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#: ../source/paddlenlp.transformers.mbart.modeling.rst:2
msgid "modeling"
msgstr ""

#: of paddlenlp.transformers.mbart.modeling.MBartDecoder:1
#: paddlenlp.transformers.mbart.modeling.MBartEncoder:1
#: paddlenlp.transformers.mbart.modeling.MBartForConditionalGeneration:1
#: paddlenlp.transformers.mbart.modeling.MBartForQuestionAnswering:1
#: paddlenlp.transformers.mbart.modeling.MBartForSequenceClassification:1
#: paddlenlp.transformers.mbart.modeling.MBartModel:1
msgid "基类：:class:`paddlenlp.transformers.mbart.modeling.MBartPretrainedModel`"
msgstr ""

#: of paddlenlp.transformers.mbart.modeling.MBartModel:1
msgid "The bare MBart Model transformer outputting raw hidden-states."
msgstr ""

#: of paddlenlp.transformers.mbart.modeling.MBartModel: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.mbart.modeling.MBartModel: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.mbart.modeling.MBartClassificationHead.forward
#: paddlenlp.transformers.mbart.modeling.MBartDecoder.forward
#: paddlenlp.transformers.mbart.modeling.MBartEncoder.forward
#: paddlenlp.transformers.mbart.modeling.MBartForConditionalGeneration
#: paddlenlp.transformers.mbart.modeling.MBartForConditionalGeneration.forward
#: paddlenlp.transformers.mbart.modeling.MBartForQuestionAnswering
#: paddlenlp.transformers.mbart.modeling.MBartForQuestionAnswering.forward
#: paddlenlp.transformers.mbart.modeling.MBartForSequenceClassification
#: paddlenlp.transformers.mbart.modeling.MBartForSequenceClassification.forward
#: paddlenlp.transformers.mbart.modeling.MBartModel
#: paddlenlp.transformers.mbart.modeling.MBartModel.forward
msgid "参数"
msgstr ""

#: of paddlenlp.transformers.mbart.modeling.MBartModel:10
msgid ""
"Vocabulary size of `inputs_ids` in `MBartModel`. 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 `MBartModel`."
msgstr ""

#: of paddlenlp.transformers.mbart.modeling.MBartModel:13
msgid ""
"The beginning of sequence token that was used during pretraining. Can be "
"used a sequence classifier token. Defaults to `0`."
msgstr ""

#: of paddlenlp.transformers.mbart.modeling.MBartModel:17
msgid "The index of padding token in the token vocabulary. Defaults to `1`."
msgstr ""

#: of paddlenlp.transformers.mbart.modeling.MBartModel:20
msgid ""
"A special token representing the end of a sequence that was used during "
"pretraining. Defaults to `2`."
msgstr ""

#: of paddlenlp.transformers.mbart.modeling.MBartModel:23
msgid ""
"Dimensionality of the embedding layer, encoder layer and decoder layer. "
"Defaults to `768`."
msgstr ""

#: of paddlenlp.transformers.mbart.modeling.MBartModel:25
msgid "Number of hidden layers in the Transformer encoder. Defaults to `6`."
msgstr ""

#: of paddlenlp.transformers.mbart.modeling.MBartModel:27
msgid "Number of hidden layers in the Transformer decoder. Defaults to `6`."
msgstr ""

#: of paddlenlp.transformers.mbart.modeling.MBartModel:29
msgid ""
"Number of attention heads for each attention layer in the Transformer "
"encoder. Defaults to `12`."
msgstr ""

#: of paddlenlp.transformers.mbart.modeling.MBartModel:32
msgid ""
"Number of attention heads for each attention layer in the Transformer "
"decoder. Defaults to `12`."
msgstr ""

#: of paddlenlp.transformers.mbart.modeling.MBartModel:35
msgid ""
"Dimensionality of the feed-forward (ff) layer in the encoder. Input "
"tensors to ff layers are firstly projected from `d_model` to "
"`encoder_ffn_dim`, and then projected back to `d_model`. Typically "
"`encoder_ffn_dim` is larger than `d_model`. Defaults to `3072`."
msgstr ""

#: of paddlenlp.transformers.mbart.modeling.MBartModel:40
msgid ""
"Dimensionality of the feed-forward (ff) layer in the encoder. Input "
"tensors to ff layers are firstly projected from `d_model` to "
"`decoder_ffn_dim`, and then projected back to `d_model`. Typically "
"`decoder_ffn_dim` is larger than `d_model`. Defaults to `3072`."
msgstr ""

