
 
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. It’s 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 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 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 Google’s Tesseract (there’s 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:
	Copied
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:
	Copied
from PIL import Image
# Document can be a png, jpg, etc. PDFs must be converted to images.
image = Image.open(name_of_your_document).convert("RGB")
width, height = image.size
Resources
	
A list of official Hugging Face and community (indicated by 🌎) resources to help you get started with LayoutLM. If you’re interested in submitting a resource to be included here, please feel free to open a Pull Request and we’ll review it! The resource should ideally demonstrate something new instead of duplicating an existing resource.
Document Question Answering
A blog post on fine-tuning
LayoutLM for document-understanding using Keras & Hugging Face
Transformers.
A blog post on how to fine-tune LayoutLM for document-understanding using only Hugging Face Transformers.
A notebook on how to fine-tune LayoutLM on the FUNSD dataset with image embeddings.
See also: Document question answering task guide
Text Classification
A notebook on how to fine-tune LayoutLM for sequence classification on the RVL-CDIP dataset.
Text classification task guide
Token Classification
A notebook on how to fine-tune LayoutLM for token classification on the FUNSD dataset.
Token classification task guide
Other resources
Masked language modeling task guide
🚀 Deploy
A blog post on how to Deploy LayoutLM with Hugging Face Inference Endpoints.
LayoutLMConfig
	
class transformers.LayoutLMConfig
<
source
>
(
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
position_embedding_type = 'absolute'
use_cache = True
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 or function, 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 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.
pad_token_id (int, optional, defaults to 0) —
The value used to pad input_ids.
position_embedding_type (str, optional, defaults to "absolute") —
Type of position embedding. Choose one of "absolute", "relative_key", "relative_key_query". For
positional embeddings use "absolute". For more information on "relative_key", please refer to
Self-Attention with Relative Position Representations (Shaw et al.).
For more information on "relative_key_query", please refer to Method 4 in Improve Transformer Models
with Better Relative Position Embeddings (Huang et al.).
use_cache (bool, optional, defaults to True) —
Whether or not the model should return the last key/values attentions (not used by all models). Only
relevant if config.is_decoder=True.
classifier_dropout (float, optional) —
The dropout ratio for the classification head.
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
microsoft/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:
	Copied
 from transformers import LayoutLMConfig, LayoutLMModel
 # Initializing a LayoutLM configuration
 configuration = LayoutLMConfig()
 # Initializing a model (with random weights) from the configuration
 model = LayoutLMModel(configuration)
 # Accessing the model configuration
 configuration = model.config
LayoutLMTokenizer
	
class transformers.LayoutLMTokenizer
<
source
>
(
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
)
Parameters 
vocab_file (str) —
File containing the vocabulary.
do_lower_case (bool, optional, defaults to True) —
Whether or not to lowercase the input when tokenizing.
do_basic_tokenize (bool, optional, defaults to True) —
Whether or not to do basic tokenization before WordPiece.
never_split (Iterable, optional) —
Collection of tokens which will never be split during tokenization. Only has an effect when
do_basic_tokenize=True
unk_token (str, optional, defaults to "[UNK]") —
The unknown token. A token that is not in the vocabulary cannot be converted to an ID and is set to be this
token instead.
sep_token (str, optional, defaults to "[SEP]") —
The separator token, which is used when building a sequence from multiple sequences, e.g. two sequences for
sequence classification or for a text and a question for question answering. It is also used as the last
token of a sequence built with special tokens.
pad_token (str, optional, defaults to "[PAD]") —
The token used for padding, for example when batching sequences of different lengths.
cls_token (str, optional, defaults to "[CLS]") —
The classifier token which is used when doing sequence classification (classification of the whole sequence
instead of per-token classification). It is the first token of the sequence when built with special tokens.
mask_token (str, optional, defaults to "[MASK]") —
The token used for masking values. This is the token used when training this model with masked language
modeling. This is the token which the model will try to predict.
tokenize_chinese_chars (bool, optional, defaults to True) —
Whether or not to tokenize Chinese characters.
This should likely be deactivated for Japanese (see this
issue).
strip_accents (bool, optional) —
Whether or not to strip all accents. If this option is not specified, then it will be determined by the
value for lowercase (as in the original LayoutLM).
Construct a LayoutLM tokenizer. Based on WordPiece.
This tokenizer inherits from PreTrainedTokenizer which contains most of the main methods. Users should refer to
this superclass for more information regarding those methods.
build_inputs_with_special_tokens
<
source
>
(
token_ids_0: typing.List[int]
token_ids_1: typing.Optional[typing.List[int]] = None
)
→
List[int]
Parameters 
token_ids_0 (List[int]) —
List of IDs to which the special tokens will be added.
token_ids_1 (List[int], optional) —
Optional second list of IDs for sequence pairs.
Returns
List[int]
List of input IDs with the appropriate special tokens.
Build model inputs from a sequence or a pair of sequence for sequence classification tasks by concatenating and
adding special tokens. A LayoutLM sequence has the following format:
single sequence: [CLS] X [SEP]
pair of sequences: [CLS] A [SEP] B [SEP]
convert_tokens_to_string
<
source
>
(
tokens
)
Converts a sequence of tokens (string) in a single string.
create_token_type_ids_from_sequences
<
source
>
(
token_ids_0: typing.List[int]
token_ids_1: typing.Optional[typing.List[int]] = None
)
→
List[int]
Parameters 
token_ids_0 (List[int]) —
List of IDs.
token_ids_1 (List[int], optional) —
Optional second list of IDs for sequence pairs.
Returns
List[int]
List of token type IDs according to the given sequence(s).
Create a mask from the two sequences passed to be used in a sequence-pair classification task. A LayoutLM
sequence pair mask has the following format:
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0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1
| first sequence    | second sequence |
If token_ids_1 is None, this method only returns the first portion of the mask (0s).
get_special_tokens_mask
<
source
>
(
token_ids_0: typing.List[int]
token_ids_1: typing.Optional[typing.List[int]] = None
already_has_special_tokens: bool = False
)
→
List[int]
Parameters 
token_ids_0 (List[int]) —
List of IDs.
token_ids_1 (List[int], optional) —
Optional second list of IDs for sequence pairs.
already_has_special_tokens (bool, optional, defaults to False) —
Whether or not the token list is already formatted with special tokens for the model.
Returns
List[int]
A list of integers in the range [0, 1]: 1 for a special token, 0 for a sequence token.
Retrieve sequence ids from a token list that has no special tokens added. This method is called when adding
special tokens using the tokenizer prepare_for_model method.
LayoutLMTokenizerFast
	
