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

#: of paddlenlp.transformers.chinesebert.modeling.ChineseBertForPretraining:1
#: paddlenlp.transformers.chinesebert.modeling.ChineseBertForQuestionAnswering:1
#: paddlenlp.transformers.chinesebert.modeling.ChineseBertForSequenceClassification:1
#: paddlenlp.transformers.chinesebert.modeling.ChineseBertForTokenClassification:1
#: paddlenlp.transformers.chinesebert.modeling.ChineseBertModel:1
msgid "基类：:class:`paddlenlp.transformers.chinesebert.modeling.ChineseBertPretrainedModel`"
msgstr ""

#: of paddlenlp.transformers.chinesebert.modeling.ChineseBertModel:1
msgid "The bare ChineseBert Model transformer outputting raw hidden-states."
msgstr ""

#: of paddlenlp.transformers.chinesebert.modeling.ChineseBertModel: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.chinesebert.modeling.ChineseBertModel: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.chinesebert.modeling.ChineseBertForPretraining
#: paddlenlp.transformers.chinesebert.modeling.ChineseBertForPretraining.forward
#: paddlenlp.transformers.chinesebert.modeling.ChineseBertForQuestionAnswering
#: paddlenlp.transformers.chinesebert.modeling.ChineseBertForQuestionAnswering.forward
#: paddlenlp.transformers.chinesebert.modeling.ChineseBertForSequenceClassification
#: paddlenlp.transformers.chinesebert.modeling.ChineseBertForSequenceClassification.forward
#: paddlenlp.transformers.chinesebert.modeling.ChineseBertForTokenClassification
#: paddlenlp.transformers.chinesebert.modeling.ChineseBertForTokenClassification.forward
#: paddlenlp.transformers.chinesebert.modeling.ChineseBertModel
#: paddlenlp.transformers.chinesebert.modeling.ChineseBertModel.forward
#: paddlenlp.transformers.chinesebert.modeling.ChineseBertPretrainingCriterion
#: paddlenlp.transformers.chinesebert.modeling.ChineseBertPretrainingCriterion.forward
msgid "参数"
msgstr ""

#: of paddlenlp.transformers.chinesebert.modeling.ChineseBertModel:10
msgid ""
"Vocabulary size of `inputs_ids` in `BChineseBertModel`. 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 "
"`ChineseBertModel`."
msgstr ""

#: of paddlenlp.transformers.chinesebert.modeling.ChineseBertModel:13
msgid ""
"Dimensionality of the embedding layer, encoder layer and pooler layer. "
"Defaults to `768`."
msgstr ""

#: of paddlenlp.transformers.chinesebert.modeling.ChineseBertModel:15
msgid "Number of hidden layers in the Transformer encoder. Defaults to `12`."
msgstr ""

#: of paddlenlp.transformers.chinesebert.modeling.ChineseBertModel:17
msgid ""
"Number of attention heads for each attention layer in the Transformer "
"encoder. Defaults to `12`."
msgstr ""

#: of paddlenlp.transformers.chinesebert.modeling.ChineseBertModel:20
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`. Defaults to `3072`."
msgstr ""

#: of paddlenlp.transformers.chinesebert.modeling.ChineseBertModel:25
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.chinesebert.modeling.ChineseBertModel:29
msgid ""
"The dropout probability for all fully connected layers in the embeddings "
"and encoder. Defaults to `0.1`."
msgstr ""

#: of paddlenlp.transformers.chinesebert.modeling.ChineseBertModel:32
msgid ""
"The dropout probability used in MultiHeadAttention in all encoder layers "
"to drop some attention target. Defaults to `0.1`."
msgstr ""

#: of paddlenlp.transformers.chinesebert.modeling.ChineseBertModel:35
msgid ""
"The maximum value of the dimensionality of position encoding, which "
"dictates the maximum supported length of an input sequence. Defaults to "
"`512`."
msgstr ""

#: of paddlenlp.transformers.chinesebert.modeling.ChineseBertModel:38
msgid "The vocabulary size of `token_type_ids`. Defaults to `2`."
msgstr ""

#: of paddlenlp.transformers.chinesebert.modeling.ChineseBertModel:41
msgid ""
"The standard deviation of the normal initializer. Defaults to 0.02.  .. "
"note::     A normal_initializer initializes weight matrices as normal "
"distributions.     See :meth:`BertPretrainedModel.init_weights()` for how"
" weights are initialized in `BertModel`."
msgstr ""

