgstr ""

#: of paddlenlp.transformers.electra.modeling.ElectraDiscriminator.forward:1
#: paddlenlp.transformers.electra.modeling.ElectraDiscriminator.forward:3
#: paddlenlp.transformers.electra.modeling.ElectraDiscriminator.forward:5
#: paddlenlp.transformers.electra.modeling.ElectraDiscriminator.forward:7
#: paddlenlp.transformers.electra.modeling.ElectraForQuestionAnswering.forward:3
#: paddlenlp.transformers.electra.modeling.ElectraForQuestionAnswering.forward:5
#: paddlenlp.transformers.electra.modeling.ElectraForQuestionAnswering.forward:7
#: paddlenlp.transformers.electra.modeling.ElectraForQuestionAnswering.forward:9
#: paddlenlp.transformers.electra.modeling.ElectraForSequenceClassification.forward:3
#: paddlenlp.transformers.electra.modeling.ElectraForSequenceClassification.forward:5
#: paddlenlp.transformers.electra.modeling.ElectraForSequenceClassification.forward:7
#: paddlenlp.transformers.electra.modeling.ElectraForSequenceClassification.forward:9
#: paddlenlp.transformers.electra.modeling.ElectraForTokenClassification.forward:3
#: paddlenlp.transformers.electra.modeling.ElectraForTokenClassification.forward:5
#: paddlenlp.transformers.electra.modeling.ElectraForTokenClassification.forward:7
#: paddlenlp.transformers.electra.modeling.ElectraForTokenClassification.forward:9
#: paddlenlp.transformers.electra.modeling.ElectraForTotalPretraining.forward:3
#: paddlenlp.transformers.electra.modeling.ElectraForTotalPretraining.forward:5
#: paddlenlp.transformers.electra.modeling.ElectraForTotalPretraining.forward:7
#: paddlenlp.transformers.electra.modeling.ElectraForTotalPretraining.forward:9
#: paddlenlp.transformers.electra.modeling.ElectraGenerator.forward:1
#: paddlenlp.transformers.electra.modeling.ElectraGenerator.forward:3
#: paddlenlp.transformers.electra.modeling.ElectraGenerator.forward:5
#: paddlenlp.transformers.electra.modeling.ElectraGenerator.forward:7
#: paddlenlp.transformers.electra.modeling.ElectraPretrainingCriterion.forward:14
#: paddlenlp.transformers.electra.modeling.ErnieHealthDiscriminator.forward:1
#: paddlenlp.transformers.electra.modeling.ErnieHealthDiscriminator.forward:6
#: paddlenlp.transformers.electra.modeling.ErnieHealthDiscriminator.forward:8
#: paddlenlp.transformers.electra.modeling.ErnieHealthDiscriminator.forward:10
#: paddlenlp.transformers.electra.modeling.ErnieHealthForTotalPretraining.forward:3
#: paddlenlp.transformers.electra.modeling.ErnieHealthForTotalPretraining.forward:5
#: paddlenlp.transformers.electra.modeling.ErnieHealthForTotalPretraining.forward:7
#: paddlenlp.transformers.electra.modeling.ErnieHealthForTotalPretraining.forward:9
#: paddlenlp.transformers.electra.modeling.ErnieHealthPretrainingCriterion.forward:14
msgid "See :class:`ElectraModel`."
msgstr ""

#: of
#: paddlenlp.transformers.electra.modeling.ElectraForTotalPretraining.forward:11
#: paddlenlp.transformers.electra.modeling.ErnieHealthForTotalPretraining.forward:11
msgid ""
"Raw inputs used to get discriminator labels. Its data type should be "
"`int64` and it has a shape of [batch_size, sequence_length]."
msgstr ""

#: of
#: paddlenlp.transformers.electra.modeling.ElectraForTotalPretraining.forward:14
#: paddlenlp.transformers.electra.modeling.ErnieHealthForTotalPretraining.forward:14
msgid ""
"Labels to compute the discriminator inputs. Its data type should be int64"
" and its shape is [batch_size, sequence_length]. The value for unmasked "
"tokens should be -100 and value for masked tokens should be 0."
msgstr ""

