LayoutLM¶

Overview¶

The LayoutLM model was proposed in LayoutLM: Pre-training of Text and Layout for Document Image Understanding by Yiheng Xu, Minghao Li, Lei Cui, Shaohan Huang, Furu Wei, and Ming Zhou. It’s a simple but effective pre-training method of text and layout for document image understanding and information extraction tasks, such as form understanding and receipt understanding.

The abstract from the paper is the following:

Pre-training techniques have been verified successfully in a variety of NLP tasks in recent years. Despite the widespread use of pre-training models for NLP applications, they almost exclusively focus on text-level manipulation, while neglecting layout and style information that is vital for document image understanding. In this paper, we propose the textbf{LayoutLM} to jointly model interactions between text and layout information across scanned document images, which is beneficial for a great number of real-world document image understanding tasks such as information extraction from scanned documents. Furthermore, we also leverage image features to incorporate words’ visual information into LayoutLM. To the best of our knowledge, this is the first time that text and layout are jointly learned in a single framework for document-level pre-training. It achieves new state-of-the-art results in several downstream tasks, including form understanding (from 70.72 to 79.27), receipt understanding (from 94.02 to 95.24) and document image classification (from 93.07 to 94.42).

Tips:

LayoutLM has an extra input called bbox, which is the bounding boxes of the input tokens.
The bbox requires the data that on 0-1000 scale, which means you should normalize the bounding box before passing them into model.

The original code can be found here.

LayoutLMConfig¶

class transformers.LayoutLMConfig(vocab_size=30522, hidden_size=768, num_hidden_layers=12, num_attention_heads=12, intermediate_size=3072, hidden_act='gelu', hidden_dropout_prob=0.1, attention_probs_dropout_prob=0.1, max_position_embeddings=512, type_vocab_size=2, initializer_range=0.02, layer_norm_eps=1e-12, pad_token_id=0, gradient_checkpointing=False, max_2d_position_embeddings=1024, **kwargs)[source]¶

This is the configuration class to store the configuration of a LayoutLMModel. It is used to instantiate a LayoutLM model according to the specified arguments, defining the model architecture. Instantiating a configuration with the defaults will yield a similar configuration to that of the LayoutLM layoutlm-base-uncased architecture.

Configuration objects inherit from BertConfig and can be used to control the model outputs. Read the documentation from BertConfig for more information.

Parameters

vocab_size (int, optional, defaults to 30522) – Vocabulary size of the LayoutLM model. Defines the different tokens that can be represented by the inputs_ids passed to the forward method of LayoutLMModel.
hidden_size (int, optional, defaults to 768) – Dimensionality of the encoder layers and the pooler layer.
num_hidden_layers (int, optional, defaults to 12) – Number of hidden layers in the Transformer encoder.
num_attention_heads (int, optional, defaults to 12) – Number of attention heads for each attention layer in the Transformer encoder.
intermediate_size (int, optional, defaults to 3072) – Dimensionality of the “intermediate” (i.e., feed-forward) layer in the Transformer encoder.
hidden_act (str or function, optional, defaults to "gelu") – The non-linear activation function (function or string) in the encoder and pooler. If string, "gelu", "relu", "swish" and "gelu_new" are supported.
hidden_dropout_prob (float, optional, defaults to 0.1) – The dropout probabilitiy for all fully connected layers in the embeddings, encoder, and pooler.
attention_probs_dropout_prob (float, optional, defaults to 0.1) – The dropout ratio for the attention probabilities.
max_position_embeddings (int, optional, defaults to 512) – The maximum sequence length that this model might ever be used with. Typically set this to something large just in case (e.g., 512 or 1024 or 2048).
type_vocab_size (int, optional, defaults to 2) – The vocabulary size of the token_type_ids passed into LayoutLMModel.
initializer_range (float, optional, defaults to 0.02) – The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
layer_norm_eps (float, optional, defaults to 1e-12) – The epsilon used by the layer normalization layers.
gradient_checkpointing (bool, optional, defaults to False) – If True, use gradient checkpointing to save memory at the expense of slower backward pass.
max_2d_position_embeddings (int, optional, defaults to 1024) – The maximum value that the 2D position embedding might ever used. Typically set this to something large just in case (e.g., 1024).

Examples:

>>> from transformers import LayoutLMModel, LayoutLMConfig

>>> # Initializing a LayoutLM configuration
>>> configuration = LayoutLMConfig()

>>> # Initializing a model from the configuration
>>> model = LayoutLMModel(configuration)

>>> # Accessing the model configuration
>>> configuration = model.config

LayoutLMTokenizer¶

class transformers.LayoutLMTokenizer(vocab_file, do_lower_case=True, do_basic_tokenize=True, never_split=None, unk_token='[UNK]', sep_token='[SEP]', pad_token='[PAD]', cls_token='[CLS]', mask_token='[MASK]', tokenize_chinese_chars=True, strip_accents=None, **kwargs)[source]¶

Constructs a LayoutLM tokenizer.