#: of paddlenlp.transformers.chinesebert.modeling.ChineseBertModel:41
msgid "The standard deviation of the normal initializer. Defaults to 0.02."
msgstr ""

#: of paddlenlp.transformers.chinesebert.modeling.ChineseBertModel:45
msgid ""
"A normal_initializer initializes weight matrices as normal distributions."
" See :meth:`BertPretrainedModel.init_weights()` for how weights are "
"initialized in `BertModel`."
msgstr ""

#: of paddlenlp.transformers.chinesebert.modeling.ChineseBertModel:48
msgid "The index of padding token in the token vocabulary. Defaults to `0`."
msgstr ""

#: of paddlenlp.transformers.chinesebert.modeling.ChineseBertModel:51
msgid ""
"The non-linear activation function in the pooling layer. Defaults to "
"`\"tanh\"`."
msgstr ""

#: of paddlenlp.transformers.chinesebert.modeling.ChineseBertModel:54
msgid "The epsilon of layernorm. Defaults to `1e-12`."
msgstr ""

#: of paddlenlp.transformers.chinesebert.modeling.ChineseBertModel:56
msgid "The dim of glyph_embedding. Defaults to `1728`."
msgstr ""

#: of paddlenlp.transformers.chinesebert.modeling.ChineseBertModel:59
msgid "The length of pinyin map. Defaults to `32`."
msgstr ""

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

#: of paddlenlp.transformers.chinesebert.modeling.ChineseBertModel.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.chinesebert.modeling.ChineseBertModel.forward:7
msgid ""
"Indices of input sequence tokens pinyin. We apply a CNN model with width "
"2 on the pinyin sequence, followed by max-pooling to derive the resulting"
" pinyin embedding. This makes output dimensionality immune to the length "
"of the input pinyin sequence. The length of the input pinyin sequence is "
"fixed at 8. Its data type should be `int64` and it has a shape of "
"[batch_size, sequence_length, 8]. Defaults to `None`, which means we "
"don't add pinyin embeddings."
msgstr ""

#: of paddlenlp.transformers.chinesebert.modeling.ChineseBertModel.forward:14
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.chinesebert.modeling.ChineseBertModel.forward:14
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.chinesebert.modeling.ChineseBertModel.forward:19
msgid "0 corresponds to a *sentence A* token,"
msgstr ""

#: of paddlenlp.transformers.chinesebert.modeling.ChineseBertModel.forward:20
msgid "1 corresponds to a *sentence B* token."
msgstr ""

#: of paddlenlp.transformers.chinesebert.modeling.ChineseBertModel.forward:22
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.chinesebert.modeling.ChineseBertModel.forward:25
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.chinesebert.modeling.ChineseBertModel.forward:29
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.chinesebert.modeling.ChineseBertModel.forward:38
msgid "Whether to return the output of each hidden layers. Defaults to `False`."
msgstr ""

#: of
#: paddlenlp.transformers.chinesebert.modeling.ChineseBertForPretraining.forward
#: paddlenlp.transformers.chinesebert.modeling.ChineseBertForQuestionAnswering.forward
#: paddlenlp.transformers.chinesebert.modeling.ChineseBertForSequenceClassification.forward
#: paddlenlp.transformers.chinesebert.modeling.ChineseBertForTokenClassification.forward
#: paddlenlp.transformers.chinesebert.modeling.ChineseBertModel.forward
#: paddlenlp.transformers.chinesebert.modeling.ChineseBertPretrainingCriterion.forward
msgid "返回"
msgstr ""

#: of paddlenlp.transformers.chinesebert.modeling.ChineseBertModel.forward:42
msgid ""
"Returns tuple (`sequence_output`, `pooled_output`) or (`encoder_outputs`,"
" `pooled_output`).  With the fields:  - `sequence_output` (Tensor):     "
"Sequence of hidden-states at the last layer of the model.     It's data "
"type should be float32 and its shape is [batch_size, sequence_length, "
"hidden_size].  - `pooled_output` (Tensor):     The output of first token "
"(`[CLS]`) in sequence.     We \"pool\" the model by simply taking the "
"hidden state corresponding to the first token.     Its data type should "
"be float32 and its shape is [batch_size, hidden_size].  - "
"`encoder_outputs` (List(Tensor)):     A list of Tensor containing hidden-"
"states of the model at each hidden layer in the Transformer encoder.     "
"The length of the list is `num_hidden_layers`.     Each Tensor has a data"
" type of float32 and its shape is [batch_size, sequence_length, "
"hidden_size]."
msgstr ""

