
 
LayoutLMV2
	
Overview
	
The LayoutLMV2 model was proposed in LayoutLMv2: Multi-modal Pre-training for Visually-Rich Document Understanding by Yang Xu, Yiheng Xu, Tengchao Lv, Lei Cui, Furu Wei, Guoxin Wang, Yijuan Lu,
Dinei Florencio, Cha Zhang, Wanxiang Che, Min Zhang, Lidong Zhou. LayoutLMV2 improves LayoutLM to obtain
state-of-the-art results across several document image understanding benchmarks:
information extraction from scanned documents: the FUNSD dataset (a
collection of 199 annotated forms comprising more than 30,000 words), the CORD
dataset (a collection of 800 receipts for training, 100 for validation and 100 for testing), the SROIE dataset (a collection of 626 receipts for training and 347 receipts for testing)
and the Kleister-NDA dataset (a collection of non-disclosure
agreements from the EDGAR database, including 254 documents for training, 83 documents for validation, and 203
documents for testing).
document image classification: the RVL-CDIP dataset (a collection of
400,000 images belonging to one of 16 classes).
document visual question answering: the DocVQA dataset (a collection of 50,000
questions defined on 12,000+ document images).
The abstract from the paper is the following:
Pre-training of text and layout has proved effective in a variety of visually-rich document understanding tasks due to
its effective model architecture and the advantage of large-scale unlabeled scanned/digital-born documents. In this
paper, we present LayoutLMv2 by pre-training text, layout and image in a multi-modal framework, where new model
architectures and pre-training tasks are leveraged. Specifically, LayoutLMv2 not only uses the existing masked
visual-language modeling task but also the new text-image alignment and text-image matching tasks in the pre-training
stage, where cross-modality interaction is better learned. Meanwhile, it also integrates a spatial-aware self-attention
mechanism into the Transformer architecture, so that the model can fully understand the relative positional
relationship among different text blocks. Experiment results show that LayoutLMv2 outperforms strong baselines and
achieves new state-of-the-art results on a wide variety of downstream visually-rich document understanding tasks,
including FUNSD (0.7895 -> 0.8420), CORD (0.9493 -> 0.9601), SROIE (0.9524 -> 0.9781), Kleister-NDA (0.834 -> 0.852),
RVL-CDIP (0.9443 -> 0.9564), and DocVQA (0.7295 -> 0.8672). The pre-trained LayoutLMv2 model is publicly available at
this https URL.
LayoutLMv2 depends on detectron2, torchvision and tesseract. Run the
following to install them:
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python -m pip install 'git+https://github.com/facebookresearch/detectron2.git'
python -m pip install torchvision tesseract
(If you are developing for LayoutLMv2, note that passing the doctests also requires the installation of these packages.)
Tips:
The main difference between LayoutLMv1 and LayoutLMv2 is that the latter incorporates visual embeddings during
pre-training (while LayoutLMv1 only adds visual embeddings during fine-tuning).
LayoutLMv2 adds both a relative 1D attention bias as well as a spatial 2D attention bias to the attention scores in
the self-attention layers. Details can be found on page 5 of the paper.
Demo notebooks on how to use the LayoutLMv2 model on RVL-CDIP, FUNSD, DocVQA, CORD can be found here.
LayoutLMv2 uses Facebook AI’s Detectron2 package for its visual
backbone. See this link for installation
instructions.
In addition to input_ids, forward() expects 2 additional inputs, namely
image and bbox. The image input corresponds to the original document image in which the text
tokens occur. The model expects each document image to be of size 224x224. This means that if you have a batch of
document images, image should be a tensor of shape (batch_size, 3, 224, 224). This can be either a
torch.Tensor or a Detectron2.structures.ImageList. You don’t need to normalize the channels, as this is
done by the model. Important to note is that the visual backbone expects BGR channels instead of RGB, as all models
in Detectron2 are pre-trained using the BGR format. The bbox input are the bounding boxes (i.e. 2D-positions)
of the input text tokens. This is identical to LayoutLMModel. 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:
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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 (before resizing the image). Those can be obtained using the Python Image Library (PIL) library for example, as
follows:
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from PIL import Image
image = Image.open(
    "name_of_your_document - can be a png, jpg, etc. of your documents (PDFs must be converted to images)."
)
width, height = image.size
However, this model includes a brand new LayoutLMv2Processor which can be used to directly
prepare data for the model (including applying OCR under the hood). More information can be found in the “Usage”
section below.