class transformers.LayoutLMTokenizerFast
<
source
>
(
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
)
Parameters 
vocab_file (str) —
File containing the vocabulary.
do_lower_case (bool, optional, defaults to True) —
Whether or not to lowercase the input when tokenizing.
unk_token (str, optional, defaults to "[UNK]") —
The unknown token. A token that is not in the vocabulary cannot be converted to an ID and is set to be this
token instead.
sep_token (str, optional, defaults to "[SEP]") —
The separator token, which is used when building a sequence from multiple sequences, e.g. two sequences for
sequence classification or for a text and a question for question answering. It is also used as the last
token of a sequence built with special tokens.
pad_token (str, optional, defaults to "[PAD]") —
The token used for padding, for example when batching sequences of different lengths.
cls_token (str, optional, defaults to "[CLS]") —
The classifier token which is used when doing sequence classification (classification of the whole sequence
instead of per-token classification). It is the first token of the sequence when built with special tokens.
mask_token (str, optional, defaults to "[MASK]") —
The token used for masking values. This is the token used when training this model with masked language
modeling. This is the token which the model will try to predict.
clean_text (bool, optional, defaults to True) —
Whether or not to clean the text before tokenization by removing any control characters and replacing all
whitespaces by the classic one.
tokenize_chinese_chars (bool, optional, defaults to True) —
Whether or not to tokenize Chinese characters. This should likely be deactivated for Japanese (see this
issue).
strip_accents (bool, optional) —
Whether or not to strip all accents. If this option is not specified, then it will be determined by the
value for lowercase (as in the original LayoutLM).
wordpieces_prefix (str, optional, defaults to "##") —
The prefix for subwords.
Construct a “fast” LayoutLM tokenizer (backed by HuggingFace’s tokenizers library). Based on WordPiece.
This tokenizer inherits from PreTrainedTokenizerFast which contains most of the main methods. Users should
refer to this superclass for more information regarding those methods.
build_inputs_with_special_tokens
<
source
>
(
token_ids_0
token_ids_1 = None
)
→
List[int]
Parameters 
token_ids_0 (List[int]) —
List of IDs to which the special tokens will be added.
token_ids_1 (List[int], optional) —
Optional second list of IDs for sequence pairs.
Returns
List[int]
List of input IDs with the appropriate special tokens.
Build model inputs from a sequence or a pair of sequence for sequence classification tasks by concatenating and
adding special tokens. A LayoutLM sequence has the following format:
single sequence: [CLS] X [SEP]
pair of sequences: [CLS] A [SEP] B [SEP]
create_token_type_ids_from_sequences
<
source
>
(
token_ids_0: typing.List[int]
token_ids_1: typing.Optional[typing.List[int]] = None
)
→
List[int]
Parameters 
token_ids_0 (List[int]) —
List of IDs.
token_ids_1 (List[int], optional) —
Optional second list of IDs for sequence pairs.
Returns
List[int]
List of token type IDs according to the given sequence(s).
Create a mask from the two sequences passed to be used in a sequence-pair classification task. A LayoutLM
sequence pair mask has the following format:
	Copied
0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1
| first sequence    | second sequence |
If token_ids_1 is None, this method only returns the first portion of the mask (0s).
LayoutLMModel
	