#: of
#: paddlenlp.transformers.electra.modeling.ElectraForTotalPretraining.forward:19
#: paddlenlp.transformers.electra.modeling.ErnieHealthForTotalPretraining.forward:19
msgid ""
"Returns tuple (generator_logits, disc_logits, disc_labels, "
"attention_mask).  With the fields:  - `generator_logits` (Tensor):     "
"The scores of Electra Generator.     Its data type should be int64 and "
"its shape is [batch_size, sequence_length, vocab_size].  - `disc_logits` "
"(Tensor):     The prediction result of replaced tokens.     Its data "
"type should be float32 and if batch_size>1, its shape is [batch_size, "
"sequence_length],     if batch_size=1, its shape is [sequence_length].  -"
" `disc_labels` (Tensor):     The labels of electra discriminator. Its "
"data type should be int32,     and its shape is [batch_size, "
"sequence_length].  - `attention_mask` (Tensor):     See "
":class:`ElectraModel`. Its data type should be bool."
msgstr ""

#: of
#: paddlenlp.transformers.electra.modeling.ElectraForTotalPretraining.forward:19
#: paddlenlp.transformers.electra.modeling.ErnieHealthForTotalPretraining.forward:19
msgid ""
"Returns tuple (generator_logits, disc_logits, disc_labels, "
"attention_mask)."
msgstr ""

#: of
#: paddlenlp.transformers.electra.modeling.ElectraForQuestionAnswering.forward:14
#: paddlenlp.transformers.electra.modeling.ElectraForTotalPretraining.forward:21
#: paddlenlp.transformers.electra.modeling.ErnieHealthForTotalPretraining.forward:21
msgid "With the fields:"
msgstr ""

#: of
#: paddlenlp.transformers.electra.modeling.ElectraForTotalPretraining.forward:25
#: paddlenlp.transformers.electra.modeling.ErnieHealthForTotalPretraining.forward:25
msgid "`generator_logits` (Tensor):"
msgstr ""

#: of
#: paddlenlp.transformers.electra.modeling.ElectraForTotalPretraining.forward:24
#: paddlenlp.transformers.electra.modeling.ErnieHealthForTotalPretraining.forward:24
msgid ""
"The scores of Electra Generator. Its data type should be int64 and its "
"shape is [batch_size, sequence_length, vocab_size]."
msgstr ""

#: of
#: paddlenlp.transformers.electra.modeling.ElectraForTotalPretraining.forward:30
#: paddlenlp.transformers.electra.modeling.ErnieHealthForTotalPretraining.forward:30
msgid "`disc_logits` (Tensor):"
msgstr ""

#: of
#: paddlenlp.transformers.electra.modeling.ElectraForTotalPretraining.forward:28
#: paddlenlp.transformers.electra.modeling.ErnieHealthForTotalPretraining.forward:28
msgid ""
"The prediction result of replaced tokens. Its data type should be "
"float32 and if batch_size>1, its shape is [batch_size, sequence_length], "
"if batch_size=1, its shape is [sequence_length]."
msgstr ""

#: of
#: paddlenlp.transformers.electra.modeling.ElectraForTotalPretraining.forward:34
#: paddlenlp.transformers.electra.modeling.ErnieHealthForTotalPretraining.forward:34
msgid "`disc_labels` (Tensor):"
msgstr ""

#: of
#: paddlenlp.transformers.electra.modeling.ElectraForTotalPretraining.forward:33
#: paddlenlp.transformers.electra.modeling.ErnieHealthForTotalPretraining.forward:33
msgid ""
"The labels of electra discriminator. Its data type should be int32, and "
"its shape is [batch_size, sequence_length]."
msgstr ""

#: of
#: paddlenlp.transformers.electra.modeling.ElectraForTotalPretraining.forward:36
#: paddlenlp.transformers.electra.modeling.ErnieHealthForTotalPretraining.forward:36
msgid "`attention_mask` (Tensor):"
msgstr ""

#: of
#: paddlenlp.transformers.electra.modeling.ElectraForTotalPretraining.forward:37
#: paddlenlp.transformers.electra.modeling.ErnieHealthForTotalPretraining.forward:37
msgid "See :class:`ElectraModel`. Its data type should be bool."
msgstr ""

#: of paddlenlp.transformers.electra.modeling.ElectraDiscriminator:1
msgid ""
"The Electra Discriminator can detect the tokens that are replaced by the "
"Electra Generator."
msgstr ""

#: of paddlenlp.transformers.electra.modeling.ElectraDiscriminator:3
#: paddlenlp.transformers.electra.modeling.ElectraGenerator:4
#: paddlenlp.transformers.electra.modeling.ErnieHealthDiscriminator:6
msgid "An instance of :class:`ElectraModel`."
msgstr ""