Internally, LayoutLMv2Model will send the image input through its visual backbone to
obtain a lower-resolution feature map, whose shape is equal to the image_feature_pool_shape attribute of
LayoutLMv2Config. This feature map is then flattened to obtain a sequence of image tokens. As
the size of the feature map is 7x7 by default, one obtains 49 image tokens. These are then concatenated with the text
tokens, and send through the Transformer encoder. This means that the last hidden states of the model will have a
length of 512 + 49 = 561, if you pad the text tokens up to the max length. More generally, the last hidden states
will have a shape of seq_length + image_feature_pool_shape[0] *
config.image_feature_pool_shape[1].
When calling from_pretrained(), a warning will be printed with a long list of
parameter names that are not initialized. This is not a problem, as these parameters are batch normalization
statistics, which are going to have values when fine-tuning on a custom dataset.
If you want to train the model in a distributed environment, make sure to call synchronize_batch_norm on the
model in order to properly synchronize the batch normalization layers of the visual backbone.
In addition, there’s LayoutXLM, which is a multilingual version of LayoutLMv2. More information can be found on
LayoutXLM’s documentation page.
Resources
	
A list of official Hugging Face and community (indicated by 🌎) resources to help you get started with LayoutLMv2. 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.
Text Classification
A notebook on how to finetune LayoutLMv2 for text-classification on RVL-CDIP dataset.
See also: Text classification task guide
Question Answering
A notebook on how to finetune LayoutLMv2 for question-answering on DocVQA dataset.
See also: Question answering task guide
See also: Document question answering task guide
Token Classification
A notebook on how to finetune LayoutLMv2 for token-classification on CORD dataset.
A notebook on how to finetune LayoutLMv2 for token-classification on FUNSD dataset.
See also: Token classification task guide
Usage: LayoutLMv2Processor
	
The easiest way to prepare data for the model is to use LayoutLMv2Processor, which internally
combines a image processor (LayoutLMv2ImageProcessor) and a tokenizer
(LayoutLMv2Tokenizer or LayoutLMv2TokenizerFast). The image processor
handles the image modality, while the tokenizer handles the text modality. A processor combines both, which is ideal
for a multi-modal model like LayoutLMv2. Note that you can still use both separately, if you only want to handle one
modality.
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from transformers import LayoutLMv2ImageProcessor, LayoutLMv2TokenizerFast, LayoutLMv2Processor
image_processor = LayoutLMv2ImageProcessor()  # apply_ocr is set to True by default
tokenizer = LayoutLMv2TokenizerFast.from_pretrained("microsoft/layoutlmv2-base-uncased")
processor = LayoutLMv2Processor(image_processor, tokenizer)
In short, one can provide a document image (and possibly additional data) to LayoutLMv2Processor,
and it will create the inputs expected by the model. Internally, the processor first uses
LayoutLMv2ImageProcessor to apply OCR on the image to get a list of words and normalized
bounding boxes, as well to resize the image to a given size in order to get the image input. The words and
normalized bounding boxes are then provided to LayoutLMv2Tokenizer or
LayoutLMv2TokenizerFast, which converts them to token-level input_ids,
attention_mask, token_type_ids, bbox. Optionally, one can provide word labels to the processor,
which are turned into token-level labels.
LayoutLMv2Processor uses PyTesseract, a Python
wrapper around Google’s Tesseract OCR engine, under the hood. Note that you can still use your own OCR engine of
choice, and provide the words and normalized boxes yourself. This requires initializing
LayoutLMv2ImageProcessor with apply_ocr set to False.
In total, there are 5 use cases that are supported by the processor. Below, we list them all. Note that each of these
use cases work for both batched and non-batched inputs (we illustrate them for non-batched inputs).
Use case 1: document image classification (training, inference) + token classification (inference), apply_ocr =
True
This is the simplest case, in which the processor (actually the image processor) will perform OCR on the image to get
the words and normalized bounding boxes.
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from transformers import LayoutLMv2Processor
from PIL import Image
processor = LayoutLMv2Processor.from_pretrained("microsoft/layoutlmv2-base-uncased")
image = Image.open(
    "name_of_your_document - can be a png, jpg, etc. of your documents (PDFs must be converted to images)."
