LayoutLM
Overview
The LayoutLM model was proposed in the paper 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. Itβs a simple but effective pretraining method of text and layout for document image understanding and information extraction tasks, such as form understanding and receipt understanding. It obtains state-of-the-art results on several downstream tasks:
- form understanding: the FUNSD dataset (a collection of 199 annotated forms comprising more than 30,000 words).
- receipt understanding: the SROIE dataset (a collection of 626 receipts for training and 347 receipts for testing).
- document image classification: the RVL-CDIP dataset (a collection of 400,000 images belonging to one of 16 classes).
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 pretraining 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 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 wordsβ 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 pretraining. 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:
- In addition to input_ids, forward() also expects the input
bbox
, which are the bounding boxes (i.e. 2D-positions) of the input tokens. These can be obtained using an external OCR engine such as Googleβs Tesseract (thereβs a Python wrapper available). Each bounding box should be in (x0, y0, x1, y1) format, where (x0, y0) corresponds to the position of the upper left corner in the bounding box, and (x1, y1) represents the position of the lower right corner. Note that one first needs to normalize the bounding boxes to be on a 0-1000 scale. To normalize, you can use the following function:
def normalize_bbox(bbox, width, height):
return [
int(1000 * (bbox[0] / width)),
int(1000 * (bbox[1] / height)),
int(1000 * (bbox[2] / width)),
int(1000 * (bbox[3] / height)),
]
Here, width
and height
correspond to the width and height of the original document in which the token
occurs. Those can be obtained using the Python Image Library (PIL) library for example, as follows:
from PIL import Image
image = Image.open("name_of_your_document - can be a png file, pdf, etc.")
width, height = image.size
- For a demo which shows how to fine-tune LayoutLMForTokenClassification on the FUNSD dataset (a collection of annotated forms), see this notebook. It includes an inference part, which shows how to use Googleβs Tesseract on a new document.
This model was contributed by liminghao1630. The original code can be found here.
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 max_2d_position_embeddings = 1024 **kwargs )
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
orfunction
, optional, defaults to"gelu"
) — The non-linear activation function (function or string) in the encoder and pooler. If string,"gelu"
,"relu"
,"silu"
and"gelu_new"
are supported. - hidden_dropout_prob (
float
, optional, defaults to 0.1) — The dropout probability 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 thetoken_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. -
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).
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.
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
( 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 )
Constructs a LayoutLM tokenizer.
LayoutLMTokenizer is identical to BertTokenizer and runs end-to-end tokenization: punctuation splitting + wordpiece.
Refer to superclass BertTokenizer for usage examples and documentation concerning parameters.
LayoutLMTokenizerFast
( vocab_file = None tokenizer_file = None do_lower_case = True unk_token = '[UNK]' sep_token = '[SEP]' pad_token = '[PAD]' cls_token = '[CLS]' mask_token = '[MASK]' tokenize_chinese_chars = True strip_accents = None **kwargs )
Constructs a βFastβ LayoutLMTokenizer.
LayoutLMTokenizerFast is identical to BertTokenizerFast and runs end-to-end tokenization: punctuation splitting + wordpiece.
Refer to superclass BertTokenizerFast for usage examples and documentation concerning parameters.
LayoutLMModel
( config )
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.
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 Yiheng Xu, Minghao Li, Lei Cui, Shaohan Huang, Furu Wei and Ming Zhou.
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.
(
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
)
β
transformers.modeling_outputs.BaseModelOutputWithPoolingAndCrossAttentions or tuple(torch.FloatTensor)
Parameters
-
input_ids (
torch.LongTensor
of shape(batch_size, sequence_length)
) — Indices of input sequence tokens in the vocabulary.Indices can be obtained using LayoutLMTokenizer. See PreTrainedTokenizer.encode() and PreTrainedTokenizer.call() for details.
-
bbox (
torch.LongTensor
of shape(batch_size, sequence_length, 4)
, optional) — Bounding boxes of each input sequence tokens. Selected in the range[0, config.max_2d_position_embeddings-1]
. Each bounding box should be a normalized version in (x0, y0, x1, y1) format, where (x0, y0) corresponds to the position of the upper left corner in the bounding box, and (x1, y1) represents the position of the lower right corner. See Overview for normalization. -
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 passinginput_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 toTrue
, the attentions tensors of all attention layers are returned. Seeattentions
under returned tensors for more detail. - output_hidden_states (
bool
, optional) — If set toTrue
, the hidden states of all layers are returned. Seehidden_states
under returned tensors for more detail. -
return_dict (
bool
, optional) — If set toTrue
, the model will return a ModelOutput instead of a plain tuple.
Returns
transformers.modeling_outputs.BaseModelOutputWithPoolingAndCrossAttentions or tuple(torch.FloatTensor)
A transformers.modeling_outputs.BaseModelOutputWithPoolingAndCrossAttentions or a tuple of
torch.FloatTensor
(if return_dict=False
is passed or when config.return_dict=False
) comprising various
elements depending on the configuration (LayoutLMConfig) and inputs.
-
last_hidden_state (
torch.FloatTensor
of shape(batch_size, sequence_length, hidden_size)
) β Sequence of hidden-states at the output of the last layer of the model. -
pooler_output (
torch.FloatTensor
of shape(batch_size, hidden_size)
) β Last layer hidden-state of the first token of the sequence (classification token) after further processing through the layers used for the auxiliary pretraining task. E.g. for BERT-family of models, this returns the classification token after processing through 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 whenoutput_hidden_states=True
is passed or whenconfig.output_hidden_states=True
) β Tuple oftorch.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 whenoutput_attentions=True
is passed or whenconfig.output_attentions=True
) β Tuple oftorch.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.
-
cross_attentions (
tuple(torch.FloatTensor)
, optional, returned whenoutput_attentions=True
andconfig.add_cross_attention=True
is passed or whenconfig.output_attentions=True
) β Tuple oftorch.FloatTensor
(one for each layer) of shape(batch_size, num_heads, sequence_length, sequence_length)
.Attentions weights of the decoderβs cross-attention layer, after the attention softmax, used to compute the weighted average in the cross-attention heads.