#: of paddlenlp.transformers.electra.modeling.ElectraDiscriminator.forward:10
msgid ""
"Returns tensor `logits`, the prediction result of replaced tokens. Its "
"data type should be float32 and if batch_size>1, its shape is "
"[batch_size, sequence_length], if batch_size=1, its shape is "
"[sequence_length]."
msgstr ""

#: of paddlenlp.transformers.electra.modeling.ElectraGenerator:1
msgid ""
"The Electra Generator will replace some tokens of the given sequence, it "
"is trained as a masked language model."
msgstr ""

#: of paddlenlp.transformers.electra.modeling.ElectraGenerator.forward:10
msgid ""
"Returns tensor `prediction_scores`, the scores of Electra Generator. Its "
"data type should be int64 and its shape is [batch_size, sequence_length, "
"vocab_size]."
msgstr ""

#: of paddlenlp.transformers.electra.modeling.ElectraClassificationHead:1
#: paddlenlp.transformers.electra.modeling.ElectraPretrainingCriterion:1
#: paddlenlp.transformers.electra.modeling.ErnieHealthPretrainingCriterion:1
msgid "基类：:class:`paddle.fluid.dygraph.layers.Layer`"
msgstr ""

#: of paddlenlp.transformers.electra.modeling.ElectraClassificationHead:1
msgid "Perform sentence-level classification tasks."
msgstr ""

#: of paddlenlp.transformers.electra.modeling.ElectraClassificationHead:5
msgid "The dropout probability for all fully connected layers."
msgstr ""

#: of paddlenlp.transformers.electra.modeling.ElectraClassificationHead:7
msgid "The number of classes."
msgstr ""

#: of paddlenlp.transformers.electra.modeling.ElectraClassificationHead:9
msgid "The activation function name between layers."
msgstr ""

#: of
#: paddlenlp.transformers.electra.modeling.ElectraClassificationHead.forward:1
msgid ""
"The ElectraClassificationHead forward method, overrides the __call__() "
"special method."
msgstr ""

#: of
#: paddlenlp.transformers.electra.modeling.ElectraClassificationHead.forward:3
msgid ""
"Input sequence, usually the `sequence_output` of electra model. Its data "
"type should be float32 and its shape is [batch_size, sequence_length, "
"hidden_size]."
msgstr ""

#: of
#: paddlenlp.transformers.electra.modeling.ElectraClassificationHead.forward:7
msgid ""
"Returns a tensor of the input text classification logits. Shape as "
"`[batch_size, num_classes]` and dtype as float32."
msgstr ""

#: of
#: paddlenlp.transformers.electra.modeling.ElectraForSequenceClassification:1
msgid ""
"Electra Model with a linear layer on top of the output layer, designed "
"for sequence classification/regression tasks like GLUE tasks."
msgstr ""

#: of paddlenlp.transformers.electra.modeling.ElectraForMultipleChoice:4
#: paddlenlp.transformers.electra.modeling.ElectraForQuestionAnswering:4
#: paddlenlp.transformers.electra.modeling.ElectraForSequenceClassification:4
#: paddlenlp.transformers.electra.modeling.ElectraForTokenClassification:4
msgid "An instance of ElectraModel."
msgstr ""

#: of
#: paddlenlp.transformers.electra.modeling.ElectraForSequenceClassification:6
#: paddlenlp.transformers.electra.modeling.ElectraForTokenClassification:6
msgid "The number of classes. Defaults to `2`."
msgstr ""

#: of paddlenlp.transformers.electra.modeling.ElectraForMultipleChoice:8
#: paddlenlp.transformers.electra.modeling.ElectraForSequenceClassification:8
#: paddlenlp.transformers.electra.modeling.ElectraForTokenClassification:8
msgid ""
"The dropout probability for output of Electra. If None, use the same "
"value as `hidden_dropout_prob` of `ElectraModel` instance `electra`. "
"Defaults to None."
msgstr ""

#: of
#: paddlenlp.transformers.electra.modeling.ElectraForSequenceClassification:12
msgid "The activation function name for classifier. Defaults to \"gelu\"."
msgstr ""

#: of
#: paddlenlp.transformers.electra.modeling.ElectraForSequenceClassification:15
msgid "The epsilon to initialize nn.LayerNorm layers. Defaults to 1e-12."
msgstr ""