).convert("RGB")
encoding = processor(
    image, return_tensors="pt"
)  # you can also add all tokenizer parameters here such as padding, truncation
print(encoding.keys())
# dict_keys(['input_ids', 'token_type_ids', 'attention_mask', 'bbox', 'image'])
Use case 2: document image classification (training, inference) + token classification (inference), apply_ocr=False
In case one wants to do OCR themselves, one can initialize the image processor with apply_ocr set to
False. In that case, one should provide the words and corresponding (normalized) bounding boxes themselves to
the processor.
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from transformers import LayoutLMv2Processor
from PIL import Image
processor = LayoutLMv2Processor.from_pretrained("microsoft/layoutlmv2-base-uncased", revision="no_ocr")
image = Image.open(
    "name_of_your_document - can be a png, jpg, etc. of your documents (PDFs must be converted to images)."
).convert("RGB")
words = ["hello", "world"]
boxes = [[1, 2, 3, 4], [5, 6, 7, 8]]  # make sure to normalize your bounding boxes
encoding = processor(image, words, boxes=boxes, return_tensors="pt")
print(encoding.keys())
# dict_keys(['input_ids', 'token_type_ids', 'attention_mask', 'bbox', 'image'])
Use case 3: token classification (training), apply_ocr=False
For token classification tasks (such as FUNSD, CORD, SROIE, Kleister-NDA), one can also provide the corresponding word
labels in order to train a model. The processor will then convert these into token-level labels. By default, it
will only label the first wordpiece of a word, and label the remaining wordpieces with -100, which is the
ignore_index of PyTorch’s CrossEntropyLoss. In case you want all wordpieces of a word to be labeled, you can
initialize the tokenizer with only_label_first_subword set to False.
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from transformers import LayoutLMv2Processor
from PIL import Image
processor = LayoutLMv2Processor.from_pretrained("microsoft/layoutlmv2-base-uncased", revision="no_ocr")
image = Image.open(
    "name_of_your_document - can be a png, jpg, etc. of your documents (PDFs must be converted to images)."
).convert("RGB")
words = ["hello", "world"]
boxes = [[1, 2, 3, 4], [5, 6, 7, 8]]  # make sure to normalize your bounding boxes
word_labels = [1, 2]
encoding = processor(image, words, boxes=boxes, word_labels=word_labels, return_tensors="pt")
print(encoding.keys())
# dict_keys(['input_ids', 'token_type_ids', 'attention_mask', 'bbox', 'labels', 'image'])
Use case 4: visual question answering (inference), apply_ocr=True
For visual question answering tasks (such as DocVQA), you can provide a question to the processor. By default, the
processor will apply OCR on the image, and create [CLS] question tokens [SEP] word tokens [SEP].
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from transformers import LayoutLMv2Processor
from PIL import Image
processor = LayoutLMv2Processor.from_pretrained("microsoft/layoutlmv2-base-uncased")
image = Image.open(
    "name_of_your_document - can be a png, jpg, etc. of your documents (PDFs must be converted to images)."
).convert("RGB")
question = "What's his name?"
encoding = processor(image, question, return_tensors="pt")
print(encoding.keys())
# dict_keys(['input_ids', 'token_type_ids', 'attention_mask', 'bbox', 'image'])
Use case 5: visual question answering (inference), apply_ocr=False
For visual question answering tasks (such as DocVQA), you can provide a question to the processor. If you want to
perform OCR yourself, you can provide your own words and (normalized) bounding boxes to the processor.
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from transformers import LayoutLMv2Processor
from PIL import Image
processor = LayoutLMv2Processor.from_pretrained("microsoft/layoutlmv2-base-uncased", revision="no_ocr")
image = Image.open(
    "name_of_your_document - can be a png, jpg, etc. of your documents (PDFs must be converted to images)."
).convert("RGB")
question = "What's his name?"
words = ["hello", "world"]
boxes = [[1, 2, 3, 4], [5, 6, 7, 8]]  # make sure to normalize your bounding boxes
encoding = processor(image, question, words, boxes=boxes, return_tensors="pt")
print(encoding.keys())
# dict_keys(['input_ids', 'token_type_ids', 'attention_mask', 'bbox', 'image'])
LayoutLMv2Config
	
class transformers.LayoutLMv2Config
<
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
max_2d_position_embeddings = 1024
max_rel_pos = 128
rel_pos_bins = 32
fast_qkv = True
max_rel_2d_pos = 256
rel_2d_pos_bins = 64
convert_sync_batchnorm = True
image_feature_pool_shape = [7, 7, 256]
coordinate_size = 128
shape_size = 128
has_relative_attention_bias = True
has_spatial_attention_bias = True
has_visual_segment_embedding = False
detectron2_config_args = None
**kwargs
)
Parameters 
vocab_size (int, optional, defaults to 30522) —
Vocabulary size of the LayoutLMv2 model. Defines the number of different tokens that can be represented by
the inputs_ids passed when calling LayoutLMv2Model or TFLayoutLMv2Model.