class transformers.LayoutLMModel
<
source
>
(
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.
forward
<
source
>
(
input_ids: typing.Optional[torch.LongTensor] = None
bbox: typing.Optional[torch.LongTensor] = None
attention_mask: typing.Optional[torch.FloatTensor] = None
token_type_ids: typing.Optional[torch.LongTensor] = None
position_ids: typing.Optional[torch.LongTensor] = None
head_mask: typing.Optional[torch.FloatTensor] = None
inputs_embeds: typing.Optional[torch.FloatTensor] = None
encoder_hidden_states: typing.Optional[torch.FloatTensor] = None
encoder_attention_mask: typing.Optional[torch.FloatTensor] = None
output_attentions: typing.Optional[bool] = None
output_hidden_states: typing.Optional[bool] = None
return_dict: typing.Optional[bool] = None
)
→
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 AutoTokenizer. See PreTrainedTokenizer.encode() and
PreTrainedTokenizer.call() for details.
What are input IDs?
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.
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
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)) — 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) 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 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, if the model has an embedding layer, +
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 optional 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.
cross_attentions (tuple(torch.FloatTensor), optional, returned when output_attentions=True and config.add_cross_attention=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 of the decoder’s 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 when use_cache=True is passed or when config.use_cache=True) — Tuple of tuple(torch.FloatTensor) of length config.n_layers, with each tuple having 2 tensors of shape
(batch_size, num_heads, sequence_length, embed_size_per_head)) and optionally if
config.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 (see past_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:
	Copied
 from transformers import AutoTokenizer, LayoutLMModel
 import torch
 tokenizer = AutoTokenizer.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
	
class transformers.LayoutLMForMaskedLM
<
source
>
(
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.
forward
<
source
>
(
input_ids: typing.Optional[torch.LongTensor] = None
bbox: typing.Optional[torch.LongTensor] = None
attention_mask: typing.Optional[torch.FloatTensor] = None
token_type_ids: typing.Optional[torch.LongTensor] = None
position_ids: typing.Optional[torch.LongTensor] = None
head_mask: typing.Optional[torch.FloatTensor] = None
inputs_embeds: typing.Optional[torch.FloatTensor] = None
labels: typing.Optional[torch.LongTensor] = None
encoder_hidden_states = None
encoder_attention_mask = None
output_attentions: typing.Optional[bool] = None
output_hidden_states: typing.Optional[bool] = None
return_dict: typing.Optional[bool] = None
)
→
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 AutoTokenizer. See PreTrainedTokenizer.encode() and
PreTrainedTokenizer.call() for details.
What are input IDs?
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.
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.
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] (see input_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 when labels is provided) — Masked language 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, if the model has an embedding layer, +
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 optional 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.
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:
	Copied
 from transformers import AutoTokenizer, LayoutLMForMaskedLM
 import torch
 tokenizer = AutoTokenizer.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
	
class transformers.LayoutLMForSequenceClassification
<
source
>
(
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 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.
forward
<
source
>
(
input_ids: typing.Optional[torch.LongTensor] = None
bbox: typing.Optional[torch.LongTensor] = None
attention_mask: typing.Optional[torch.FloatTensor] = None
token_type_ids: typing.Optional[torch.LongTensor] = None
position_ids: typing.Optional[torch.LongTensor] = None
head_mask: typing.Optional[torch.FloatTensor] = None
inputs_embeds: typing.Optional[torch.FloatTensor] = None
labels: typing.Optional[torch.LongTensor] = None
output_attentions: typing.Optional[bool] = None
output_hidden_states: typing.Optional[bool] = None
return_dict: typing.Optional[bool] = None
)
→
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 AutoTokenizer. See PreTrainedTokenizer.encode() and
PreTrainedTokenizer.call() for details.
What are input IDs?
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.
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.
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]. If config.num_labels == 1 a regression loss is computed (Mean-Square loss), If
config.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 when labels is provided) — Classification (or regression if config.num_labels==1) loss.
logits (torch.FloatTensor of shape (batch_size, config.num_labels)) — Classification (or regression if config.num_labels==1) 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, if the model has an embedding layer, +
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 optional 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.
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:
	Copied
 from transformers import AutoTokenizer, LayoutLMForSequenceClassification
 import torch
 tokenizer = AutoTokenizer.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
	
