modeling¶
Modeling classes for LayoutLMv2 model.
-
class
LayoutLMv2Model
(with_pool='tanh', **kwargs)[源代码]¶ 基类:
paddlenlp.transformers.layoutlmv2.modeling.LayoutLMv2PretrainedModel
The bare LayoutLMv2 Model outputting raw hidden-states.
This model inherits from
PretrainedModel
. Refer to the superclass documentation for the generic methods.This model is also a Paddle paddle.nn.Layer subclass. Use it as a regular Paddle Layer and refer to the Paddle documentation for all matter related to general usage and behavior.
- 参数
vocab_size (
int
) -- Vocabulary size of the XLNet model. Defines the number of different tokens that can be represented by theinputs_ids
passed when calling XLNetModel.hidden_size (
int
, optional) -- Dimensionality of the encoder layers and the pooler layer. Defaults to768
.num_hidden_layers (
int
, optional) -- Number of hidden layers in the Transformer encoder. Defaults to12
.num_attention_heads (
int
, optional) -- Number of attention heads for each attention layer in the Transformer encoder. Defaults to12
.intermediate_size (
int
, optional) -- Dimensionality of the "intermediate" (often named feed-forward) layer in the Transformer encoder. Defaults to3072
.hidden_act (
str
, optional) -- The non-linear activation function in the feed-forward layer."gelu"
,"relu"
and any other paddle supported activation functions are supported. Defaults to"gelu"
.hidden_dropout_prob (
float
, optional) -- The dropout probability for all fully connected layers in the embeddings and encoder. Defaults to0.1
.attention_probs_dropout_prob (
float
, optional) -- The dropout probability for all fully connected layers in the pooler. Defaults to0.1
.initializer_range (
float
, optional) -- The standard deviation of the truncated_normal_initializer for initializing all weight matrices. Defaults to0.02
.
-
forward
(input_ids=None, bbox=None, image=None, token_type_ids=None, position_ids=None, attention_mask=None, head_mask=None, output_hidden_states=None, output_attentions=None)[源代码]¶ Defines the computation performed at every call. Should be overridden by all subclasses.
- 参数
*inputs (tuple) -- unpacked tuple arguments
**kwargs (dict) -- unpacked dict arguments
-
class
LayoutLMv2PretrainedModel
(name_scope=None, dtype='float32')[源代码]¶ 基类:
paddlenlp.transformers.model_utils.PretrainedModel
-
base_model_class
¶ alias of
paddlenlp.transformers.layoutlmv2.modeling.LayoutLMv2Model
-
-
class
LayoutLMv2ForTokenClassification
(layoutlmv2, num_classes=2, dropout=None)[源代码]¶ 基类:
paddlenlp.transformers.layoutlmv2.modeling.LayoutLMv2PretrainedModel
-
forward
(input_ids=None, bbox=None, image=None, attention_mask=None, token_type_ids=None, position_ids=None, head_mask=None, labels=None)[源代码]¶ Defines the computation performed at every call. Should be overridden by all subclasses.
- 参数
*inputs (tuple) -- unpacked tuple arguments
**kwargs (dict) -- unpacked dict arguments
-
-
class
LayoutLMv2ForPretraining
(layoutlmv2)[源代码]¶ 基类:
paddlenlp.transformers.layoutlmv2.modeling.LayoutLMv2PretrainedModel
-
forward
(input_ids=None, bbox=None, image=None, attention_mask=None, token_type_ids=None, position_ids=None, head_mask=None, masked_positions=None)[源代码]¶ Defines the computation performed at every call. Should be overridden by all subclasses.
- 参数
*inputs (tuple) -- unpacked tuple arguments
**kwargs (dict) -- unpacked dict arguments
-
-
class
LayoutLMv2ForRelationExtraction
(layoutlmv2, hidden_size=768, hidden_dropout_prob=0.1, dropout=None)[源代码]¶ 基类:
paddlenlp.transformers.layoutlmv2.modeling.LayoutLMv2PretrainedModel
-
forward
(input_ids, bbox, labels=None, image=None, attention_mask=None, token_type_ids=None, position_ids=None, head_mask=None, entities=None, relations=None)[源代码]¶ Defines the computation performed at every call. Should be overridden by all subclasses.
- 参数
*inputs (tuple) -- unpacked tuple arguments
**kwargs (dict) -- unpacked dict arguments
-