-
past_key_values (
tuple(tuple(torch.FloatTensor))
, optional, returned whenuse_cache=True
is passed or whenconfig.use_cache=True
) β Tuple oftuple(torch.FloatTensor)
of lengthconfig.n_layers
, with each tuple having 2 tensors of shape(batch_size, num_heads, sequence_length, embed_size_per_head)
) and optionally ifconfig.is_encoder_decoder=True
2 additional tensors of shape(batch_size, num_heads, encoder_sequence_length, embed_size_per_head)
.Contains pre-computed hidden-states (key and values in the self-attention blocks and optionally if
config.is_encoder_decoder=True
in the cross-attention blocks) that can be used (seepast_key_values
input) to speed up sequential decoding.
The LayoutLMModel forward method, overrides the __call__
special method.
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.
Examples:
>>> from transformers import LayoutLMTokenizer, LayoutLMModel
>>> import torch
>>> tokenizer = LayoutLMTokenizer.from_pretrained('microsoft/layoutlm-base-uncased')
>>> model = LayoutLMModel.from_pretrained('microsoft/layoutlm-base-uncased')
>>> words = ["Hello", "world"]
>>> normalized_word_boxes = [637, 773, 693, 782], [698, 773, 733, 782]
>>> token_boxes = []
>>> for word, box in zip(words, normalized_word_boxes):
... word_tokens = tokenizer.tokenize(word)
... token_boxes.extend([box] * len(word_tokens))
>>> # add bounding boxes of cls + sep tokens
>>> token_boxes = [[0, 0, 0, 0]] + token_boxes + [[1000, 1000, 1000, 1000]]
>>> encoding = tokenizer(' '.join(words), return_tensors="pt")
>>> input_ids = encoding["input_ids"]
>>> attention_mask = encoding["attention_mask"]
>>> token_type_ids = encoding["token_type_ids"]
>>> bbox = torch.tensor([token_boxes])
>>> outputs = model(input_ids=input_ids, bbox=bbox, attention_mask=attention_mask, token_type_ids=token_type_ids)
>>> last_hidden_states = outputs.last_hidden_state
LayoutLMForMaskedLM
( config )
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.
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 Yiheng Xu, Minghao Li, Lei Cui, Shaohan Huang, Furu Wei and Ming Zhou.
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.
(
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
)
β
transformers.modeling_outputs.MaskedLMOutput or tuple(torch.FloatTensor)
Parameters
-
input_ids (
torch.LongTensor
of shape(batch_size, sequence_length)
) — Indices of input sequence tokens in the vocabulary.Indices can be obtained using LayoutLMTokenizer. See PreTrainedTokenizer.encode() and PreTrainedTokenizer.call() for details.
-
bbox (
torch.LongTensor
of shape(batch_size, sequence_length, 4)
, optional) — Bounding boxes of each input sequence tokens. Selected in the range[0, config.max_2d_position_embeddings-1]
. Each bounding box should be a normalized version in (x0, y0, x1, y1) format, where (x0, y0) corresponds to the position of the upper left corner in the bounding box, and (x1, y1) represents the position of the lower right corner. See Overview for normalization. -
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 passinginput_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 toTrue
, the attentions tensors of all attention layers are returned. Seeattentions
under returned tensors for more detail. - output_hidden_states (
bool
, optional) — If set toTrue
, the hidden states of all layers are returned. Seehidden_states
under returned tensors for more detail. -
return_dict (
bool
, optional) — If set toTrue
, the model will return a ModelOutput instead of a plain tuple. -
labels (
torch.LongTensor
of shape(batch_size, sequence_length)
, optional) — Labels for computing the masked language modeling loss. Indices should be in[-100, 0, ..., config.vocab_size]
(seeinput_ids
docstring) Tokens with indices set to-100
are ignored (masked), the loss is only computed for the tokens with labels in[0, ..., config.vocab_size]
Returns
transformers.modeling_outputs.MaskedLMOutput or tuple(torch.FloatTensor)
A transformers.modeling_outputs.MaskedLMOutput or a tuple of
torch.FloatTensor
(if return_dict=False
is passed or when config.return_dict=False
) comprising various
elements depending on the configuration (LayoutLMConfig) and inputs.
-
loss (
torch.FloatTensor
of shape(1,)
, optional, returned whenlabels
is provided) β Masked language modeling (MLM) loss. -
logits (
torch.FloatTensor
of shape(batch_size, sequence_length, config.vocab_size)
) β Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax). -
hidden_states (
tuple(torch.FloatTensor)
, optional, returned whenoutput_hidden_states=True
is passed or whenconfig.output_hidden_states=True
) β Tuple oftorch.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 whenoutput_attentions=True
is passed or whenconfig.output_attentions=True
) β Tuple oftorch.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.
The LayoutLMForMaskedLM forward method, overrides the __call__
special method.
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.
Examples:
>>> from transformers import LayoutLMTokenizer, LayoutLMForMaskedLM
>>> import torch
>>> tokenizer = LayoutLMTokenizer.from_pretrained('microsoft/layoutlm-base-uncased')
>>> model = LayoutLMForMaskedLM.from_pretrained('microsoft/layoutlm-base-uncased')
>>> words = ["Hello", "[MASK]"]
>>> normalized_word_boxes = [637, 773, 693, 782], [698, 773, 733, 782]
>>> token_boxes = []
>>> for word, box in zip(words, normalized_word_boxes):
... word_tokens = tokenizer.tokenize(word)
... token_boxes.extend([box] * len(word_tokens))
>>> # add bounding boxes of cls + sep tokens
>>> token_boxes = [[0, 0, 0, 0]] + token_boxes + [[1000, 1000, 1000, 1000]]
>>> encoding = tokenizer(' '.join(words), return_tensors="pt")
>>> input_ids = encoding["input_ids"]
>>> attention_mask = encoding["attention_mask"]
>>> token_type_ids = encoding["token_type_ids"]
>>> bbox = torch.tensor([token_boxes])
>>> labels = tokenizer("Hello world", return_tensors="pt")["input_ids"]
>>> outputs = model(input_ids=input_ids, bbox=bbox, attention_mask=attention_mask, token_type_ids=token_type_ids,
... labels=labels)
>>> loss = outputs.loss
LayoutLMForSequenceClassification
( config )
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.