BertTokenizer and runs end-to-end tokenization: punctuation splitting + wordpiece.

Refer to superclass BertTokenizer for usage examples and documentation concerning parameters.

LayoutLMModel¶

class transformers.LayoutLMModel(config)[source]¶

The bare LayoutLM Model transformer outputting raw hidden-states without any specific head on top. The LayoutLM model was proposed in LayoutLM: Pre-training of Text and Layout for Document Image Understanding by….

This model is a PyTorch torch.nn.Module sub-class. Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and behavior.

Parameters: config (LayoutLMConfig) – Model configuration class with all the parameters of the model. Initializing with a config file does not load the weights associated with the model, only the configuration. Check out the from_pretrained() method to load the model weights.

config_class¶: alias of transformers.configuration_layoutlm.LayoutLMConfig

forward(input_ids=None, bbox=None, attention_mask=None, token_type_ids=None, position_ids=None, head_mask=None, inputs_embeds=None, encoder_hidden_states=None, encoder_attention_mask=None, output_attentions=None, output_hidden_states=None, return_dict=None)[source]¶

The LayoutLMModel forward method, overrides the __call__() special method.

Note

Although the recipe for forward pass needs to be defined within this function, one should call the Module instance afterwards instead of this since the former takes care of running the pre and post processing steps while the latter silently ignores them.

Inputs:

input_ids (torch.LongTensor of shape (batch_size, sequence_length)):

Indices of input sequence tokens in the vocabulary.

Indices can be obtained using transformers.LayoutLMTokenizer. See transformers.PreTrainedTokenizer.encode() and transformers.PreTrainedTokenizer.__call__() for details.

What are input IDs?

bbox (torch.LongTensor of shape (batch_size, sequence_length), optional):

Bounding Boxes of each input sequence tokens. Selected in the range [0, config.max_2d_position_embeddings - 1].

What are bboxes?

attention_mask (torch.FloatTensor of shape (batch_size, sequence_length), optional):

Mask to avoid performing attention on padding token indices. Mask values selected in [0, 1]: 1 for tokens that are NOT MASKED, 0 for MASKED tokens.

What are attention masks?

token_type_ids (torch.LongTensor of shape (batch_size, sequence_length), optional):

Segment token indices to indicate first and second portions of the inputs. Indices are selected in [0, 1]: 0 corresponds to a sentence A token, 1 corresponds to a sentence B token

What are token type IDs?

position_ids (torch.LongTensor of shape (batch_size, sequence_length), optional):

Indices of positions of each input sequence tokens in the position embeddings. Selected in the range [0, config.max_position_embeddings - 1].

What are position IDs?

head_mask (torch.FloatTensor of shape (num_heads,) or (num_layers, num_heads), optional):

Mask to nullify selected heads of the self-attention modules. Mask values selected in [0, 1]: 1 indicates the head is not masked, 0 indicates the head is masked.

inputs_embeds (torch.FloatTensor of shape (batch_size, sequence_length, hidden_size), optional):

Optionally, instead of passing input_ids you can choose to directly pass an embedded representation. This is useful if you want more control over how to convert input_ids indices into associated vectors than the model’s internal embedding lookup matrix.

output_attentions (bool, optional):

If set to True, the attentions tensors of all attention layers are returned. See attentions under returned tensors for more detail.

output_hidden_states (bool, optional):

If set to True, the hidden states of all layers are returned. See hidden_states under returned tensors for more detail.

return_dict (bool, optional):

If set to True, the model will return a ModelOutput instead of a plain tuple.

input_ids (torch.LongTensor of shape (batch_size, sequence_length)):

Indices of input sequence tokens in the vocabulary.

attention_mask (torch.FloatTensor of shape (batch_size, sequence_length), optional):

Mask to avoid performing attention on padding token indices. Mask values selected in [0, 1]: 1 for tokens that are NOT MASKED, 0 for MASKED tokens.

token_type_ids (torch.LongTensor of shape (batch_size, sequence_length), optional):

Segment token indices to indicate first and second portions of the inputs. Indices are selected in [0, 1]: 0 corresponds to a sentence A token, 1 corresponds to a sentence B token

position_ids (torch.LongTensor of shape (batch_size, sequence_length), optional):

Indices of positions of each input sequence tokens in the position embeddings. Selected in the range [0, config.max_position_embeddings - 1].