#: of
#: paddlenlp.transformers.electra.modeling.ElectraForSequenceClassification.forward:1
msgid ""
"The ElectraForSequenceClassification forward method, overrides the "
"__call__() special method."
msgstr ""

#: of
#: paddlenlp.transformers.electra.modeling.ElectraForSequenceClassification.forward:12
msgid ""
"Returns tensor `logits`, a tensor of the input text classification "
"logits. Shape as `[batch_size, num_classes]` and dtype as float32."
msgstr ""

#: of paddlenlp.transformers.electra.modeling.ElectraForTokenClassification:1
msgid ""
"Electra  Model with a linear layer on top of the hidden-states output "
"layer, designed for token classification tasks like NER tasks."
msgstr ""

#: of
#: paddlenlp.transformers.electra.modeling.ElectraForTokenClassification.forward:1
msgid ""
"The ElectraForTokenClassification forward method, overrides the "
"__call__() special method."
msgstr ""

#: of
#: paddlenlp.transformers.electra.modeling.ElectraForTokenClassification.forward:12
msgid ""
"Returns tensor `logits`, a tensor of the input token classification "
"logits. Shape as `[batch_size, sequence_length, num_classes]` and dtype "
"as `float32`."
msgstr ""

#: of paddlenlp.transformers.electra.modeling.ElectraPretrainingCriterion:1
#: paddlenlp.transformers.electra.modeling.ErnieHealthPretrainingCriterion:1
msgid ""
"Vocabulary size of `inputs_ids` in `ElectraModel`. Defines the number of "
"different tokens that can be represented by the `inputs_ids` passed when "
"calling `ElectraModel`."
msgstr ""

#: of paddlenlp.transformers.electra.modeling.ElectraPretrainingCriterion:4
#: paddlenlp.transformers.electra.modeling.ErnieHealthPretrainingCriterion:4
msgid "The weight of the Electra Generator."
msgstr ""

#: of paddlenlp.transformers.electra.modeling.ElectraPretrainingCriterion:6
#: paddlenlp.transformers.electra.modeling.ErnieHealthPretrainingCriterion:6
msgid "The weight of the Electra Discriminator."
msgstr ""

#: of
#: paddlenlp.transformers.electra.modeling.ElectraPretrainingCriterion.forward:1
#: paddlenlp.transformers.electra.modeling.ErnieHealthPretrainingCriterion.forward:1
msgid ""
"The scores of masked token prediction. Its data type should be float32. "
"and its shape is [batch_size, sequence_length, vocab_size]."
msgstr ""

#: of
#: paddlenlp.transformers.electra.modeling.ElectraPretrainingCriterion.forward:4
#: paddlenlp.transformers.electra.modeling.ErnieHealthPretrainingCriterion.forward:7
msgid ""
"The scores of masked token prediction. Its data type should be float32. "
"and its shape is [batch_size, sequence_length] or [sequence length] if "
"batch_size=1."
msgstr ""

#: of
#: paddlenlp.transformers.electra.modeling.ElectraPretrainingCriterion.forward:7
#: paddlenlp.transformers.electra.modeling.ErnieHealthPretrainingCriterion.forward:4
msgid ""
"The labels of the generator, its dimensionality is equal to "
"`generator_prediction_scores`. Its data type should be int64 and its "
"shape is [batch_size, sequence_size, 1]."
msgstr ""

#: of
#: paddlenlp.transformers.electra.modeling.ElectraPretrainingCriterion.forward:10
#: paddlenlp.transformers.electra.modeling.ErnieHealthPretrainingCriterion.forward:10
msgid ""
"The labels of the discriminator, its dimensionality is equal to "
"`discriminator_prediction_scores`. The labels should be numbers between 0"
" and 1. Its data type should be float32 and its shape is [batch_size, "
"sequence_size] or [sequence length] if batch_size=1."
msgstr ""

#: of
#: paddlenlp.transformers.electra.modeling.ElectraPretrainingCriterion.forward:17
#: paddlenlp.transformers.electra.modeling.ErnieHealthPretrainingCriterion.forward:17
msgid ""
"The pretraining loss, equals to weighted generator loss plus the weighted"
" discriminator loss. Its data type should be float32 and its shape is "
"[1]."
msgstr ""

#: of paddlenlp.transformers.electra.modeling.ElectraForMultipleChoice:1
msgid ""
"Electra Model with a linear layer on top of the hidden-states output "
"layer, designed for multiple choice tasks like RocStories/SWAG tasks."
msgstr ""