LayoutLM Model with a sequence classification head on top (a linear layer on top of the pooled output) e.g. for
document image classification tasks such as the RVL-CDIP <https://www.cs.cmu.edu/~aharley/rvl-cdip/>
__ dataset.
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.
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.
(
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
)
β
transformers.modeling_outputs.SequenceClassifierOutput or tuple(torch.FloatTensor)
Parameters
-
input_ids (
torch.LongTensor
of shape(batch_size, sequence_length)
) — Indices of input sequence tokens in the vocabulary.Indices can be obtained using LayoutLMTokenizer. See PreTrainedTokenizer.encode() and PreTrainedTokenizer.call() for details.
-
bbox (
torch.LongTensor
of shape(batch_size, sequence_length, 4)
, optional) — Bounding boxes of each input sequence tokens. Selected in the range[0, config.max_2d_position_embeddings-1]
. Each bounding box should be a normalized version in (x0, y0, x1, y1) format, where (x0, y0) corresponds to the position of the upper left corner in the bounding box, and (x1, y1) represents the position of the lower right corner. See Overview for normalization. -
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 passinginput_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 toTrue
, the attentions tensors of all attention layers are returned. Seeattentions
under returned tensors for more detail. - output_hidden_states (
bool
, optional) — If set toTrue
, the hidden states of all layers are returned. Seehidden_states
under returned tensors for more detail. -
return_dict (
bool
, optional) — If set toTrue
, the model will return a ModelOutput instead of a plain tuple. -
labels (
torch.LongTensor
of shape(batch_size,)
, optional) — Labels for computing the sequence classification/regression loss. Indices should be in[0, ..., config.num_labels - 1]
. Ifconfig.num_labels == 1
a regression loss is computed (Mean-Square loss), Ifconfig.num_labels > 1
a classification loss is computed (Cross-Entropy).
Returns
transformers.modeling_outputs.SequenceClassifierOutput or tuple(torch.FloatTensor)
A transformers.modeling_outputs.SequenceClassifierOutput or a tuple of
torch.FloatTensor
(if return_dict=False
is passed or when config.return_dict=False
) comprising various
elements depending on the configuration (LayoutLMConfig) and inputs.
-
loss (
torch.FloatTensor
of shape(1,)
, optional, returned whenlabels
is provided) β Classification (or regression if config.num_labels==1) loss. -
logits (
torch.FloatTensor
of shape(batch_size, config.num_labels)
) β Classification (or regression if config.num_labels==1) scores (before SoftMax). -
hidden_states (
tuple(torch.FloatTensor)
, optional, returned whenoutput_hidden_states=True
is passed or whenconfig.output_hidden_states=True
) β Tuple oftorch.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 whenoutput_attentions=True
is passed or whenconfig.output_attentions=True
) β Tuple oftorch.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.
The LayoutLMForSequenceClassification forward method, overrides the __call__
special method.
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.
Examples:
>>> from transformers import LayoutLMTokenizer, LayoutLMForSequenceClassification
>>> import torch
>>> tokenizer = LayoutLMTokenizer.from_pretrained('microsoft/layoutlm-base-uncased')
>>> model = LayoutLMForSequenceClassification.from_pretrained('microsoft/layoutlm-base-uncased')
>>> words = ["Hello", "world"]
>>> normalized_word_boxes = [637, 773, 693, 782], [698, 773, 733, 782]
>>> token_boxes = []
>>> for word, box in zip(words, normalized_word_boxes):
... word_tokens = tokenizer.tokenize(word)
... token_boxes.extend([box] * len(word_tokens))
>>> # add bounding boxes of cls + sep tokens
>>> token_boxes = [[0, 0, 0, 0]] + token_boxes + [[1000, 1000, 1000, 1000]]
>>> encoding = tokenizer(' '.join(words), return_tensors="pt")
>>> input_ids = encoding["input_ids"]
>>> attention_mask = encoding["attention_mask"]
>>> token_type_ids = encoding["token_type_ids"]
>>> bbox = torch.tensor([token_boxes])
>>> sequence_label = torch.tensor([1])
>>> outputs = model(input_ids=input_ids, bbox=bbox, attention_mask=attention_mask, token_type_ids=token_type_ids,
... labels=sequence_label)
>>> loss = outputs.loss
>>> logits = outputs.logits
LayoutLMForTokenClassification
( config )
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.
LayoutLM Model with a token classification head on top (a linear layer on top of the hidden-states output) e.g. for
sequence labeling (information extraction) tasks such as the FUNSD <https://guillaumejaume.github.io/FUNSD/>
dataset and the SROIE <https://rrc.cvc.uab.es/?ch=13>
dataset.
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.
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.
(
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
)
β
transformers.modeling_outputs.TokenClassifierOutput or tuple(torch.FloatTensor)
Parameters
-
input_ids (
torch.LongTensor
of shape(batch_size, sequence_length)
) — Indices of input sequence tokens in the vocabulary.Indices can be obtained using LayoutLMTokenizer. See PreTrainedTokenizer.encode() and PreTrainedTokenizer.call() for details.
-
bbox (
torch.LongTensor
of shape(batch_size, sequence_length, 4)
, optional) — Bounding boxes of each input sequence tokens. Selected in the range[0, config.max_2d_position_embeddings-1]
. Each bounding box should be a normalized version in (x0, y0, x1, y1) format, where (x0, y0) corresponds to the position of the upper left corner in the bounding box, and (x1, y1) represents the position of the lower right corner. See Overview for normalization. -
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 passinginput_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 toTrue
, the attentions tensors of all attention layers are returned. Seeattentions
under returned tensors for more detail. - output_hidden_states (
bool
, optional) — If set toTrue
, the hidden states of all layers are returned. Seehidden_states
under returned tensors for more detail. -
return_dict (
bool
, optional) — If set toTrue
, the model will return a ModelOutput instead of a plain tuple. -
labels (
torch.LongTensor
of shape(batch_size, sequence_length)
, optional) — Labels for computing the token classification loss. Indices should be in[0, ..., config.num_labels - 1]
.