#: of paddlenlp.transformers.chinesebert.modeling.ChineseBertModel.forward:42
msgid ""
"Returns tuple (`sequence_output`, `pooled_output`) or (`encoder_outputs`,"
" `pooled_output`)."
msgstr ""

#: of
#: paddlenlp.transformers.chinesebert.modeling.ChineseBertForPretraining.forward:16
#: paddlenlp.transformers.chinesebert.modeling.ChineseBertForQuestionAnswering.forward:12
#: paddlenlp.transformers.chinesebert.modeling.ChineseBertModel.forward:44
msgid "With the fields:"
msgstr ""

#: of paddlenlp.transformers.chinesebert.modeling.ChineseBertModel.forward:48
msgid "`sequence_output` (Tensor):"
msgstr ""

#: of paddlenlp.transformers.chinesebert.modeling.ChineseBertModel.forward:47
msgid ""
"Sequence of hidden-states at the last layer of the model. It's data type "
"should be float32 and its shape is [batch_size, sequence_length, "
"hidden_size]."
msgstr ""

#: of paddlenlp.transformers.chinesebert.modeling.ChineseBertModel.forward:53
msgid "`pooled_output` (Tensor):"
msgstr ""

#: of paddlenlp.transformers.chinesebert.modeling.ChineseBertModel.forward:51
msgid ""
"The output of first token (`[CLS]`) in sequence. We \"pool\" the model by"
" simply taking the hidden state corresponding to the first token. Its "
"data type should be float32 and its shape is [batch_size, hidden_size]."
msgstr ""

#: of paddlenlp.transformers.chinesebert.modeling.ChineseBertModel.forward:57
msgid "`encoder_outputs` (List(Tensor)):"
msgstr ""

#: of paddlenlp.transformers.chinesebert.modeling.ChineseBertModel.forward:56
msgid ""
"A list of Tensor containing hidden-states of the model at each hidden "
"layer in the Transformer encoder. The length of the list is "
"`num_hidden_layers`. Each Tensor has a data type of float32 and its shape"
" is [batch_size, sequence_length, hidden_size]."
msgstr ""

#: of
#: paddlenlp.transformers.chinesebert.modeling.ChineseBertForPretraining.forward
#: paddlenlp.transformers.chinesebert.modeling.ChineseBertForQuestionAnswering.forward
#: paddlenlp.transformers.chinesebert.modeling.ChineseBertForSequenceClassification.forward
#: paddlenlp.transformers.chinesebert.modeling.ChineseBertForTokenClassification.forward
#: paddlenlp.transformers.chinesebert.modeling.ChineseBertModel.forward
#: paddlenlp.transformers.chinesebert.modeling.ChineseBertPretrainingCriterion.forward
msgid "返回类型"
msgstr ""

#: of
#: paddlenlp.transformers.chinesebert.modeling.ChineseBertForQuestionAnswering.forward:24
#: paddlenlp.transformers.chinesebert.modeling.ChineseBertForSequenceClassification.forward:19
#: paddlenlp.transformers.chinesebert.modeling.ChineseBertForTokenClassification.forward:19
#: paddlenlp.transformers.chinesebert.modeling.ChineseBertModel.forward:62
msgid "示例"
msgstr ""

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

#: of paddlenlp.transformers.chinesebert.modeling.ChineseBertPretrainedModel:1
msgid ""
"An abstract class for pretrained ChineseBert models. It provides "
"ChineseBert 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.chinesebert.modeling.ChineseBertPretrainedModel.init_weights:1
msgid "Initialize the weights."
msgstr ""

#: of paddlenlp.transformers.chinesebert.modeling.ChineseBertForPretraining:1
msgid "ChineseBert Model with pretraining tasks on top."
msgstr ""

#: of paddlenlp.transformers.chinesebert.modeling.ChineseBertForPretraining:3
msgid "An instance of :class:`ChineseBertModel`."
msgstr ""