#: of paddlenlp.transformers.mbart.modeling.MBartModel:45
msgid ""
"The dropout probability used in all fully connected layers (pre-process "
"and post-process of MHA and FFN sub-layer) in the encoders and decoders. "
"Defaults to `0.1`."
msgstr ""

#: of paddlenlp.transformers.mbart.modeling.MBartModel:48
msgid ""
"The non-linear activation function in the feed-forward layer. "
"``\"gelu\"``, ``\"relu\"`` and any other paddle supported activation "
"functions are supported. Defaults to `\"gelu\"`."
msgstr ""

#: of paddlenlp.transformers.mbart.modeling.MBartModel:52
msgid ""
"The dropout probability used in MultiHeadAttention in all encoder layers "
"and decoder layers to drop some attention target. Defaults to `0.1`."
msgstr ""

#: of paddlenlp.transformers.mbart.modeling.MBartModel:55
msgid ""
"The dropout probability used after FFN activation in all encoder layers "
"and decoder layers. Defaults to `0.1`."
msgstr ""

#: of paddlenlp.transformers.mbart.modeling.MBartModel:58
msgid ""
"The maximum value of the dimensionality of position encoding, which "
"dictates the maximum supported length of an input sequence. Defaults to "
"`1024`."
msgstr ""

#: of paddlenlp.transformers.mbart.modeling.MBartModel:61
msgid ""
"The standard deviation of the truncated_normal_initializer for "
"initializing all weight matrices. Default to `0.02`."
msgstr ""

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

#: of paddlenlp.transformers.mbart.modeling.MBartModel.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.mbart.modeling.MBartModel.forward:7
msgid ""
"Mask used in multi-head attention to avoid performing attention 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]`. For example, its shape can be  [batch_size, "
"sequence_length], [batch_size, sequence_length, sequence_length], "
"[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.mbart.modeling.MBartModel.forward:18
msgid ""
"Indices of decoder input sequence tokens in the vocabulary. Its data type"
" should be `int64` and it has a shape of [batch_size, sequence_length]. "
"Defaults to `None`, which means no `decoder_input_ids` is provided, the "
"model will create the tensor by shifting the `input_ids` to the right."
msgstr ""

#: of paddlenlp.transformers.mbart.modeling.MBartModel.forward:23
msgid ""
"Mask used in multi-head attention to avoid performing attention to some "
"unwanted positions in `decoder_input_ids`. Its data type and shape is the"
" same as `attention_mask`. Defaults to `None`."
msgstr ""

#: of paddlenlp.transformers.mbart.modeling.MBartModel.forward:26
msgid ""
"The output of the encoder, a tuple consists `last_hidden_state`, "
"`hidden_states`(optional), `attentions`(optional). The data type of "
"`last_hidden_state` is float32 and its shape is `[batch_size, "
"sequence_length, hidden_size]`. `hidden_states` is hidden_states of all "
"layers in the Transformer encoder. The length of `hidden_states` is "
"`num_hidden_layers + 1`. For all element in the tuple, its data type "
"should be float32 and its shape is [`batch_size, sequence_length, "
"hidden_size`]. `attentions` is attentions of all layers of in the "
"Transformer encoder. The length of `attentions` is `num_hidden_layers`. "
"For all element in the tuple, its data type should be float32 and its "
"shape is [`batch_size, num_attention_heads, sequence_length, "
"sequence_length`]."
msgstr ""

#: of paddlenlp.transformers.mbart.modeling.MBartModel.forward:33
msgid ""
"Whether or not to use cache. Defaults to `False`. If set to `True`, key "
"value states will be returned and can be used to speed up decoding."
msgstr ""

#: of paddlenlp.transformers.mbart.modeling.MBartModel.forward:36
msgid ""
"It is a list, and each element in the list is a tuple "
"`(incremental_cache, static_cache)`. See `TransformerDecoder.gen_cache "
"<https://github.com/PaddlePaddle/Paddle/blob/release/2.1/python/paddle/nn/layer/transformer.py#L1060>`__"
" for more details. It is only used for inference and should be None for "
"training. Default to `None`."
msgstr ""

#: of paddlenlp.transformers.mbart.modeling.MBartDecoder.forward
#: paddlenlp.transformers.mbart.modeling.MBartEncoder.forward
#: paddlenlp.transformers.mbart.modeling.MBartForConditionalGeneration.forward
#: paddlenlp.transformers.mbart.modeling.MBartForQuestionAnswering.forward
#: paddlenlp.transformers.mbart.modeling.MBartForSequenceClassification.forward
#: paddlenlp.transformers.mbart.modeling.MBartModel.forward
msgid "返回"
msgstr ""