class transformers.LayoutLMForTokenClassification
<
source
>
(
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
dataset and the SROIE 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.
forward
<
source
>
(
input_ids: typing.Optional[torch.LongTensor] = None
bbox: typing.Optional[torch.LongTensor] = None
attention_mask: typing.Optional[torch.FloatTensor] = None
token_type_ids: typing.Optional[torch.LongTensor] = None
position_ids: typing.Optional[torch.LongTensor] = None
head_mask: typing.Optional[torch.FloatTensor] = None
inputs_embeds: typing.Optional[torch.FloatTensor] = None
labels: typing.Optional[torch.LongTensor] = None
output_attentions: typing.Optional[bool] = None
output_hidden_states: typing.Optional[bool] = None
return_dict: typing.Optional[bool] = None
)
→
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 AutoTokenizer. See PreTrainedTokenizer.encode() and
PreTrainedTokenizer.call() for details.
What are input IDs?
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.
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.
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 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, if the model has an embedding layer, +
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 optional 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.
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:
	Copied
 from transformers import AutoTokenizer, LayoutLMForTokenClassification
 import torch
 tokenizer = AutoTokenizer.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
LayoutLMForQuestionAnswering
	
class transformers.LayoutLMForQuestionAnswering
<
source
>
(
config
has_visual_segment_embedding = True
)
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 span classification head on top for extractive question-answering tasks such as
DocVQA (a linear layer on top of the final hidden-states output to compute span start logits and span end logits).
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.
forward
<
source
>
(
input_ids: typing.Optional[torch.LongTensor] = None
bbox: typing.Optional[torch.LongTensor] = None
attention_mask: typing.Optional[torch.FloatTensor] = None
token_type_ids: typing.Optional[torch.LongTensor] = None
position_ids: typing.Optional[torch.LongTensor] = None
head_mask: typing.Optional[torch.FloatTensor] = None
inputs_embeds: typing.Optional[torch.FloatTensor] = None
start_positions: typing.Optional[torch.LongTensor] = None
end_positions: typing.Optional[torch.LongTensor] = None
output_attentions: typing.Optional[bool] = None
output_hidden_states: typing.Optional[bool] = None
return_dict: typing.Optional[bool] = None
)
→
transformers.modeling_outputs.QuestionAnsweringModelOutput or tuple(torch.FloatTensor)
Returns
transformers.modeling_outputs.QuestionAnsweringModelOutput or tuple(torch.FloatTensor)
A transformers.modeling_outputs.QuestionAnsweringModelOutput 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 when labels is provided) — Total span extraction loss is the sum of a Cross-Entropy for the start and end positions.
start_logits (torch.FloatTensor of shape (batch_size, sequence_length)) — Span-start scores (before SoftMax).
end_logits (torch.FloatTensor of shape (batch_size, sequence_length)) — Span-end 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, if the model has an embedding layer, +
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 optional 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.
start_positions (torch.LongTensor of shape (batch_size,), optional):
Labels for position (index) of the start of the labelled span for computing the token classification loss.
Positions are clamped to the length of the sequence (sequence_length). Position outside of the sequence
are not taken into account for computing the loss.
end_positions (torch.LongTensor of shape (batch_size,), optional):
Labels for position (index) of the end of the labelled span for computing the token classification loss.
Positions are clamped to the length of the sequence (sequence_length). Position outside of the sequence
are not taken into account for computing the loss.
Example:
In the example below, we prepare a question + context pair for the LayoutLM model. It will give us a prediction
of what it thinks the answer is (the span of the answer within the texts parsed from the image).
	Copied
 from transformers import AutoTokenizer, LayoutLMForQuestionAnswering
 from datasets import load_dataset
 import torch
 tokenizer = AutoTokenizer.from_pretrained("impira/layoutlm-document-qa", add_prefix_space=True)
 model = LayoutLMForQuestionAnswering.from_pretrained("impira/layoutlm-document-qa", revision="1e3ebac")
 dataset = load_dataset("nielsr/funsd", split="train")
 example = dataset[0]
 question = "what's his name?"
 words = example["words"]
 boxes = example["bboxes"]
 encoding = tokenizer(
...     question.split(), words, is_split_into_words=True, return_token_type_ids=True, return_tensors="pt"
... )
 bbox = []
 for i, s, w in zip(encoding.input_ids[0], encoding.sequence_ids(0), encoding.word_ids(0)):
...     if s == 1:
...         bbox.append(boxes[w])
...     elif i == tokenizer.sep_token_id:
...         bbox.append([1000] * 4)
...     else:
...         bbox.append([0] * 4)
 encoding["bbox"] = torch.tensor([bbox])
 word_ids = encoding.word_ids(0)
 outputs = model(**encoding)
 loss = outputs.loss
 start_scores = outputs.start_logits
 end_scores = outputs.end_logits
 start, end = word_ids[start_scores.argmax(-1)], word_ids[end_scores.argmax(-1)]
 print(" ".join(words[start : end + 1]))
M. Hamann P. Harper, P. Martinez
TFLayoutLMModel
	