#: of
#: paddlenlp.transformers.chinesebert.modeling.ChineseBertForPretraining.forward:1
#: paddlenlp.transformers.chinesebert.modeling.ChineseBertForPretraining.forward:3
#: paddlenlp.transformers.chinesebert.modeling.ChineseBertForPretraining.forward:5
#: paddlenlp.transformers.chinesebert.modeling.ChineseBertForPretraining.forward:7
#: paddlenlp.transformers.chinesebert.modeling.ChineseBertForPretraining.forward:9
#: paddlenlp.transformers.chinesebert.modeling.ChineseBertForQuestionAnswering.forward:3
#: paddlenlp.transformers.chinesebert.modeling.ChineseBertForQuestionAnswering.forward:5
#: paddlenlp.transformers.chinesebert.modeling.ChineseBertForQuestionAnswering.forward:7
#: paddlenlp.transformers.chinesebert.modeling.ChineseBertForSequenceClassification.forward:3
#: paddlenlp.transformers.chinesebert.modeling.ChineseBertForSequenceClassification.forward:5
#: paddlenlp.transformers.chinesebert.modeling.ChineseBertForSequenceClassification.forward:7
#: paddlenlp.transformers.chinesebert.modeling.ChineseBertForSequenceClassification.forward:9
#: paddlenlp.transformers.chinesebert.modeling.ChineseBertForSequenceClassification.forward:11
#: paddlenlp.transformers.chinesebert.modeling.ChineseBertForTokenClassification.forward:3
#: paddlenlp.transformers.chinesebert.modeling.ChineseBertForTokenClassification.forward:5
#: paddlenlp.transformers.chinesebert.modeling.ChineseBertForTokenClassification.forward:7
#: paddlenlp.transformers.chinesebert.modeling.ChineseBertForTokenClassification.forward:9
#: paddlenlp.transformers.chinesebert.modeling.ChineseBertForTokenClassification.forward:11
msgid "See :class:`ChineseBertModel`."
msgstr ""

#: of
#: paddlenlp.transformers.chinesebert.modeling.ChineseBertForPretraining.forward:11
msgid "See :class:`ChineseBertPretrainingHeads`."
msgstr ""

#: of
#: paddlenlp.transformers.chinesebert.modeling.ChineseBertForPretraining.forward:14
msgid ""
"Returns tuple (``prediction_scores``, ``seq_relationship_score``).  With "
"the fields:  - `prediction_scores` (Tensor):     The scores of masked "
"token prediction. Its data type should be float32.     If "
"`masked_positions` is None, its shape is [batch_size, sequence_length, "
"vocab_size].     Otherwise, its shape is [batch_size, mask_token_num, "
"vocab_size].  - `seq_relationship_score` (Tensor):     The scores of next"
" sentence prediction.     Its data type should be float32 and its shape "
"is [batch_size, 2]."
msgstr ""

#: of
#: paddlenlp.transformers.chinesebert.modeling.ChineseBertForPretraining.forward:14
msgid "Returns tuple (``prediction_scores``, ``seq_relationship_score``)."
msgstr ""

#: of
#: paddlenlp.transformers.chinesebert.modeling.ChineseBertForPretraining.forward:21
msgid "`prediction_scores` (Tensor):"
msgstr ""

#: of
#: paddlenlp.transformers.chinesebert.modeling.ChineseBertForPretraining.forward:19
msgid ""
"The scores of masked token prediction. Its data type should be float32. "
"If `masked_positions` is None, its shape is [batch_size, sequence_length,"
" vocab_size]. Otherwise, its shape is [batch_size, mask_token_num, "
"vocab_size]."
msgstr ""

#: of
#: paddlenlp.transformers.chinesebert.modeling.ChineseBertForPretraining.forward:24
msgid "`seq_relationship_score` (Tensor):"
msgstr ""

#: of
#: paddlenlp.transformers.chinesebert.modeling.ChineseBertForPretraining.forward:24
msgid ""
"The scores of next sentence prediction. Its data type should be float32 "
"and its shape is [batch_size, 2]."
msgstr ""

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

#: of
#: paddlenlp.transformers.chinesebert.modeling.ChineseBertPretrainingCriterion:1
msgid ""
"Vocabulary size of `inputs_ids` in `ChineseBertModel`. Defines the number"
" of different tokens that can be represented by the `inputs_ids` passed "
"when calling `ChineseBertBertModel`."
msgstr ""

#: of
#: paddlenlp.transformers.chinesebert.modeling.ChineseBertPretrainingCriterion.forward:1
msgid ""
"The scores of masked token prediction. Its data type should be float32. "
"If `masked_positions` is None, its shape is [batch_size, sequence_length,"
" vocab_size]. Otherwise, its shape is [batch_size, mask_token_num, "
"vocab_size]"
msgstr ""

#: of
#: paddlenlp.transformers.chinesebert.modeling.ChineseBertPretrainingCriterion.forward:5
msgid ""
"The scores of next sentence prediction. Its data type should be float32 "
"and its shape is [batch_size, 2]"
msgstr ""