#: of paddlenlp.transformers.mbart.modeling.MBartDecoder.forward:14
#: paddlenlp.transformers.mbart.modeling.MBartModel.forward:42
msgid ""
"Returns tensor `decoder_output`, 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.mbart.modeling.MBartDecoder.forward
#: paddlenlp.transformers.mbart.modeling.MBartEncoder.forward
#: paddlenlp.transformers.mbart.modeling.MBartForConditionalGeneration.forward
#: paddlenlp.transformers.mbart.modeling.MBartForQuestionAnswering.forward
#: paddlenlp.transformers.mbart.modeling.MBartForSequenceClassification.forward
#: paddlenlp.transformers.mbart.modeling.MBartModel.forward
msgid "返回类型"
msgstr ""

#: of
#: paddlenlp.transformers.mbart.modeling.MBartForConditionalGeneration.forward:31
#: paddlenlp.transformers.mbart.modeling.MBartForQuestionAnswering.forward:32
#: paddlenlp.transformers.mbart.modeling.MBartForSequenceClassification.forward:23
#: paddlenlp.transformers.mbart.modeling.MBartModel.forward:47
msgid "示例"
msgstr ""

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

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

#: of paddlenlp.transformers.mbart.modeling.MBartPretrainedModel.init_weights:1
msgid "Initialization hook"
msgstr ""

#: of paddlenlp.transformers.mbart.modeling.MBartEncoder:1
msgid ""
"The Transformer Encoder of MBartModel. The arguments of MBartEncoder can "
"see :class:`MBartModel`."
msgstr ""

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

#: of paddlenlp.transformers.mbart.modeling.MBartDecoder.forward:3
#: paddlenlp.transformers.mbart.modeling.MBartDecoder.forward:5
#: paddlenlp.transformers.mbart.modeling.MBartDecoder.forward:7
#: paddlenlp.transformers.mbart.modeling.MBartDecoder.forward:9
#: paddlenlp.transformers.mbart.modeling.MBartDecoder.forward:11
#: paddlenlp.transformers.mbart.modeling.MBartEncoder.forward:3
#: paddlenlp.transformers.mbart.modeling.MBartEncoder.forward:5
#: paddlenlp.transformers.mbart.modeling.MBartForConditionalGeneration.forward:3
#: paddlenlp.transformers.mbart.modeling.MBartForConditionalGeneration.forward:5
#: paddlenlp.transformers.mbart.modeling.MBartForConditionalGeneration.forward:7
#: paddlenlp.transformers.mbart.modeling.MBartForConditionalGeneration.forward:9
#: paddlenlp.transformers.mbart.modeling.MBartForConditionalGeneration.forward:11
#: paddlenlp.transformers.mbart.modeling.MBartForConditionalGeneration.forward:13
#: paddlenlp.transformers.mbart.modeling.MBartForConditionalGeneration.forward:15
#: paddlenlp.transformers.mbart.modeling.MBartForConditionalGeneration.forward:27
#: paddlenlp.transformers.mbart.modeling.MBartForQuestionAnswering.forward:3
#: paddlenlp.transformers.mbart.modeling.MBartForQuestionAnswering.forward:5
#: paddlenlp.transformers.mbart.modeling.MBartForQuestionAnswering.forward:7
#: paddlenlp.transformers.mbart.modeling.MBartForQuestionAnswering.forward:9
#: paddlenlp.transformers.mbart.modeling.MBartForQuestionAnswering.forward:11
#: paddlenlp.transformers.mbart.modeling.MBartForQuestionAnswering.forward:13
#: paddlenlp.transformers.mbart.modeling.MBartForQuestionAnswering.forward:15
#: paddlenlp.transformers.mbart.modeling.MBartForSequenceClassification.forward:3
#: paddlenlp.transformers.mbart.modeling.MBartForSequenceClassification.forward:5
#: paddlenlp.transformers.mbart.modeling.MBartForSequenceClassification.forward:7
#: paddlenlp.transformers.mbart.modeling.MBartForSequenceClassification.forward:9
#: paddlenlp.transformers.mbart.modeling.MBartForSequenceClassification.forward:11
#: paddlenlp.transformers.mbart.modeling.MBartForSequenceClassification.forward:13
#: paddlenlp.transformers.mbart.modeling.MBartForSequenceClassification.forward:15
msgid "See :class:`MBartModel`."
msgstr ""