class transformers.TFLayoutLMModel
<
source
>
(
*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.
TensorFlow models and layers in transformers accept two formats as input:
having all inputs as keyword arguments (like PyTorch models), or
having all inputs as a list, tuple or dict in the first positional argument.
The reason the second format is supported is that Keras methods prefer this format when passing inputs to models
and layers. Because of this support, when using methods like model.fit() things should “just work” for you - just
pass your inputs and labels in any format that model.fit() supports! If, however, you want to use the second
format outside of Keras methods like fit() and predict(), such as when creating your own layers or models with
the Keras Functional API, 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(input_ids)
a list of varying length with one or several input Tensors IN THE ORDER given in the docstring:
model([input_ids, attention_mask]) or model([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})
Note that when creating models and layers with
subclassing then you don’t need to worry
about any of this, as you can just pass inputs like you would to any other Python function!
call
<
source
>
(
input_ids: TFModelInputType | None = None
bbox: np.ndarray | tf.Tensor | None = None
attention_mask: np.ndarray | tf.Tensor | None = None
token_type_ids: np.ndarray | tf.Tensor | None = None
position_ids: np.ndarray | tf.Tensor | None = None
head_mask: np.ndarray | tf.Tensor | None = None
inputs_embeds: np.ndarray | tf.Tensor | None = None
encoder_hidden_states: np.ndarray | tf.Tensor | None = None
encoder_attention_mask: np.ndarray | tf.Tensor | None = None
output_attentions: Optional[bool] = None
output_hidden_states: Optional[bool] = None
return_dict: Optional[bool] = None
training: Optional[bool] = False
)
→
transformers.modeling_tf_outputs.TFBaseModelOutputWithPoolingAndCrossAttentions or tuple(tf.Tensor)
Parameters 
input_ids (Numpy array or tf.Tensor of shape (batch_size, sequence_length)) —
Indices of input sequence tokens in the vocabulary.
Indices can be obtained using AutoTokenizer. See PreTrainedTokenizer.call() and
PreTrainedTokenizer.encode() for details.
What are input IDs?
bbox (Numpy array or tf.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 or tf.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.
What are attention masks?
token_type_ids (Numpy array or tf.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.
What are token type IDs?
position_ids (Numpy array or tf.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].
What are position IDs?
head_mask (Numpy array or tf.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 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) —
Whether or not to return the attentions tensors of all attention layers. See attentions under returned
tensors for more detail.
output_hidden_states (bool, optional) —
Whether or not to return the hidden states of all layers. See hidden_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 to False) —
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)) — Sequence of hidden-states at the output of the last layer of the model.
pooler_output (tf.Tensor 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.
This output is usually not a good summary of the semantic content of the input, you’re often better with
averaging or pooling the sequence of hidden-states for the whole input sequence.
past_key_values (List[tf.Tensor], optional, returned when use_cache=True is passed or when config.use_cache=True) — List of tf.Tensor of length config.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 when output_hidden_states=True is passed or when config.output_hidden_states=True) — Tuple of tf.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 when output_attentions=True is passed or when config.output_attentions=True) — Tuple of tf.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 when output_attentions=True is passed or when config.output_attentions=True) — Tuple of tf.Tensor (one for each layer) of shape (batch_size, num_heads, sequence_length, sequence_length).
Attentions weights of the decoder’s 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:
	Copied
 from transformers import AutoTokenizer, TFLayoutLMModel
 import tensorflow as tf
 tokenizer = AutoTokenizer.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
	