Returns
transformers.modeling_outputs.TokenClassifierOutput or tuple(torch.FloatTensor)
A transformers.modeling_outputs.TokenClassifierOutput or a tuple of
torch.FloatTensor
(if return_dict=False
is passed or when config.return_dict=False
) comprising various
elements depending on the configuration (LayoutLMConfig) and inputs.
-
loss (
torch.FloatTensor
of shape(1,)
, optional, returned whenlabels
is provided) β Classification loss. -
logits (
torch.FloatTensor
of shape(batch_size, sequence_length, config.num_labels)
) β Classification scores (before SoftMax). -
hidden_states (
tuple(torch.FloatTensor)
, optional, returned whenoutput_hidden_states=True
is passed or whenconfig.output_hidden_states=True
) β Tuple oftorch.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 whenoutput_attentions=True
is passed or whenconfig.output_attentions=True
) β Tuple oftorch.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.
The LayoutLMForTokenClassification forward method, overrides the __call__
special method.
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.
Examples:
>>> from transformers import LayoutLMTokenizer, LayoutLMForTokenClassification
>>> import torch
>>> tokenizer = LayoutLMTokenizer.from_pretrained('microsoft/layoutlm-base-uncased')
>>> model = LayoutLMForTokenClassification.from_pretrained('microsoft/layoutlm-base-uncased')
>>> words = ["Hello", "world"]
>>> normalized_word_boxes = [637, 773, 693, 782], [698, 773, 733, 782]
>>> token_boxes = []
>>> for word, box in zip(words, normalized_word_boxes):
... word_tokens = tokenizer.tokenize(word)
... token_boxes.extend([box] * len(word_tokens))
>>> # add bounding boxes of cls + sep tokens
>>> token_boxes = [[0, 0, 0, 0]] + token_boxes + [[1000, 1000, 1000, 1000]]
>>> encoding = tokenizer(' '.join(words), return_tensors="pt")
>>> input_ids = encoding["input_ids"]
>>> attention_mask = encoding["attention_mask"]
>>> token_type_ids = encoding["token_type_ids"]
>>> bbox = torch.tensor([token_boxes])
>>> token_labels = torch.tensor([1,1,0,0]).unsqueeze(0) # batch size of 1
>>> outputs = model(input_ids=input_ids, bbox=bbox, attention_mask=attention_mask, token_type_ids=token_type_ids,
... labels=token_labels)
>>> loss = outputs.loss
>>> logits = outputs.logits
TFLayoutLMModel
( *args **kwargs )
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.
The bare LayoutLM Model transformer outputting raw hidden-states without any specific head on top.
This model inherits from TFPreTrainedModel. Check the superclass documentation for the generic methods the library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads etc.)
This model is also a tf.keras.Model subclass. Use it as a regular TF 2.0 Keras Model and refer to the TF 2.0 documentation for all matter related to general usage and behavior.
TF 2.0 models accepts two formats as inputs:
- having all inputs as keyword arguments (like PyTorch models), or
- having all inputs as a list, tuple or dict in the first positional arguments.
This second option is useful when using tf.keras.Model.fit
method which currently requires having all
the tensors in the first argument of the model call function: model(inputs)
.
If you choose this second option, there are three possibilities you can use to gather all the input Tensors in the first positional argument :
- a single Tensor with
input_ids
only and nothing else:model(inputs_ids)
- a list of varying length with one or several input Tensors IN THE ORDER given in the docstring:
model([input_ids, attention_mask])
ormodel([input_ids, attention_mask, token_type_ids])
- a dictionary with one or several input Tensors associated to the input names given in the docstring:
model({"input_ids": input_ids, "token_type_ids": token_type_ids})
(
input_ids: typing.Union[typing.List[tensorflow.python.framework.ops.Tensor], typing.List[numpy.ndarray], typing.List[tensorflow.python.keras.engine.keras_tensor.KerasTensor], typing.Dict[str, tensorflow.python.framework.ops.Tensor], typing.Dict[str, numpy.ndarray], typing.Dict[str, tensorflow.python.keras.engine.keras_tensor.KerasTensor], tensorflow.python.framework.ops.Tensor, numpy.ndarray, tensorflow.python.keras.engine.keras_tensor.KerasTensor, NoneType] = None
bbox: typing.Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor, NoneType] = None
attention_mask: typing.Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor, NoneType] = None
token_type_ids: typing.Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor, NoneType] = None
position_ids: typing.Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor, NoneType] = None
head_mask: typing.Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor, NoneType] = None
inputs_embeds: typing.Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor, NoneType] = None
encoder_hidden_states: typing.Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor, NoneType] = None
encoder_attention_mask: typing.Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor, NoneType] = None
output_attentions: typing.Optional[bool] = None
output_hidden_states: typing.Optional[bool] = None
return_dict: typing.Optional[bool] = None
training: typing.Optional[bool] = False
**kwargs
)
β
transformers.modeling_tf_outputs.TFBaseModelOutputWithPoolingAndCrossAttentions or tuple(tf.Tensor)
Parameters
-
input_ids (
Numpy array
ortf.Tensor
of shape(batch_size, sequence_length)
) — Indices of input sequence tokens in the vocabulary.Indices can be obtained using LayoutLMTokenizer. See PreTrainedTokenizer.call() and PreTrainedTokenizer.encode() for details.
-
bbox (
Numpy array
ortf.Tensor
of shape(batch_size, sequence_length, 4)
, optional) — Bounding Boxes of each input sequence tokens. Selected in the range[0, config.max_2d_position_embeddings- 1]
. -
attention_mask (
Numpy array
ortf.Tensor
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 tokens that are masked.