head_mask (torch.FloatTensor of shape (num_heads,) or (num_layers, num_heads), optional):

Mask to nullify selected heads of the self-attention modules. Mask values selected in [0, 1]: 1 indicates the head is not masked, 0 indicates the head is masked.

inputs_embeds (torch.FloatTensor of shape (batch_size, sequence_length, hidden_size), optional):

Optionally, instead of passing input_ids you can choose to directly pass an embedded representation. This is useful if you want more control over how to convert input_ids indices into associated vectors than the model’s internal embedding lookup matrix.

output_attentions (bool, optional):

If set to True, the attentions tensors of all attention layers are returned.

output_hidden_states (bool, optional):

If set to True, the hidden states of all layers are returned.

return_dict (bool, optional):

If set to True, the model will return a ModelOutput instead of a plain tuple.

Returns

A BaseModelOutputWithPooling (if return_dict=True is passed or when config.return_dict=True) or a tuple of torch.FloatTensor comprising various elements depending on the configuration (LayoutLMConfig) and inputs.

last_hidden_state (torch.FloatTensor of shape (batch_size, sequence_length, hidden_size)) – Sequence of hidden-states at the output of the last layer of the model.
pooler_output (torch.FloatTensor of shape (batch_size, hidden_size)) – Last layer hidden-state of the first token of the sequence (classification token) further processed by a Linear layer and a Tanh activation function. The Linear layer weights are trained from the next sentence prediction (classification) objective during pretraining.
hidden_states (tuple(torch.FloatTensor), optional, returned when output_hidden_states=True is passed or when config.output_hidden_states=True) – Tuple of torch.FloatTensor (one for the output of the embeddings + one for the output of each layer) of shape (batch_size, sequence_length, hidden_size).

Hidden-states of the model at the output of each layer plus the initial embedding outputs.
attentions (tuple(torch.FloatTensor), optional, returned when output_attentions=True is passed or when config.output_attentions=True) – Tuple of torch.FloatTensor (one for each layer) of shape (batch_size, num_heads, sequence_length, sequence_length).

Attentions weights after the attention softmax, used to compute the weighted average in the self-attention heads.

Return type

BaseModelOutputWithPooling or tuple(torch.FloatTensor)

Example:

>>> from transformers import LayoutLMTokenizer, LayoutLMModel
>>> import torch

>>> tokenizer = LayoutLMTokenizer.from_pretrained('layoutlm-base-uncased')
>>> model = LayoutLMModel.from_pretrained('layoutlm-base-uncased', return_dict=True)

>>> inputs = tokenizer("Hello, my dog is cute", return_tensors="pt")
>>> outputs = model(**inputs)

>>> last_hidden_states = outputs.last_hidden_state

get_input_embeddings()[source]¶

Returns the model’s input embeddings.

Returns: A torch module mapping vocabulary to hidden states.
Return type: nn.Module

set_input_embeddings(value)[source]¶

Set model’s input embeddings

Parameters: value (nn.Module) – A module mapping vocabulary to hidden states.

LayoutLMForMaskedLM¶

class transformers.LayoutLMForMaskedLM(config)[source]¶

LayoutLM Model with a language modeling head on top. The LayoutLM model was proposed in LayoutLM: Pre-training of Text and Layout for Document Image Understanding by….

This model is a PyTorch torch.nn.Module sub-class. Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and behavior.

Parameters: config (LayoutLMConfig) – Model configuration class with all the parameters of the model. Initializing with a config file does not load the weights associated with the model, only the configuration. Check out the from_pretrained() method to load the model weights.

config_class¶: alias of transformers.configuration_layoutlm.LayoutLMConfig

forward(input_ids=None, bbox=None, attention_mask=None, token_type_ids=None, position_ids=None, head_mask=None, inputs_embeds=None, labels=None, encoder_hidden_states=None, encoder_attention_mask=None, output_attentions=None, output_hidden_states=None, return_dict=None)[source]¶

The LayoutLMForMaskedLM forward method, overrides the __call__() special method.

Note

Although the recipe for forward pass needs to be defined within this function, one should call the Module instance afterwards instead of this since the former takes care of running the pre and post processing steps while the latter silently ignores them.

Inputs:

input_ids (torch.LongTensor of shape (batch_size, sequence_length)):

Indices of input sequence tokens in the vocabulary.

Indices can be obtained using transformers.LayoutLMTokenizer. See transformers.PreTrainedTokenizer.encode() and transformers.PreTrainedTokenizer.__call__() for details.