class transformers.TFLayoutLMForMaskedLM
<
source
>
(
*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.
TensorFlow models and layers in transformers accept two formats as input:
having all inputs as keyword arguments (like PyTorch models), or
having all inputs as a list, tuple or dict in the first positional argument.
The reason the second format is supported is that Keras methods prefer this format when passing inputs to models
and layers. Because of this support, when using methods like model.fit() things should “just work” for you - just
pass your inputs and labels in any format that model.fit() supports! If, however, you want to use the second
format outside of Keras methods like fit() and predict(), such as when creating your own layers or models with
the Keras Functional API, 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(input_ids)
a list of varying length with one or several input Tensors IN THE ORDER given in the docstring:
model([input_ids, attention_mask]) or model([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})
Note that when creating models and layers with
subclassing then you don’t need to worry
about any of this, as you can just pass inputs like you would to any other Python function!
call
<
source
>
(
input_ids: TFModelInputType | None = None
bbox: np.ndarray | tf.Tensor | None = None
attention_mask: np.ndarray | tf.Tensor | None = None
token_type_ids: np.ndarray | tf.Tensor | None = None
position_ids: np.ndarray | tf.Tensor | None = None
head_mask: np.ndarray | tf.Tensor | None = None
inputs_embeds: np.ndarray | tf.Tensor | None = None
output_attentions: Optional[bool] = None
output_hidden_states: Optional[bool] = None
return_dict: Optional[bool] = None
labels: np.ndarray | tf.Tensor | None = None
training: Optional[bool] = False
)
→
transformers.modeling_tf_outputs.TFMaskedLMOutput or tuple(tf.Tensor)
Parameters 
input_ids (Numpy array or tf.Tensor of shape (batch_size, sequence_length)) —
Indices of input sequence tokens in the vocabulary.
Indices can be obtained using AutoTokenizer. See PreTrainedTokenizer.call() and
PreTrainedTokenizer.encode() for details.
What are input IDs?
bbox (Numpy array or tf.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 or tf.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.
What are attention masks?
token_type_ids (Numpy array or tf.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.
What are token type IDs?
position_ids (Numpy array or tf.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].
What are position IDs?
head_mask (Numpy array or tf.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 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) —
Whether or not to return the attentions tensors of all attention layers. See attentions under returned
tensors for more detail.
output_hidden_states (bool, optional) —
Whether or not to return the hidden states of all layers. See hidden_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 to False) —
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 or np.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] (see input_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 when labels is provided) — Masked language modeling (MLM) loss.
logits (tf.Tensor 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(tf.Tensor), optional, returned when output_hidden_states=True is passed or when config.output_hidden_states=True) — Tuple of tf.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 when output_attentions=True is passed or when config.output_attentions=True) — Tuple of tf.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:
	Copied
 from transformers import AutoTokenizer, TFLayoutLMForMaskedLM
 import tensorflow as tf
 tokenizer = AutoTokenizer.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
	
class transformers.TFLayoutLMForSequenceClassification
<
source
>
(
*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.
TensorFlow models and layers in transformers accept two formats as input:
having all inputs as keyword arguments (like PyTorch models), or
having all inputs as a list, tuple or dict in the first positional argument.
The reason the second format is supported is that Keras methods prefer this format when passing inputs to models
and layers. Because of this support, when using methods like model.fit() things should “just work” for you - just
pass your inputs and labels in any format that model.fit() supports! If, however, you want to use the second
format outside of Keras methods like fit() and predict(), such as when creating your own layers or models with
the Keras Functional API, 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(input_ids)
a list of varying length with one or several input Tensors IN THE ORDER given in the docstring:
model([input_ids, attention_mask]) or model([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})
Note that when creating models and layers with
subclassing then you don’t need to worry
about any of this, as you can just pass inputs like you would to any other Python function!
call
<
source
>
(
input_ids: TFModelInputType | None = None
bbox: np.ndarray | tf.Tensor | None = None
attention_mask: np.ndarray | tf.Tensor | None = None
token_type_ids: np.ndarray | tf.Tensor | None = None
position_ids: np.ndarray | tf.Tensor | None = None
head_mask: np.ndarray | tf.Tensor | None = None
inputs_embeds: np.ndarray | tf.Tensor | None = None
output_attentions: Optional[bool] = None
output_hidden_states: Optional[bool] = None
return_dict: Optional[bool] = None
labels: np.ndarray | tf.Tensor | None = None
training: Optional[bool] = False
)
→
transformers.modeling_tf_outputs.TFSequenceClassifierOutput or tuple(tf.Tensor)
Parameters 
input_ids (Numpy array or tf.Tensor of shape (batch_size, sequence_length)) —
Indices of input sequence tokens in the vocabulary.
Indices can be obtained using AutoTokenizer. See PreTrainedTokenizer.call() and
PreTrainedTokenizer.encode() for details.
What are input IDs?
bbox (Numpy array or tf.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 or tf.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.
What are attention masks?
token_type_ids (Numpy array or tf.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.
What are token type IDs?
position_ids (Numpy array or tf.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].
What are position IDs?
head_mask (Numpy array or tf.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 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) —
Whether or not to return the attentions tensors of all attention layers. See attentions under returned
tensors for more detail.
output_hidden_states (bool, optional) —
Whether or not to return the hidden states of all layers. See hidden_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 to False) —
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 or np.ndarray of shape (batch_size,), optional) —
Labels for computing the sequence classification/regression loss. Indices should be in [0, ..., config.num_labels - 1]. If config.num_labels == 1 a regression loss is computed (Mean-Square loss), If
config.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 when labels is provided) — Classification (or regression if config.num_labels==1) loss.
logits (tf.Tensor of shape (batch_size, config.num_labels)) — Classification (or regression if config.num_labels==1) scores (before SoftMax).
hidden_states (tuple(tf.Tensor), optional, returned when output_hidden_states=True is passed or when config.output_hidden_states=True) — Tuple of tf.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 when output_attentions=True is passed or when config.output_attentions=True) — Tuple of tf.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:
	Copied
 from transformers import AutoTokenizer, TFLayoutLMForSequenceClassification
 import tensorflow as tf
 tokenizer = AutoTokenizer.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
	