-
token_type_ids (
Numpy array
ortf.Tensor
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 (
Numpy array
ortf.Tensor
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 (
Numpy array
ortf.Tensor
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 (
tf.Tensor
of shape(batch_size, sequence_length, hidden_size)
, optional) — Optionally, instead of passinginput_ids
you can choose to directly pass an embedded representation. This is useful if you want more control over how to convertinput_ids
indices into associated vectors than the model’s internal embedding lookup matrix. -
output_attentions (
bool
, optional) — Whether or not to return the attentions tensors of all attention layers. Seeattentions
under returned tensors for more detail. - output_hidden_states (
bool
, optional) — Whether or not to return the hidden states of all layers. Seehidden_states
under returned tensors for more detail. -
return_dict (
bool
, optional) — Whether or not to return a ModelOutput instead of a plain tuple. -
training (
bool
, optional, defaults toFalse
) — Whether or not to use the model in training mode (some modules like dropout modules have different behaviors between training and evaluation).
Returns
transformers.modeling_tf_outputs.TFBaseModelOutputWithPoolingAndCrossAttentions or tuple(tf.Tensor)
A transformers.modeling_tf_outputs.TFBaseModelOutputWithPoolingAndCrossAttentions or a tuple of tf.Tensor
(if
return_dict=False
is passed or when config.return_dict=False
) comprising various elements depending on the
configuration (LayoutLMConfig) and inputs.
-
last_hidden_state (
tf.Tensor
of shape(batch_size, sequence_length, hidden_size)
) β Sequence of hidden-states at the output of the last layer of the model. -
pooler_output (
tf.Tensor
of shape(batch_size, hidden_size)
) β 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.This output is usually not a good summary of the semantic content of the input, youβre often better with averaging or pooling the sequence of hidden-states for the whole input sequence.
-
past_key_values (
List[tf.Tensor]
, optional, returned whenuse_cache=True
is passed or whenconfig.use_cache=True
) β List oftf.Tensor
of lengthconfig.n_layers
, with each tensor of shape(2, batch_size, num_heads, sequence_length, embed_size_per_head)
).Contains pre-computed hidden-states (key and values in the attention blocks) that can be used (see
past_key_values
input) to speed up sequential decoding. -
hidden_states (
tuple(tf.Tensor)
, optional, returned whenoutput_hidden_states=True
is passed or whenconfig.output_hidden_states=True
) β Tuple oftf.Tensor
(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(tf.Tensor)
, optional, returned whenoutput_attentions=True
is passed or whenconfig.output_attentions=True
) β Tuple oftf.Tensor
(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.
-
cross_attentions (
tuple(tf.Tensor)
, optional, returned whenoutput_attentions=True
is passed or whenconfig.output_attentions=True
) β Tuple oftf.Tensor
(one for each layer) of shape(batch_size, num_heads, sequence_length, sequence_length)
.Attentions weights of the decoderβs cross-attention layer, after the attention softmax, used to compute the weighted average in the cross-attention heads.
The TFLayoutLMModel forward method, overrides the __call__
special method.
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.
Examples:
>>> from transformers import LayoutLMTokenizer, TFLayoutLMModel
>>> import tensorflow as tf
>>> tokenizer = LayoutLMTokenizer.from_pretrained('microsoft/layoutlm-base-uncased')
>>> model = TFLayoutLMModel.from_pretrained('microsoft/layoutlm-base-uncased')
>>> words = ["Hello", "world"]
>>> normalized_word_boxes = [637, 773, 693, 782], [698, 773, 733, 782]
>>> token_boxes = []
>>> for word, box in zip(words, normalized_word_boxes):
... word_tokens = tokenizer.tokenize(word)
... token_boxes.extend([box] * len(word_tokens))
>>> # add bounding boxes of cls + sep tokens
>>> token_boxes = [[0, 0, 0, 0]] + token_boxes + [[1000, 1000, 1000, 1000]]
>>> encoding = tokenizer(' '.join(words), return_tensors="tf")
>>> input_ids = encoding["input_ids"]
>>> attention_mask = encoding["attention_mask"]
>>> token_type_ids = encoding["token_type_ids"]
>>> bbox = tf.convert_to_tensor([token_boxes])
>>> outputs = model(input_ids=input_ids, bbox=bbox, attention_mask=attention_mask, token_type_ids=token_type_ids)
>>> last_hidden_states = outputs.last_hidden_state
TFLayoutLMForMaskedLM
( *args **kwargs )
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.
LayoutLM Model with a language modeling
head on top.
This model inherits from TFPreTrainedModel. Check the superclass documentation for the generic methods the library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads etc.)
This model is also a tf.keras.Model subclass. Use it as a regular TF 2.0 Keras Model and refer to the TF 2.0 documentation for all matter related to general usage and behavior.
TF 2.0 models accepts two formats as inputs:
- having all inputs as keyword arguments (like PyTorch models), or
- having all inputs as a list, tuple or dict in the first positional arguments.
This second option is useful when using tf.keras.Model.fit
method which currently requires having all
the tensors in the first argument of the model call function: model(inputs)
.
If you choose this second option, there are three possibilities you can use to gather all the input Tensors in the first positional argument :
- a single Tensor with
input_ids
only and nothing else:model(inputs_ids)
- a list of varying length with one or several input Tensors IN THE ORDER given in the docstring:
model([input_ids, attention_mask])
ormodel([input_ids, attention_mask, token_type_ids])
- a dictionary with one or several input Tensors associated to the input names given in the docstring:
model({"input_ids": input_ids, "token_type_ids": token_type_ids})
(
input_ids: typing.Union[typing.List[tensorflow.python.framework.ops.Tensor], typing.List[numpy.ndarray], typing.List[tensorflow.python.keras.engine.keras_tensor.KerasTensor], typing.Dict[str, tensorflow.python.framework.ops.Tensor], typing.Dict[str, numpy.ndarray], typing.Dict[str, tensorflow.python.keras.engine.keras_tensor.KerasTensor], tensorflow.python.framework.ops.Tensor, numpy.ndarray, tensorflow.python.keras.engine.keras_tensor.KerasTensor, NoneType] = None
bbox: typing.Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor, NoneType] = None
attention_mask: typing.Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor, NoneType] = None
token_type_ids: typing.Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor, NoneType] = None
position_ids: typing.Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor, NoneType] = None
head_mask: typing.Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor, NoneType] = None
inputs_embeds: typing.Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor, NoneType] = None
output_attentions: typing.Optional[bool] = None
output_hidden_states: typing.Optional[bool] = None
return_dict: typing.Optional[bool] = None
labels: typing.Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor, NoneType] = None
training: typing.Optional[bool] = False
**kwargs
)
β
transformers.modeling_tf_outputs.TFMaskedLMOutput or tuple(tf.Tensor)
Parameters
-
input_ids (
Numpy array
ortf.Tensor
of shape(batch_size, sequence_length)
) — Indices of input sequence tokens in the vocabulary.Indices can be obtained using LayoutLMTokenizer. See PreTrainedTokenizer.call() and PreTrainedTokenizer.encode() for details.