hidden_size (int, optional, defaults to 768) —
Dimension 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) —
Dimension 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", "selu" and "gelu_new" are supported.
hidden_dropout_prob (float, optional, defaults to 0.1) —
The dropout probabilitiy for all fully connected layers in the embeddings, encoder, and pooler.
attention_probs_dropout_prob (float, optional, defaults to 0.1) —
The dropout ratio for the attention probabilities.
max_position_embeddings (int, optional, defaults to 512) —
The maximum sequence length that this model might ever be used with. Typically set this to something large
just in case (e.g., 512 or 1024 or 2048).
type_vocab_size (int, optional, defaults to 2) —
The vocabulary size of the token_type_ids passed when calling LayoutLMv2Model or
TFLayoutLMv2Model.
initializer_range (float, optional, defaults to 0.02) —
The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
layer_norm_eps (float, optional, defaults to 1e-12) —
The epsilon used by the layer normalization layers.
max_2d_position_embeddings (int, optional, defaults to 1024) —
The maximum value that the 2D position embedding might ever be used with. Typically set this to something
large just in case (e.g., 1024).
max_rel_pos (int, optional, defaults to 128) —
The maximum number of relative positions to be used in the self-attention mechanism.
rel_pos_bins (int, optional, defaults to 32) —
The number of relative position bins to be used in the self-attention mechanism.
fast_qkv (bool, optional, defaults to True) —
Whether or not to use a single matrix for the queries, keys, values in the self-attention layers.
max_rel_2d_pos (int, optional, defaults to 256) —
The maximum number of relative 2D positions in the self-attention mechanism.
rel_2d_pos_bins (int, optional, defaults to 64) —
The number of 2D relative position bins in the self-attention mechanism.
image_feature_pool_shape (List[int], optional, defaults to [7, 7, 256]) —
The shape of the average-pooled feature map.
coordinate_size (int, optional, defaults to 128) —
Dimension of the coordinate embeddings.
shape_size (int, optional, defaults to 128) —
Dimension of the width and height embeddings.
has_relative_attention_bias (bool, optional, defaults to True) —
Whether or not to use a relative attention bias in the self-attention mechanism.
has_spatial_attention_bias (bool, optional, defaults to True) —
Whether or not to use a spatial attention bias in the self-attention mechanism.
has_visual_segment_embedding (bool, optional, defaults to False) —
Whether or not to add visual segment embeddings.
detectron2_config_args (dict, optional) —
Dictionary containing the configuration arguments of the Detectron2 visual backbone. Refer to this
file
for details regarding default values.
This is the configuration class to store the configuration of a LayoutLMv2Model. It is used to instantiate an
LayoutLMv2 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 LayoutLMv2
microsoft/layoutlmv2-base-uncased architecture.
Configuration objects inherit from PretrainedConfig and can be used to control the model outputs. Read the
documentation from PretrainedConfig for more information.
Example:
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 from transformers import LayoutLMv2Config, LayoutLMv2Model
 # Initializing a LayoutLMv2 microsoft/layoutlmv2-base-uncased style configuration
 configuration = LayoutLMv2Config()
 # Initializing a model (with random weights) from the microsoft/layoutlmv2-base-uncased style configuration
 model = LayoutLMv2Model(configuration)
 # Accessing the model configuration
 configuration = model.config
LayoutLMv2FeatureExtractor
	
class transformers.LayoutLMv2FeatureExtractor
<
source
>
(
*args
**kwargs
)
__call__
<
source
>
(
images
**kwargs
)
Preprocess an image or a batch of images.