class transformers.TFLayoutLMForTokenClassification
<
source
>
(
*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.
TensorFlow models and layers in transformers accept two formats as input:
having all inputs as keyword arguments (like PyTorch models), or
having all inputs as a list, tuple or dict in the first positional argument.
The reason the second format is supported is that Keras methods prefer this format when passing inputs to models
and layers. Because of this support, when using methods like model.fit() things should “just work” for you - just
pass your inputs and labels in any format that model.fit() supports! If, however, you want to use the second
format outside of Keras methods like fit() and predict(), such as when creating your own layers or models with
the Keras Functional API, 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(input_ids)
a list of varying length with one or several input Tensors IN THE ORDER given in the docstring:
model([input_ids, attention_mask]) or model([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})
Note that when creating models and layers with
subclassing then you don’t need to worry
about any of this, as you can just pass inputs like you would to any other Python function!
call
<
source
>
(
input_ids: TFModelInputType | None = None
bbox: np.ndarray | tf.Tensor | None = None
attention_mask: np.ndarray | tf.Tensor | None = None
token_type_ids: np.ndarray | tf.Tensor | None = None
position_ids: np.ndarray | tf.Tensor | None = None
head_mask: np.ndarray | tf.Tensor | None = None
inputs_embeds: np.ndarray | tf.Tensor | None = None
output_attentions: Optional[bool] = None
output_hidden_states: Optional[bool] = None
return_dict: Optional[bool] = None
labels: np.ndarray | tf.Tensor | None = None
training: Optional[bool] = False
)
→
transformers.modeling_tf_outputs.TFTokenClassifierOutput or tuple(tf.Tensor)
Parameters 
input_ids (Numpy array or tf.Tensor of shape (batch_size, sequence_length)) —
Indices of input sequence tokens in the vocabulary.
Indices can be obtained using AutoTokenizer. See PreTrainedTokenizer.call() and
PreTrainedTokenizer.encode() for details.
What are input IDs?
bbox (Numpy array or tf.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 or tf.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.
What are attention masks?
token_type_ids (Numpy array or tf.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.
What are token type IDs?
position_ids (Numpy array or tf.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].
What are position IDs?
head_mask (Numpy array or tf.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 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) —
Whether or not to return the attentions tensors of all attention layers. See attentions under returned
tensors for more detail.
output_hidden_states (bool, optional) —
Whether or not to return the hidden states of all layers. See hidden_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 to False) —
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 or np.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 when labels is provided)  — Classification loss.
logits (tf.Tensor of shape (batch_size, sequence_length, config.num_labels)) — Classification scores (before SoftMax).
hidden_states (tuple(tf.Tensor), optional, returned when output_hidden_states=True is passed or when config.output_hidden_states=True) — Tuple of tf.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 when output_attentions=True is passed or when config.output_attentions=True) — Tuple of tf.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:
	Copied
 import tensorflow as tf
 from transformers import AutoTokenizer, TFLayoutLMForTokenClassification
 tokenizer = AutoTokenizer.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
TFLayoutLMForQuestionAnswering
	