#: of paddlenlp.transformers.mbart.modeling.MBartEncoder.forward:8
msgid ""
"Returns tensor `encoder_output`, 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.mbart.modeling.MBartDecoder:1
msgid ""
"The Transformer Decoder of MBartModel. The arguments of MBartDecoder can "
"see :class:`MBartModel`."
msgstr ""

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

#: of paddlenlp.transformers.mbart.modeling.MBartClassificationHead:1
msgid "基类：:class:`paddle.fluid.dygraph.layers.Layer`"
msgstr ""

#: of paddlenlp.transformers.mbart.modeling.MBartClassificationHead:1
msgid "Head for sentence-level classification tasks."
msgstr ""

#: of paddlenlp.transformers.mbart.modeling.MBartClassificationHead.forward:1
msgid "Hidden states of the classification model."
msgstr ""

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

#: of paddlenlp.transformers.mbart.modeling.MBartForConditionalGeneration:4
#: paddlenlp.transformers.mbart.modeling.MBartForQuestionAnswering:4
#: paddlenlp.transformers.mbart.modeling.MBartForSequenceClassification:4
msgid "An instance of MBartModel."
msgstr ""

#: of paddlenlp.transformers.mbart.modeling.MBartForSequenceClassification:6
msgid "The number of different labels. Defaults to `2`."
msgstr ""

#: of paddlenlp.transformers.mbart.modeling.MBartForSequenceClassification:8
msgid ""
"The dropout probability for output of MBart. If None, use the same value "
"as `hidden_dropout_prob` of `MBartModel` instance `mbart`. Defaults to "
"None."
msgstr ""

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

#: of
#: paddlenlp.transformers.mbart.modeling.MBartForSequenceClassification.forward:18
msgid ""
"Returns tensor `logits`, a tensor of the input text classification "
"logits. Shape as `[batch_size, num_labels]` and dtype as float32."
msgstr ""

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

#: of
#: paddlenlp.transformers.mbart.modeling.MBartForQuestionAnswering.forward:18
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.mbart.modeling.MBartForQuestionAnswering.forward:18
msgid "Returns tuple (`start_logits`, `end_logits`)."
msgstr ""

#: of
#: paddlenlp.transformers.mbart.modeling.MBartForConditionalGeneration.forward:20
#: paddlenlp.transformers.mbart.modeling.MBartForQuestionAnswering.forward:20
msgid "With the fields:"
msgstr ""

#: of
#: paddlenlp.transformers.mbart.modeling.MBartForQuestionAnswering.forward:24
msgid "`start_logits` (Tensor):"
msgstr ""

#: of
#: paddlenlp.transformers.mbart.modeling.MBartForQuestionAnswering.forward:23
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.mbart.modeling.MBartForQuestionAnswering.forward:27
msgid "`end_logits` (Tensor):"
msgstr ""

#: of
#: paddlenlp.transformers.mbart.modeling.MBartForQuestionAnswering.forward:27
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.mbart.modeling.MBartForConditionalGeneration:1
msgid ""
"MBart 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.mbart.modeling.MBartForConditionalGeneration.forward:1
msgid ""
"The MBartForConditionalGeneration forward method, overrides the "
"__call__() special method."
msgstr ""

#: of
#: paddlenlp.transformers.mbart.modeling.MBartForConditionalGeneration.forward:18
msgid ""
"Returns Tensor `lm_logits` if `use_cache` is `False`, otherwise, returns "
"tuple (`lm_logits`, `cache`).  With the fields:  - `lm_logits` (Tensor):"
"     The generated sentence of the model.     Its data type should be "
"float32 and has a shape of [batch_size, sequence_length, vocab_size].  - "
"`cache` (Tensor):     See :class:`MBartModel`."
msgstr ""

#: of
#: paddlenlp.transformers.mbart.modeling.MBartForConditionalGeneration.forward:18
msgid ""
"Returns Tensor `lm_logits` if `use_cache` is `False`, otherwise, returns "
"tuple (`lm_logits`, `cache`)."
msgstr ""

#: of
#: paddlenlp.transformers.mbart.modeling.MBartForConditionalGeneration.forward:24
msgid "`lm_logits` (Tensor):"
msgstr ""

#: of
#: paddlenlp.transformers.mbart.modeling.MBartForConditionalGeneration.forward:23
msgid ""
"The generated sentence of the model. Its data type should be float32 and "
"has a shape of [batch_size, sequence_length, vocab_size]."
msgstr ""

#: of
#: paddlenlp.transformers.mbart.modeling.MBartForConditionalGeneration.forward:26
msgid "`cache` (Tensor):"
msgstr ""