LayoutLMv2ImageProcessor
	
class transformers.LayoutLMv2ImageProcessor
<
source
>
(
do_resize: bool = True
size: typing.Dict[str, int] = None
resample: Resampling = <Resampling.BILINEAR: 2>
apply_ocr: bool = True
ocr_lang: typing.Optional[str] = None
tesseract_config: typing.Optional[str] = ''
**kwargs
)
Parameters 
do_resize (bool, optional, defaults to True) —
Whether to resize the image’s (height, width) dimensions to (size["height"], size["width"]). Can be
overridden by do_resize in preprocess.
size (Dict[str, int] optional, defaults to {"height" -- 224, "width": 224}):
Size of the image after resizing. Can be overridden by size in preprocess.
resample (PILImageResampling, optional, defaults to PILImageResampling.BILINEAR) —
Resampling filter to use if resizing the image. Can be overridden by the resample parameter in the
preprocess method.
apply_ocr (bool, optional, defaults to True) —
Whether to apply the Tesseract OCR engine to get words + normalized bounding boxes. Can be overridden by
apply_ocr in preprocess.
ocr_lang (str, optional) —
The language, specified by its ISO code, to be used by the Tesseract OCR engine. By default, English is
used. Can be overridden by ocr_lang in preprocess.
tesseract_config (str, optional) —
Any additional custom configuration flags that are forwarded to the config parameter when calling
Tesseract. For example: ‘—psm 6’. Can be overridden by tesseract_config in preprocess.
Constructs a LayoutLMv2 image processor.
preprocess
<
source
>
(
images: typing.Union[ForwardRef('PIL.Image.Image'), numpy.ndarray, ForwardRef('torch.Tensor'), typing.List[ForwardRef('PIL.Image.Image')], typing.List[numpy.ndarray], typing.List[ForwardRef('torch.Tensor')]]
do_resize: bool = None
size: typing.Dict[str, int] = None
resample: Resampling = None
apply_ocr: bool = None
ocr_lang: typing.Optional[str] = None
tesseract_config: typing.Optional[str] = None
return_tensors: typing.Union[str, transformers.utils.generic.TensorType, NoneType] = None
data_format: ChannelDimension = <ChannelDimension.FIRST: 'channels_first'>
**kwargs
)
Parameters 
images (ImageInput) —
Image to preprocess.
do_resize (bool, optional, defaults to self.do_resize) —
Whether to resize the image.
size (Dict[str, int], optional, defaults to self.size) —
Desired size of the output image after resizing.
resample (PILImageResampling, optional, defaults to self.resample) —
Resampling filter to use if resizing the image. This can be one of the enum PIL.Image resampling
filter. Only has an effect if do_resize is set to True.
apply_ocr (bool, optional, defaults to self.apply_ocr) —
Whether to apply the Tesseract OCR engine to get words + normalized bounding boxes.
ocr_lang (str, optional, defaults to self.ocr_lang) —
The language, specified by its ISO code, to be used by the Tesseract OCR engine. By default, English is
used.
tesseract_config (str, optional, defaults to self.tesseract_config) —
Any additional custom configuration flags that are forwarded to the config parameter when calling
Tesseract.
return_tensors (str or TensorType, optional) —
The type of tensors to return. Can be one of:
Unset: Return a list of np.ndarray.
TensorType.TENSORFLOW or 'tf': Return a batch of type tf.Tensor.
TensorType.PYTORCH or 'pt': Return a batch of type torch.Tensor.
TensorType.NUMPY or 'np': Return a batch of type np.ndarray.
TensorType.JAX or 'jax': Return a batch of type jax.numpy.ndarray.
data_format (ChannelDimension or str, optional, defaults to ChannelDimension.FIRST) —
The channel dimension format for the output image. Can be one of:
ChannelDimension.FIRST: image in (num_channels, height, width) format.
ChannelDimension.LAST: image in (height, width, num_channels) format.
Preprocess an image or batch of images.
LayoutLMv2Tokenizer
	
class transformers.LayoutLMv2Tokenizer
<
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]'
cls_token_box = [0, 0, 0, 0]
sep_token_box = [1000, 1000, 1000, 1000]
pad_token_box = [0, 0, 0, 0]
pad_token_label = -100
only_label_first_subword = True
tokenize_chinese_chars = True
strip_accents = None
model_max_length: int = 512
additional_special_tokens: typing.Optional[typing.List[str]] = None
**kwargs
)
Construct a LayoutLMv2 tokenizer. Based on WordPiece. LayoutLMv2Tokenizer can be used to turn words, word-level
bounding boxes and optional word labels to token-level input_ids, attention_mask, token_type_ids, bbox, and
optional labels (for token classification).