#: of paddlenlp.transformers.electra.modeling.ElectraForMultipleChoice:6
msgid "The number of choices. Defaults to `2`."
msgstr ""

#: of
#: paddlenlp.transformers.electra.modeling.ElectraForMultipleChoice.forward:1
msgid ""
"The ElectraForMultipleChoice forward method, overrides the __call__() "
"special method."
msgstr ""

#: of
#: paddlenlp.transformers.electra.modeling.ElectraForMultipleChoice.forward:3
#: paddlenlp.transformers.electra.modeling.ElectraForMultipleChoice.forward:5
#: paddlenlp.transformers.electra.modeling.ElectraForMultipleChoice.forward:7
#: paddlenlp.transformers.electra.modeling.ElectraForMultipleChoice.forward:9
msgid ""
"See :class:`ElectraModel` and shape as [batch_size, num_choice, "
"sequence_length]."
msgstr ""

#: of
#: paddlenlp.transformers.electra.modeling.ElectraForMultipleChoice.forward:12
msgid ""
"Returns tensor `reshaped_logits`, a tensor of the multiple choice "
"classification logits. Shape as `[batch_size, num_choice]` and dtype as "
"`float32`."
msgstr ""

#: of paddlenlp.transformers.electra.modeling.ElectraForQuestionAnswering:1
msgid ""
"Electra Model with a linear layer on top of the hidden-states output to "
"compute `span_start_logits` and `span_end_logits`, designed for question-"
"answering tasks like SQuAD."
msgstr ""

#: of
#: paddlenlp.transformers.electra.modeling.ElectraForQuestionAnswering.forward:1
msgid ""
"The ElectraForQuestionAnswering forward method, overrides the __call__() "
"special method."
msgstr ""

#: of
#: paddlenlp.transformers.electra.modeling.ElectraForQuestionAnswering.forward:12
msgid ""
"Returns tuple (`start_logits`, `end_logits`).  With the fields:  - "
"`start_logits` (Tensor):     A tensor of the input token classification "
"logits, indicates the start position of the labelled span.     Its data "
"type should be float32 and its shape is [batch_size, sequence_length].  -"
" `end_logits` (Tensor):     A tensor of the input token classification "
"logits, indicates the end position of the labelled span.     Its data "
"type should be float32 and its shape is [batch_size, sequence_length]."
msgstr ""

#: of
#: paddlenlp.transformers.electra.modeling.ElectraForQuestionAnswering.forward:12
msgid "Returns tuple (`start_logits`, `end_logits`)."
msgstr ""

#: of
#: paddlenlp.transformers.electra.modeling.ElectraForQuestionAnswering.forward:18
msgid "`start_logits` (Tensor):"
msgstr ""

#: of
#: paddlenlp.transformers.electra.modeling.ElectraForQuestionAnswering.forward:17
msgid ""
"A tensor of the input token classification logits, indicates the start "
"position of the labelled span. Its data type should be float32 and its "
"shape is [batch_size, sequence_length]."
msgstr ""

#: of
#: paddlenlp.transformers.electra.modeling.ElectraForQuestionAnswering.forward:21
msgid "`end_logits` (Tensor):"
msgstr ""

#: of
#: paddlenlp.transformers.electra.modeling.ElectraForQuestionAnswering.forward:21
msgid ""
"A tensor of the input token classification logits, indicates the end "
"position of the labelled span. Its data type should be float32 and its "
"shape is [batch_size, sequence_length]."
msgstr ""

#: of paddlenlp.transformers.electra.modeling.ErnieHealthForTotalPretraining:1
msgid "基类：:class:`paddlenlp.transformers.electra.modeling.ElectraForTotalPretraining`"
msgstr ""

#: of paddlenlp.transformers.electra.modeling.ErnieHealthForTotalPretraining:1
msgid "ERNIE-Health Model for pretraining task."
msgstr ""

#: of paddlenlp.transformers.electra.modeling.ErnieHealthForTotalPretraining:5
msgid ""
"class:`ErnieHealthDiscriminator): An instance of "
":class:`ErnieHealthDiscriminator`."
msgstr ""

#: of
#: paddlenlp.transformers.electra.modeling.ErnieHealthForTotalPretraining.sample_negatives_from_softmax:1
msgid "Sample K=5 non-original negative samples for candidate set."
msgstr ""