-
bbox (
Numpy array
ortf.Tensor
of shape(batch_size, sequence_length, 4)
, optional) — Bounding Boxes of each input sequence tokens. Selected in the range[0, config.max_2d_position_embeddings- 1]
. -
attention_mask (
Numpy array
ortf.Tensor
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 tokens that are masked.
-
token_type_ids (
Numpy array
ortf.Tensor
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 (
Numpy array
ortf.Tensor
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 (
Numpy array
ortf.Tensor
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 (
tf.Tensor
of shape(batch_size, sequence_length, hidden_size)
, optional) — Optionally, instead of passinginput_ids
you can choose to directly pass an embedded representation. This is useful if you want more control over how to convertinput_ids
indices into associated vectors than the model’s internal embedding lookup matrix. -
output_attentions (
bool
, optional) — Whether or not to return the attentions tensors of all attention layers. Seeattentions
under returned tensors for more detail. - output_hidden_states (
bool
, optional) — Whether or not to return the hidden states of all layers. Seehidden_states
under returned tensors for more detail. -
return_dict (
bool
, optional) — Whether or not to return a ModelOutput instead of a plain tuple. -
training (
bool
, optional, defaults toFalse
) — Whether or not to use the model in training mode (some modules like dropout modules have different behaviors between training and evaluation). -
labels (
tf.Tensor
ornp.ndarray
of shape(batch_size, sequence_length)
, optional) — Labels for computing the masked language modeling loss. Indices should be in[-100, 0, ..., config.vocab_size]
(seeinput_ids
docstring) Tokens with indices set to-100
are ignored (masked), the loss is only computed for the tokens with labels in[0, ..., config.vocab_size]
Returns
transformers.modeling_tf_outputs.TFMaskedLMOutput or tuple(tf.Tensor)
A transformers.modeling_tf_outputs.TFMaskedLMOutput or a tuple of tf.Tensor
(if
return_dict=False
is passed or when config.return_dict=False
) comprising various elements depending on the
configuration (LayoutLMConfig) and inputs.
-
loss (
tf.Tensor
of shape(n,)
, optional, where n is the number of non-masked labels, returned whenlabels
is provided) β Masked language modeling (MLM) loss. -
logits (
tf.Tensor
of shape(batch_size, sequence_length, config.vocab_size)
) β Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax). -
hidden_states (
tuple(tf.Tensor)
, optional, returned whenoutput_hidden_states=True
is passed or whenconfig.output_hidden_states=True
) β Tuple oftf.Tensor
(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(tf.Tensor)
, optional, returned whenoutput_attentions=True
is passed or whenconfig.output_attentions=True
) β Tuple oftf.Tensor
(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.
The TFLayoutLMForMaskedLM forward method, overrides the __call__
special method.
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.
Examples:
>>> from transformers import LayoutLMTokenizer, TFLayoutLMForMaskedLM
>>> import tensorflow as tf
>>> tokenizer = LayoutLMTokenizer.from_pretrained('microsoft/layoutlm-base-uncased')
>>> model = TFLayoutLMForMaskedLM.from_pretrained('microsoft/layoutlm-base-uncased')
>>> words = ["Hello", "[MASK]"]
>>> normalized_word_boxes = [637, 773, 693, 782], [698, 773, 733, 782]
>>> token_boxes = []
>>> for word, box in zip(words, normalized_word_boxes):
... word_tokens = tokenizer.tokenize(word)
... token_boxes.extend([box] * len(word_tokens))
>>> # add bounding boxes of cls + sep tokens
>>> token_boxes = [[0, 0, 0, 0]] + token_boxes + [[1000, 1000, 1000, 1000]]
>>> encoding = tokenizer(' '.join(words), return_tensors="tf")
>>> input_ids = encoding["input_ids"]
>>> attention_mask = encoding["attention_mask"]
>>> token_type_ids = encoding["token_type_ids"]
>>> bbox = tf.convert_to_tensor([token_boxes])
>>> labels = tokenizer("Hello world", return_tensors="tf")["input_ids"]
>>> outputs = model(input_ids=input_ids, bbox=bbox, attention_mask=attention_mask, token_type_ids=token_type_ids,
... labels=labels)
>>> loss = outputs.loss
TFLayoutLMForSequenceClassification
( *args **kwargs )
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.
LayoutLM Model transformer with a sequence classification/regression head on top (a linear layer on top of the pooled output) e.g. for GLUE tasks.
This model inherits from TFPreTrainedModel. Check the superclass documentation for the generic methods the library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads etc.)
This model is also a tf.keras.Model subclass. Use it as a regular TF 2.0 Keras Model and refer to the TF 2.0 documentation for all matter related to general usage and behavior.
TF 2.0 models accepts two formats as inputs:
- having all inputs as keyword arguments (like PyTorch models), or
- having all inputs as a list, tuple or dict in the first positional arguments.
This second option is useful when using tf.keras.Model.fit
method which currently requires having all
the tensors in the first argument of the model call function: model(inputs)
.