What are input IDs?

bbox (torch.LongTensor of shape (batch_size, sequence_length), optional):

Bounding Boxes of each input sequence tokens. Selected in the range [0, config.max_2d_position_embeddings - 1].

What are bboxes?

attention_mask (torch.FloatTensor of shape (batch_size, sequence_length), optional):

Mask to avoid performing attention on padding token indices. Mask values selected in [0, 1]: 1 for tokens that are NOT MASKED, 0 for MASKED tokens.

What are attention masks?

token_type_ids (torch.LongTensor of shape (batch_size, sequence_length), optional):

Segment token indices to indicate first and second portions of the inputs. Indices are selected in [0, 1]: 0 corresponds to a sentence A token, 1 corresponds to a sentence B token

What are token type IDs?

position_ids (torch.LongTensor of shape (batch_size, sequence_length), optional):

Indices of positions of each input sequence tokens in the position embeddings. Selected in the range [0, config.max_position_embeddings - 1].

What are position IDs?

head_mask (torch.FloatTensor of shape (num_heads,) or (num_layers, num_heads), optional):

Mask to nullify selected heads of the self-attention modules. Mask values selected in [0, 1]: 1 indicates the head is not masked, 0 indicates the head is masked.

inputs_embeds (torch.FloatTensor of shape (batch_size, sequence_length, hidden_size), optional):

Optionally, instead of passing input_ids you can choose to directly pass an embedded representation. This is useful if you want more control over how to convert input_ids indices into associated vectors than the model’s internal embedding lookup matrix.

output_attentions (bool, optional):

If set to True, the attentions tensors of all attention layers are returned. See attentions under returned tensors for more detail.

output_hidden_states (bool, optional):

If set to True, the hidden states of all layers are returned. See hidden_states under returned tensors for more detail.

return_dict (bool, optional):

If set to True, the model will return a ModelOutput instead of a plain tuple.

Returns

A MaskedLMOutput (if return_dict=True is passed or when config.return_dict=True) or a tuple of torch.FloatTensor comprising various elements depending on the configuration (LayoutLMConfig) and inputs.

loss (torch.FloatTensor of shape (1,), optional, returned when labels is provided) – Masked languaged modeling (MLM) loss.
logits (torch.FloatTensor of shape (batch_size, sequence_length, config.vocab_size)) – Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax).
hidden_states (tuple(torch.FloatTensor), optional, returned when output_hidden_states=True is passed or when config.output_hidden_states=True) – Tuple of torch.FloatTensor (one for the output of the embeddings + one for the output of each layer) of shape (batch_size, sequence_length, hidden_size).

Hidden-states of the model at the output of each layer plus the initial embedding outputs.
attentions (tuple(torch.FloatTensor), optional, returned when output_attentions=True is passed or when config.output_attentions=True) – Tuple of torch.FloatTensor (one for each layer) of shape (batch_size, num_heads, sequence_length, sequence_length).

Attentions weights after the attention softmax, used to compute the weighted average in the self-attention heads.

Return type

MaskedLMOutput or tuple(torch.FloatTensor)

Example:

>>> from transformers import LayoutLMTokenizer, LayoutLMForMaskedLM
>>> import torch

>>> tokenizer = LayoutLMTokenizer.from_pretrained('layoutlm-base-uncased')
>>> model = LayoutLMForMaskedLM.from_pretrained('layoutlm-base-uncased', return_dict=True)

>>> input_ids = tokenizer("Hello, my dog is cute", return_tensors="pt")["input_ids"]

>>> outputs = model(input_ids, labels=input_ids)
>>> loss = outputs.loss
>>> prediction_logits = outputs.logits

get_input_embeddings()[source]¶

Returns the model’s input embeddings.

Returns: A torch module mapping vocabulary to hidden states.
Return type: nn.Module

get_output_embeddings()[source]¶

Returns the model’s output embeddings.

Returns: A torch module mapping hidden states to vocabulary.
Return type: nn.Module

LayoutLMForTokenClassification¶

class transformers.LayoutLMForTokenClassification(config)[source]¶

LayoutLM Model with a token classification head on top (a linear layer on top of the hidden-states output) e.g. for Named-Entity-Recognition (NER) tasks. The LayoutLM model was proposed in LayoutLM: Pre-training of Text and Layout for Document Image Understanding by….

This model is a PyTorch torch.nn.Module sub-class. Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and behavior.

Parameters: config (LayoutLMConfig) – Model configuration class with all the parameters of the model. Initializing with a config file does not load the weights associated with the model, only the configuration. Check out the from_pretrained() method to load the model weights.

config_class¶: alias of transformers.configuration_layoutlm.LayoutLMConfig

forward(input_ids=None, bbox=None, attention_mask=None, token_type_ids=None, position_ids=None, head_mask=None, inputs_embeds=None, labels=None, output_attentions=None, output_hidden_states=None, return_dict=None)[source]¶

The LayoutLMForTokenClassification forward method, overrides the __call__() special method.