class transformers.TFLayoutLMForQuestionAnswering
<
source
>
(
*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 span classification head on top for extractive question-answering tasks such as
DocVQA (a linear layer on top of the final hidden-states output to compute span start logits and span end logits).
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.
TensorFlow models and layers in transformers accept two formats as input:
having all inputs as keyword arguments (like PyTorch models), or
having all inputs as a list, tuple or dict in the first positional argument.
The reason the second format is supported is that Keras methods prefer this format when passing inputs to models
and layers. Because of this support, when using methods like model.fit() things should “just work” for you - just
pass your inputs and labels in any format that model.fit() supports! If, however, you want to use the second
format outside of Keras methods like fit() and predict(), such as when creating your own layers or models with
the Keras Functional API, 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(input_ids)
a list of varying length with one or several input Tensors IN THE ORDER given in the docstring:
model([input_ids, attention_mask]) or model([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})
Note that when creating models and layers with
subclassing then you don’t need to worry
about any of this, as you can just pass inputs like you would to any other Python function!
call
<
source
>
(
input_ids: TFModelInputType | None = None
bbox: np.ndarray | tf.Tensor | None = None
attention_mask: np.ndarray | tf.Tensor | None = None
token_type_ids: np.ndarray | tf.Tensor | None = None
position_ids: np.ndarray | tf.Tensor | None = None
head_mask: np.ndarray | tf.Tensor | None = None
inputs_embeds: np.ndarray | tf.Tensor | None = None
output_attentions: Optional[bool] = None
output_hidden_states: Optional[bool] = None
return_dict: Optional[bool] = None
start_positions: np.ndarray | tf.Tensor | None = None
end_positions: np.ndarray | tf.Tensor | None = None
training: Optional[bool] = False
)
→
transformers.modeling_tf_outputs.TFQuestionAnsweringModelOutput or tuple(tf.Tensor)
Parameters 
input_ids (Numpy array or tf.Tensor of shape (batch_size, sequence_length)) —
Indices of input sequence tokens in the vocabulary.
Indices can be obtained using AutoTokenizer. See PreTrainedTokenizer.call() and
PreTrainedTokenizer.encode() for details.
What are input IDs?
bbox (Numpy array or tf.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 or tf.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.
What are attention masks?
token_type_ids (Numpy array or tf.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.
What are token type IDs?
position_ids (Numpy array or tf.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].
What are position IDs?
head_mask (Numpy array or tf.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 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) —
Whether or not to return the attentions tensors of all attention layers. See attentions under returned
tensors for more detail.
output_hidden_states (bool, optional) —
Whether or not to return the hidden states of all layers. See hidden_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 to False) —
Whether or not to use the model in training mode (some modules like dropout modules have different
behaviors between training and evaluation).
start_positions (tf.Tensor or np.ndarray of shape (batch_size,), optional) —
Labels for position (index) of the start of the labelled span for computing the token classification loss.
Positions are clamped to the length of the sequence (sequence_length). Position outside of the sequence
are not taken into account for computing the loss.
end_positions (tf.Tensor or np.ndarray of shape (batch_size,), optional) —
Labels for position (index) of the end of the labelled span for computing the token classification loss.
Positions are clamped to the length of the sequence (sequence_length). Position outside of the sequence
are not taken into account for computing the loss.
Returns
transformers.modeling_tf_outputs.TFQuestionAnsweringModelOutput or tuple(tf.Tensor)
A transformers.modeling_tf_outputs.TFQuestionAnsweringModelOutput 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 when start_positions and end_positions are provided) — Total span extraction loss is the sum of a Cross-Entropy for the start and end positions.
start_logits (tf.Tensor of shape (batch_size, sequence_length)) — Span-start scores (before SoftMax).
end_logits (tf.Tensor of shape (batch_size, sequence_length)) — Span-end scores (before SoftMax).
hidden_states (tuple(tf.Tensor), optional, returned when output_hidden_states=True is passed or when config.output_hidden_states=True) — Tuple of tf.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 when output_attentions=True is passed or when config.output_attentions=True) — Tuple of tf.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 TFLayoutLMForQuestionAnswering 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:
	Copied
 import tensorflow as tf
 from transformers import AutoTokenizer, TFLayoutLMForQuestionAnswering
 from datasets import load_dataset
 tokenizer = AutoTokenizer.from_pretrained("impira/layoutlm-document-qa", add_prefix_space=True)
 model = TFLayoutLMForQuestionAnswering.from_pretrained("impira/layoutlm-document-qa", revision="1e3ebac")
 dataset = load_dataset("nielsr/funsd", split="train")
 example = dataset[0]
 question = "what's his name?"
 words = example["words"]
 boxes = example["bboxes"]
 encoding = tokenizer(
...     question.split(), words, is_split_into_words=True, return_token_type_ids=True, return_tensors="tf"
... )
 bbox = []
 for i, s, w in zip(encoding.input_ids[0], encoding.sequence_ids(0), encoding.word_ids(0)):
...     if s == 1:
...         bbox.append(boxes[w])
...     elif i == tokenizer.sep_token_id:
...         bbox.append([1000] * 4)
...     else:
...         bbox.append([0] * 4)
 encoding["bbox"] = tf.convert_to_tensor([bbox])
 word_ids = encoding.word_ids(0)
 outputs = model(**encoding)
 loss = outputs.loss
 start_scores = outputs.start_logits
 end_scores = outputs.end_logits
 start, end = word_ids[tf.math.argmax(start_scores, -1)[0]], word_ids[tf.math.argmax(end_scores, -1)[0]]
 print(" ".join(words[start : end + 1]))
M. Hamann P. Harper, P. Martinez
←InstructBLIP
LayoutLMV2→
LayoutLM
Overview
Resources
LayoutLMConfig
LayoutLMTokenizer
LayoutLMTokenizerFast
LayoutLMModel
LayoutLMForMaskedLM
LayoutLMForSequenceClassification
LayoutLMForTokenClassification
LayoutLMForQuestionAnswering
TFLayoutLMModel
TFLayoutLMForMaskedLM
TFLayoutLMForSequenceClassification
TFLayoutLMForTokenClassification
TFLayoutLMForQuestionAnswering