If you choose this second option, there are three possibilities you can use to gather all the input Tensors in the first positional argument :
- a single Tensor with
input_ids
only and nothing else:model(inputs_ids)
- a list of varying length with one or several input Tensors IN THE ORDER given in the docstring:
model([input_ids, attention_mask])
ormodel([input_ids, attention_mask, token_type_ids])
- a dictionary with one or several input Tensors associated to the input names given in the docstring:
model({"input_ids": input_ids, "token_type_ids": token_type_ids})
(
input_ids: typing.Union[typing.List[tensorflow.python.framework.ops.Tensor], typing.List[numpy.ndarray], typing.List[tensorflow.python.keras.engine.keras_tensor.KerasTensor], typing.Dict[str, tensorflow.python.framework.ops.Tensor], typing.Dict[str, numpy.ndarray], typing.Dict[str, tensorflow.python.keras.engine.keras_tensor.KerasTensor], tensorflow.python.framework.ops.Tensor, numpy.ndarray, tensorflow.python.keras.engine.keras_tensor.KerasTensor, NoneType] = None
bbox: typing.Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor, NoneType] = None
attention_mask: typing.Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor, NoneType] = None
token_type_ids: typing.Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor, NoneType] = None
position_ids: typing.Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor, NoneType] = None
head_mask: typing.Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor, NoneType] = None
inputs_embeds: typing.Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor, NoneType] = None
output_attentions: typing.Optional[bool] = None
output_hidden_states: typing.Optional[bool] = None
return_dict: typing.Optional[bool] = None
labels: typing.Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor, NoneType] = None
training: typing.Optional[bool] = False
**kwargs
)
β
transformers.modeling_tf_outputs.TFSequenceClassifierOutput or tuple(tf.Tensor)
Parameters
-
input_ids (
Numpy array
ortf.Tensor
of shape(batch_size, sequence_length)
) — Indices of input sequence tokens in the vocabulary.Indices can be obtained using LayoutLMTokenizer. See PreTrainedTokenizer.call() and PreTrainedTokenizer.encode() for details.
-
bbox (
Numpy array
ortf.Tensor
of shape(batch_size, sequence_length, 4)
, optional) — Bounding Boxes of each input sequence tokens. Selected in the range[0, config.max_2d_position_embeddings- 1]
. -
attention_mask (
Numpy array
ortf.Tensor
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 tokens that are masked.
-
token_type_ids (
Numpy array
ortf.Tensor
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 (
Numpy array
ortf.Tensor
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 (
Numpy array
ortf.Tensor
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 (
tf.Tensor
of shape(batch_size, sequence_length, hidden_size)
, optional) — Optionally, instead of passinginput_ids
you can choose to directly pass an embedded representation. This is useful if you want more control over how to convertinput_ids
indices into associated vectors than the model’s internal embedding lookup matrix. -
output_attentions (
bool
, optional) — Whether or not to return the attentions tensors of all attention layers. Seeattentions
under returned tensors for more detail. - output_hidden_states (
bool
, optional) — Whether or not to return the hidden states of all layers. Seehidden_states
under returned tensors for more detail. -
return_dict (
bool
, optional) — Whether or not to return a ModelOutput instead of a plain tuple. -
training (
bool
, optional, defaults toFalse
) — Whether or not to use the model in training mode (some modules like dropout modules have different behaviors between training and evaluation). -
labels (
tf.Tensor
ornp.ndarray
of shape(batch_size,)
, optional) — Labels for computing the sequence classification/regression loss. Indices should be in[0, ..., config.num_labels - 1]
. Ifconfig.num_labels == 1
a regression loss is computed (Mean-Square loss), Ifconfig.num_labels > 1
a classification loss is computed (Cross-Entropy).
Returns
transformers.modeling_tf_outputs.TFSequenceClassifierOutput or tuple(tf.Tensor)
A transformers.modeling_tf_outputs.TFSequenceClassifierOutput or a tuple of tf.Tensor
(if
return_dict=False
is passed or when config.return_dict=False
) comprising various elements depending on the
configuration (LayoutLMConfig) and inputs.
-
loss (
tf.Tensor
of shape(batch_size, )
, optional, returned whenlabels
is provided) β Classification (or regression if config.num_labels==1) loss. -
logits (
tf.Tensor
of shape(batch_size, config.num_labels)
) β Classification (or regression if config.num_labels==1) scores (before SoftMax). -
hidden_states (
tuple(tf.Tensor)
, optional, returned whenoutput_hidden_states=True
is passed or whenconfig.output_hidden_states=True
) β Tuple oftf.Tensor
(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(tf.Tensor)
, optional, returned whenoutput_attentions=True
is passed or whenconfig.output_attentions=True
) β Tuple oftf.Tensor
(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.
The TFLayoutLMForSequenceClassification forward method, overrides the __call__
special method.
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.
Examples:
>>> from transformers import LayoutLMTokenizer, TFLayoutLMForSequenceClassification
>>> import tensorflow as tf
>>> tokenizer = LayoutLMTokenizer.from_pretrained('microsoft/layoutlm-base-uncased')
>>> model = TFLayoutLMForSequenceClassification.from_pretrained('microsoft/layoutlm-base-uncased')
>>> words = ["Hello", "world"]
>>> normalized_word_boxes = [637, 773, 693, 782], [698, 773, 733, 782]
>>> token_boxes = []
>>> for word, box in zip(words, normalized_word_boxes):
... word_tokens = tokenizer.tokenize(word)
... token_boxes.extend([box] * len(word_tokens))
>>> # add bounding boxes of cls + sep tokens
>>> token_boxes = [[0, 0, 0, 0]] + token_boxes + [[1000, 1000, 1000, 1000]]
>>> encoding = tokenizer(' '.join(words), return_tensors="tf")
>>> input_ids = encoding["input_ids"]
>>> attention_mask = encoding["attention_mask"]
>>> token_type_ids = encoding["token_type_ids"]
>>> bbox = tf.convert_to_tensor([token_boxes])
>>> sequence_label = tf.convert_to_tensor([1])
>>> outputs = model(input_ids=input_ids, bbox=bbox, attention_mask=attention_mask, token_type_ids=token_type_ids,
... labels=sequence_label)
>>> loss = outputs.loss
>>> logits = outputs.logits
TFLayoutLMForTokenClassification
( *args **kwargs )
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.
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.
This model inherits from TFPreTrainedModel. Check the superclass documentation for the generic methods the library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads etc.)
This model is also a tf.keras.Model subclass. Use it as a regular TF 2.0 Keras Model and refer to the TF 2.0 documentation for all matter related to general usage and behavior.
TF 2.0 models accepts two formats as inputs:
- having all inputs as keyword arguments (like PyTorch models), or
- having all inputs as a list, tuple or dict in the first positional arguments.
