modeling

Modeling classes for LayoutLMv2 model.

class LayoutLMv2Model(with_pool='tanh', use_visual_backbone=True, **kwargs)[source]

Bases: 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.

Parameters
  • vocab_size (int) – Vocabulary size of the XLNet model. Defines the number of different tokens that can be represented by the inputs_ids passed when calling XLNetModel.

  • hidden_size (int, optional) – Dimensionality of the encoder layers and the pooler layer. Defaults to 768.

  • num_hidden_layers (int, optional) – Number of hidden layers in the Transformer encoder. Defaults to 12.

  • num_attention_heads (int, optional) – Number of attention heads for each attention layer in the Transformer encoder. Defaults to 12.

  • intermediate_size (int, optional) – Dimensionality of the “intermediate” (often named feed-forward) layer in the Transformer encoder. Defaults to 3072.

  • 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 to 0.1.

  • attention_probs_dropout_prob (float, optional) – The dropout probability for all fully connected layers in the pooler. Defaults to 0.1.

  • initializer_range (float, optional) – The standard deviation of the truncated_normal_initializer for initializing all weight matrices. Defaults to 0.02.

resize_position_embeddings(new_num_position_embeddings)[source]

Resizes position embeddings of the model if new_num_position_embeddings != config["max_position_embeddings"].

Parameters

new_num_position_embeddings (int) – The number of new position embedding matrix. If position embeddings are learned, increasing the size will add newly initialized vectors at the end, whereas reducing the size will remove vectors from the end.

forward(input_ids=None, bbox=None, image=None, token_type_ids=None, position_ids=None, attention_mask=None, head_mask=None, output_hidden_states=False, output_attentions=False)[source]

Defines the computation performed at every call. Should be overridden by all subclasses.

Parameters
  • *inputs (tuple) – unpacked tuple arguments

  • **kwargs (dict) – unpacked dict arguments

class LayoutLMv2PretrainedModel(*args, **kwargs)[source]

Bases: paddlenlp.transformers.model_utils.PretrainedModel

init_weights(layer)[source]

Initialization hook

base_model_class

alias of paddlenlp.transformers.layoutlmv2.modeling.LayoutLMv2Model

class LayoutLMv2ForTokenClassification(layoutlmv2, num_classes=2, dropout=None)[source]

Bases: paddlenlp.transformers.layoutlmv2.modeling.LayoutLMv2PretrainedModel

get_input_embeddings()[source]

get input embedding of model

Returns

embedding of model

Return type

nn.Embedding

resize_position_embeddings(new_num_position_embeddings)[source]

Resizes position embeddings of the model if new_num_position_embeddings != config["max_position_embeddings"].

Parameters

new_num_position_embeddings (int) – The number of new position embedding matrix. If position embeddings are learned, increasing the size will add newly initialized vectors at the end, whereas reducing the size will remove vectors from the end.

forward(input_ids=None, bbox=None, image=None, attention_mask=None, token_type_ids=None, position_ids=None, head_mask=None, labels=None)[source]

Defines the computation performed at every call. Should be overridden by all subclasses.

Parameters
  • *inputs (tuple) – unpacked tuple arguments

  • **kwargs (dict) – unpacked dict arguments

class LayoutLMv2ForPretraining(layoutlmv2)[source]

Bases: paddlenlp.transformers.layoutlmv2.modeling.LayoutLMv2PretrainedModel

resize_position_embeddings(new_num_position_embeddings)[source]

Resizes position embeddings of the model if new_num_position_embeddings != config["max_position_embeddings"].

Parameters

new_num_position_embeddings (int) – The number of new position embedding matrix. If position embeddings are learned, increasing the size will add newly initialized vectors at the end, whereas reducing the size will remove vectors from the end.

forward(input_ids=None, bbox=None, image=None, attention_mask=None, token_type_ids=None, position_ids=None, head_mask=None, masked_positions=None)[source]

Defines the computation performed at every call. Should be overridden by all subclasses.

Parameters
  • *inputs (tuple) – unpacked tuple arguments

  • **kwargs (dict) – unpacked dict arguments

class LayoutLMv2ForRelationExtraction(layoutlmv2, hidden_size=768, hidden_dropout_prob=0.1, dropout=None)[source]

Bases: paddlenlp.transformers.layoutlmv2.modeling.LayoutLMv2PretrainedModel

init_weights(layer)[source]

Initialize the weights

resize_position_embeddings(new_num_position_embeddings)[source]

Resizes position embeddings of the model if new_num_position_embeddings != config["max_position_embeddings"].

Parameters

new_num_position_embeddings (int) – The number of new position embedding matrix. If position embeddings are learned, increasing the size will add newly initialized vectors at the end, whereas reducing the size will remove vectors from the end.

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)[source]

Defines the computation performed at every call. Should be overridden by all subclasses.

Parameters
  • *inputs (tuple) – unpacked tuple arguments

  • **kwargs (dict) – unpacked dict arguments