#: of
#: paddlenlp.transformers.chinesebert.modeling.ChineseBertPretrainingCriterion.forward:8
msgid ""
"The labels of the masked language modeling, its dimensionality is equal "
"to `prediction_scores`. Its data type should be int64. If "
"`masked_positions` is None, its shape is [batch_size, sequence_length, "
"1]. Otherwise, its shape is [batch_size, mask_token_num, 1]"
msgstr ""

#: of
#: paddlenlp.transformers.chinesebert.modeling.ChineseBertPretrainingCriterion.forward:12
msgid ""
"The labels of the next sentence prediction task, the dimensionality of "
"`next_sentence_labels` is equal to `seq_relation_labels`. Its data type "
"should be int64 and its shape is [batch_size, 1]"
msgstr ""

#: of
#: paddlenlp.transformers.chinesebert.modeling.ChineseBertPretrainingCriterion.forward:16
msgid ""
"The scale of masked tokens. Used for the normalization of masked language"
" modeling loss. If it is a `Tensor`, its data type should be int64 and "
"its shape is equal to `prediction_scores`."
msgstr ""

#: of
#: paddlenlp.transformers.chinesebert.modeling.ChineseBertPretrainingCriterion.forward:20
msgid ""
"The pretraining loss, equals to the sum of `masked_lm_loss` plus the mean"
" of `next_sentence_loss`. Its data type should be float32 and its shape "
"is [1]."
msgstr ""

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

#: of
#: paddlenlp.transformers.chinesebert.modeling.ChineseBertForQuestionAnswering:4
#: paddlenlp.transformers.chinesebert.modeling.ChineseBertForSequenceClassification:4
#: paddlenlp.transformers.chinesebert.modeling.ChineseBertForTokenClassification:4
msgid "An instance of ChineseBertModel."
msgstr ""

#: of
#: paddlenlp.transformers.chinesebert.modeling.ChineseBertForSequenceClassification:6
#: paddlenlp.transformers.chinesebert.modeling.ChineseBertForTokenClassification:6
msgid "The number of classes. Defaults to `2`."
msgstr ""

#: of
#: paddlenlp.transformers.chinesebert.modeling.ChineseBertForSequenceClassification:8
#: paddlenlp.transformers.chinesebert.modeling.ChineseBertForTokenClassification:8
msgid ""
"The dropout probability for output of ChineseBert. If None, use the same "
"value as `hidden_dropout_prob` of `ChineseBertModel` instance "
"`chinesebert`. Defaults to None."
msgstr ""

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

#: of
#: paddlenlp.transformers.chinesebert.modeling.ChineseBertForSequenceClassification.forward:14
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.chinesebert.modeling.ChineseBertForTokenClassification:1
msgid ""
"ChineseBert 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.chinesebert.modeling.ChineseBertForTokenClassification.forward:1
msgid ""
"The ChineseBertForTokenClassification forward method, overrides the "
"__call__() special method."
msgstr ""

#: of
#: paddlenlp.transformers.chinesebert.modeling.ChineseBertForTokenClassification.forward:14
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.chinesebert.modeling.ChineseBertForQuestionAnswering:1
msgid ""
"ChineseBert 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.chinesebert.modeling.ChineseBertForQuestionAnswering:6
msgid ""
"The dropout probability for output of ChineseBert. If None, use the same "
"value as `hidden_dropout_prob` of `ChineseBertModel` instance "
"`chinesebert`. Defaults to `None`."
msgstr ""

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

#: of
#: paddlenlp.transformers.chinesebert.modeling.ChineseBertForQuestionAnswering.forward:10
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.chinesebert.modeling.ChineseBertForQuestionAnswering.forward:10
msgid "Returns tuple (`start_logits`, `end_logits`)."
msgstr ""

#: of
#: paddlenlp.transformers.chinesebert.modeling.ChineseBertForQuestionAnswering.forward:16
msgid "`start_logits` (Tensor):"
msgstr ""

#: of
#: paddlenlp.transformers.chinesebert.modeling.ChineseBertForQuestionAnswering.forward:15
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.chinesebert.modeling.ChineseBertForQuestionAnswering.forward:19
msgid "`end_logits` (Tensor):"
msgstr ""

#: of
#: paddlenlp.transformers.chinesebert.modeling.ChineseBertForQuestionAnswering.forward:19
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 ""