This second option is useful when using tf.keras.Model.fit
method which currently requires having all
the tensors in the first argument of the model call function: model(inputs)
.
If you choose this second option, there are three possibilities you can use to gather all the input Tensors in the first positional argument :
- a single Tensor with
input_ids
only and nothing else:model(inputs_ids)
- a list of varying length with one or several input Tensors IN THE ORDER given in the docstring:
model([input_ids, attention_mask])
ormodel([input_ids, attention_mask, token_type_ids])
- a dictionary with one or several input Tensors associated to the input names given in the docstring:
model({"input_ids": input_ids, "token_type_ids": token_type_ids})
(
input_ids: typing.Union[typing.List[tensorflow.python.framework.ops.Tensor], typing.List[numpy.ndarray], typing.List[tensorflow.python.keras.engine.keras_tensor.KerasTensor], typing.Dict[str, tensorflow.python.framework.ops.Tensor], typing.Dict[str, numpy.ndarray], typing.Dict[str, tensorflow.python.keras.engine.keras_tensor.KerasTensor], tensorflow.python.framework.ops.Tensor, numpy.ndarray, tensorflow.python.keras.engine.keras_tensor.KerasTensor, NoneType] = None
bbox: typing.Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor, NoneType] = None
attention_mask: typing.Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor, NoneType] = None
token_type_ids: typing.Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor, NoneType] = None
position_ids: typing.Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor, NoneType] = None
head_mask: typing.Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor, NoneType] = None
inputs_embeds: typing.Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor, NoneType] = None
output_attentions: typing.Optional[bool] = None
output_hidden_states: typing.Optional[bool] = None
return_dict: typing.Optional[bool] = None
labels: typing.Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor, NoneType] = None
training: typing.Optional[bool] = False
**kwargs
)
β
transformers.modeling_tf_outputs.TFTokenClassifierOutput or tuple(tf.Tensor)
Parameters
-
input_ids (
Numpy array
ortf.Tensor
of shape(batch_size, sequence_length)
) — Indices of input sequence tokens in the vocabulary.Indices can be obtained using LayoutLMTokenizer. See PreTrainedTokenizer.call() and PreTrainedTokenizer.encode() for details.
-
bbox (
Numpy array
ortf.Tensor
of shape(batch_size, sequence_length, 4)
, optional) — Bounding Boxes of each input sequence tokens. Selected in the range[0, config.max_2d_position_embeddings- 1]
. -
attention_mask (
Numpy array
ortf.Tensor
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 tokens that are masked.
-
token_type_ids (
Numpy array
ortf.Tensor
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 (
Numpy array
ortf.Tensor
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 (
Numpy array
ortf.Tensor
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 (
tf.Tensor
of shape(batch_size, sequence_length, hidden_size)
, optional) — Optionally, instead of passinginput_ids
you can choose to directly pass an embedded representation. This is useful if you want more control over how to convertinput_ids
indices into associated vectors than the model’s internal embedding lookup matrix. -
output_attentions (
bool
, optional) — Whether or not to return the attentions tensors of all attention layers. Seeattentions
under returned tensors for more detail. - output_hidden_states (
bool
, optional) — Whether or not to return the hidden states of all layers. Seehidden_states
under returned tensors for more detail. -
return_dict (
bool
, optional) — Whether or not to return a ModelOutput instead of a plain tuple. -
training (
bool
, optional, defaults toFalse
) — Whether or not to use the model in training mode (some modules like dropout modules have different behaviors between training and evaluation). -
labels (
tf.Tensor
ornp.ndarray
of shape(batch_size, sequence_length)
, optional) — Labels for computing the token classification loss. Indices should be in[0, ..., config.num_labels - 1]
.
Returns
transformers.modeling_tf_outputs.TFTokenClassifierOutput or tuple(tf.Tensor)
A transformers.modeling_tf_outputs.TFTokenClassifierOutput or a tuple of tf.Tensor
(if
return_dict=False
is passed or when config.return_dict=False
) comprising various elements depending on the
configuration (LayoutLMConfig) and inputs.
-
loss (
tf.Tensor
of shape(n,)
, optional, where n is the number of unmasked labels, returned whenlabels
is provided) β Classification loss. -
logits (
tf.Tensor
of shape(batch_size, sequence_length, config.num_labels)
) β Classification scores (before SoftMax). -
hidden_states (
tuple(tf.Tensor)
, optional, returned whenoutput_hidden_states=True
is passed or whenconfig.output_hidden_states=True
) β Tuple oftf.Tensor
(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(tf.Tensor)
, optional, returned whenoutput_attentions=True
is passed or whenconfig.output_attentions=True
) β Tuple oftf.Tensor
(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.
The TFLayoutLMForTokenClassification forward method, overrides the __call__
special method.
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.
Examples:
>>> from transformers import LayoutLMTokenizer, TFLayoutLMForTokenClassification
>>> import torch
>>> tokenizer = LayoutLMTokenizer.from_pretrained('microsoft/layoutlm-base-uncased')
>>> model = TFLayoutLMForTokenClassification.from_pretrained('microsoft/layoutlm-base-uncased')
>>> words = ["Hello", "world"]
>>> normalized_word_boxes = [637, 773, 693, 782], [698, 773, 733, 782]
>>> token_boxes = []
>>> for word, box in zip(words, normalized_word_boxes):
... word_tokens = tokenizer.tokenize(word)
... token_boxes.extend([box] * len(word_tokens))
>>> # add bounding boxes of cls + sep tokens
>>> token_boxes = [[0, 0, 0, 0]] + token_boxes + [[1000, 1000, 1000, 1000]]
>>> encoding = tokenizer(' '.join(words), return_tensors="tf")
>>> input_ids = encoding["input_ids"]
>>> attention_mask = encoding["attention_mask"]
>>> token_type_ids = encoding["token_type_ids"]
>>> bbox = tf.convert_to_tensor([token_boxes])
>>> token_labels = tf.convert_to_tensor([1,1,0,0])
>>> outputs = model(input_ids=input_ids, bbox=bbox, attention_mask=attention_mask, token_type_ids=token_type_ids,
... labels=token_labels)
>>> loss = outputs.loss
>>> logits = outputs.logits