#: of
#: paddlenlp.transformers.electra.modeling.ErnieHealthForTotalPretraining.sample_negatives_from_softmax:3
msgid ""
"Returns tensor `neg_samples_ids`, a tensor of the negative samples of "
"original inputs. Shape as ` [batch_size, sequence_length, K, vocab_size]`"
" and dtype as `int64`."
msgstr ""

#: of paddlenlp.transformers.electra.modeling.ErnieHealthDiscriminator:4
msgid ""
"The Discriminators in ERNIE-Health (https://arxiv.org/abs/2110.07244), "
"including"
msgstr ""

#: of paddlenlp.transformers.electra.modeling.ErnieHealthDiscriminator:2
msgid "token-level Replaced Token Detection (RTD) task"
msgstr ""

#: of paddlenlp.transformers.electra.modeling.ErnieHealthDiscriminator:3
msgid "token-level Multi-Token Selection (MTS) task"
msgstr ""

#: of paddlenlp.transformers.electra.modeling.ErnieHealthDiscriminator:4
msgid "sequence-level Contrastive Sequence Prediction (CSP) task."
msgstr ""

#: of
#: paddlenlp.transformers.electra.modeling.ErnieHealthDiscriminator.forward:3
msgid ""
"The candidate indices of input sequence tokens in the vocabulary for MTS "
"task. Its data type should be `int64` and it has a shape of [batch_size, "
"sequence_length]."
msgstr ""

#: of
#: paddlenlp.transformers.electra.modeling.ErnieHealthDiscriminator.forward:13
msgid ""
"Returns list of tensors, the prediction results of RTD, MTS and CSP. The "
"logits' data type should be float32 and if batch_size > 1,     - the "
"shape of `logits_rtd` is [batch_size, sequence_length],     - the shape "
"of `logits_mts` is [batch_size, sequence_length, num_candidate],     - "
"the shape of `logits_csp` is [batch_size, 128]. If batch_size=1, the "
"shapes are [sequence_length], [sequence_length, num_cadidate], [128], "
"separately."
msgstr ""

#: of
#: paddlenlp.transformers.electra.modeling.ErnieHealthDiscriminator.forward:13
msgid ""
"Returns list of tensors, the prediction results of RTD, MTS and CSP. The "
"logits' data type should be float32 and if batch_size > 1,"
msgstr ""

#: of
#: paddlenlp.transformers.electra.modeling.ErnieHealthDiscriminator.forward:15
msgid "the shape of `logits_rtd` is [batch_size, sequence_length],"
msgstr ""

#: of
#: paddlenlp.transformers.electra.modeling.ErnieHealthDiscriminator.forward:16
msgid "the shape of `logits_mts` is [batch_size, sequence_length, num_candidate],"
msgstr ""

#: of
#: paddlenlp.transformers.electra.modeling.ErnieHealthDiscriminator.forward:17
msgid "the shape of `logits_csp` is [batch_size, 128]."
msgstr ""

#: of
#: paddlenlp.transformers.electra.modeling.ErnieHealthDiscriminator.forward:18
msgid ""
"If batch_size=1, the shapes are [sequence_length], [sequence_length, "
"num_cadidate], [128], separately."
msgstr ""

#~ msgid ""
#~ "Returns tuple (gen_logits, disc_logits, "
#~ "disc_labels, attention_mask).  With the "
#~ "fields:  - `gen_logits` (Tensor):     The "
#~ "scores of Electra Generator.     Its "
#~ "data type should be int64 and its"
#~ " shape is [batch_size, sequence_length, "
#~ "vocab_size].  - `disc_logits` (Tensor):     "
#~ "The prediction result of replaced"
#~ " tokens.     Its data type should be"
#~ " float32 and if batch_size>1, its "
#~ "shape is [batch_size, sequence_length],     if"
#~ " batch_size=1, its shape is "
#~ "[sequence_length].  - `disc_labels` (Tensor):"
#~ "     The labels of electra discriminator."
#~ " Its data type should be int32,"
#~ "     and its shape is [batch_size, "
#~ "sequence_length].  - `attention_mask` (Tensor):"
#~ "     See :class:`ElectraModel`. Its data "
#~ "type should be bool."
#~ msgstr ""

#~ msgid "Returns tuple (gen_logits, disc_logits, disc_labels, attention_mask)."
#~ msgstr ""

#~ msgid "`gen_logits` (Tensor):"
#~ msgstr ""