Note

Although the recipe for forward pass needs to be defined within this function, one should call the Module instance afterwards instead of this since the former takes care of running the pre and post processing steps while the latter silently ignores them.

Inputs:

input_ids (torch.LongTensor of shape (batch_size, sequence_length)):

Indices of input sequence tokens in the vocabulary.

Indices can be obtained using transformers.LayoutLMTokenizer. See transformers.PreTrainedTokenizer.encode() and transformers.PreTrainedTokenizer.__call__() for details.

What are input IDs?

bbox (torch.LongTensor of shape (batch_size, sequence_length), optional):

Bounding Boxes of each input sequence tokens. Selected in the range [0, config.max_2d_position_embeddings - 1].

What are bboxes?

attention_mask (torch.FloatTensor of shape (batch_size, sequence_length), optional):

Mask to avoid performing attention on padding token indices. Mask values selected in [0, 1]: 1 for tokens that are NOT MASKED, 0 for MASKED tokens.

What are attention masks?

token_type_ids (torch.LongTensor of shape (batch_size, sequence_length), optional):

Segment token indices to indicate first and second portions of the inputs. Indices are selected in [0, 1]: 0 corresponds to a sentence A token, 1 corresponds to a sentence B token

What are token type IDs?

position_ids (torch.LongTensor of shape (batch_size, sequence_length), optional):

Indices of positions of each input sequence tokens in the position embeddings. Selected in the range [0, config.max_position_embeddings - 1].

What are position IDs?

head_mask (torch.FloatTensor of shape (num_heads,) or (num_layers, num_heads), optional):

Mask to nullify selected heads of the self-attention modules. Mask values selected in [0, 1]: 1 indicates the head is not masked, 0 indicates the head is masked.

inputs_embeds (torch.FloatTensor of shape (batch_size, sequence_length, hidden_size), optional):

Optionally, instead of passing input_ids you can choose to directly pass an embedded representation. This is useful if you want more control over how to convert input_ids indices into associated vectors than the model’s internal embedding lookup matrix.

output_attentions (bool, optional):

If set to True, the attentions tensors of all attention layers are returned. See attentions under returned tensors for more detail.

output_hidden_states (bool, optional):

If set to True, the hidden states of all layers are returned. See hidden_states under returned tensors for more detail.

return_dict (bool, optional):

If set to True, the model will return a ModelOutput instead of a plain tuple.

Returns

A TokenClassifierOutput (if return_dict=True is passed or when config.return_dict=True) or a tuple of torch.FloatTensor comprising various elements depending on the configuration (LayoutLMConfig) and inputs.

loss (torch.FloatTensor of shape (1,), optional, returned when labels is provided) – Classification loss.
logits (torch.FloatTensor of shape (batch_size, sequence_length, config.num_labels)) – Classification scores (before SoftMax).
hidden_states (tuple(torch.FloatTensor), optional, returned when output_hidden_states=True is passed or when config.output_hidden_states=True) – Tuple of torch.FloatTensor (one for the output of the embeddings + one for the output of each layer) of shape (batch_size, sequence_length, hidden_size).

Hidden-states of the model at the output of each layer plus the initial embedding outputs.
attentions (tuple(torch.FloatTensor), optional, returned when output_attentions=True is passed or when config.output_attentions=True) – Tuple of torch.FloatTensor (one for each layer) of shape (batch_size, num_heads, sequence_length, sequence_length).

Attentions weights after the attention softmax, used to compute the weighted average in the self-attention heads.

Return type

TokenClassifierOutput or tuple(torch.FloatTensor)

Example:

>>> from transformers import LayoutLMTokenizer, LayoutLMForTokenClassification
>>> import torch

>>> tokenizer = LayoutLMTokenizer.from_pretrained('layoutlm-base-uncased')
>>> model = LayoutLMForTokenClassification.from_pretrained('layoutlm-base-uncased', return_dict=True)

>>> inputs = tokenizer("Hello, my dog is cute", return_tensors="pt")
>>> labels = torch.tensor([1] * inputs["input_ids"].size(1)).unsqueeze(0)  # Batch size 1

>>> outputs = model(**inputs, labels=labels)
>>> loss = outputs.loss
>>> logits = outputs.logits

get_input_embeddings()[source]¶

Returns the model’s input embeddings.

Returns: A torch module mapping vocabulary to hidden states.
Return type: nn.Module