This tokenizer inherits from PreTrainedTokenizer which contains most of the main methods. Users should refer to
this superclass for more information regarding those methods.
LayoutLMv2Tokenizer runs end-to-end tokenization: punctuation splitting and wordpiece. It also turns the
word-level bounding boxes into token-level bounding boxes.
__call__
<
source
>
(
text: typing.Union[str, typing.List[str], typing.List[typing.List[str]]]
text_pair: typing.Union[typing.List[str], typing.List[typing.List[str]], NoneType] = None
boxes: typing.Union[typing.List[typing.List[int]], typing.List[typing.List[typing.List[int]]]] = None
word_labels: typing.Union[typing.List[int], typing.List[typing.List[int]], NoneType] = None
add_special_tokens: bool = True
padding: typing.Union[bool, str, transformers.utils.generic.PaddingStrategy] = False
truncation: typing.Union[bool, str, transformers.tokenization_utils_base.TruncationStrategy] = None
max_length: typing.Optional[int] = None
stride: int = 0
pad_to_multiple_of: typing.Optional[int] = None
return_tensors: typing.Union[str, transformers.utils.generic.TensorType, NoneType] = None
return_token_type_ids: typing.Optional[bool] = None
return_attention_mask: typing.Optional[bool] = None
return_overflowing_tokens: bool = False
return_special_tokens_mask: bool = False
return_offsets_mapping: bool = False
return_length: bool = False
verbose: bool = True
**kwargs
)
→
BatchEncoding
Parameters 
text (str, List[str], List[List[str]]) —
The sequence or batch of sequences to be encoded. Each sequence can be a string, a list of strings
(words of a single example or questions of a batch of examples) or a list of list of strings (batch of
words).
text_pair (List[str], List[List[str]]) —
The sequence or batch of sequences to be encoded. Each sequence should be a list of strings
(pretokenized string).
boxes (List[List[int]], List[List[List[int]]]) —
Word-level bounding boxes. Each bounding box should be normalized to be on a 0-1000 scale.
word_labels (List[int], List[List[int]], optional) —
Word-level integer labels (for token classification tasks such as FUNSD, CORD).
add_special_tokens (bool, optional, defaults to True) —
Whether or not to encode the sequences with the special tokens relative to their model.
padding (bool, str or PaddingStrategy, optional, defaults to False) —
Activates and controls padding. Accepts the following values:
True or 'longest': Pad to the longest sequence in the batch (or no padding if only a single
sequence if provided).
'max_length': Pad to a maximum length specified with the argument max_length or to the maximum
acceptable input length for the model if that argument is not provided.
False or 'do_not_pad' (default): No padding (i.e., can output a batch with sequences of different
lengths).
truncation (bool, str or TruncationStrategy, optional, defaults to False) —
Activates and controls truncation. Accepts the following values:
True or 'longest_first': Truncate to a maximum length specified with the argument max_length or
to the maximum acceptable input length for the model if that argument is not provided. This will
truncate token by token, removing a token from the longest sequence in the pair if a pair of
sequences (or a batch of pairs) is provided.
'only_first': Truncate to a maximum length specified with the argument max_length or to the
maximum acceptable input length for the model if that argument is not provided. This will only
truncate the first sequence of a pair if a pair of sequences (or a batch of pairs) is provided.
'only_second': Truncate to a maximum length specified with the argument max_length or to the
maximum acceptable input length for the model if that argument is not provided. This will only
truncate the second sequence of a pair if a pair of sequences (or a batch of pairs) is provided.
False or 'do_not_truncate' (default): No truncation (i.e., can output batch with sequence lengths
greater than the model maximum admissible input size).
max_length (int, optional) —
Controls the maximum length to use by one of the truncation/padding parameters.
If left unset or set to None, this will use the predefined model maximum length if a maximum length
is required by one of the truncation/padding parameters. If the model has no specific maximum input
length (like XLNet) truncation/padding to a maximum length will be deactivated.
stride (int, optional, defaults to 0) —
If set to a number along with max_length, the overflowing tokens returned when
return_overflowing_tokens=True will contain some tokens from the end of the truncated sequence
returned to provide some overlap between truncated and overflowing sequences. The value of this
argument defines the number of overlapping tokens.
pad_to_multiple_of (int, optional) —
If set will pad the sequence to a multiple of the provided value. This is especially useful to enable
the use of Tensor Cores on NVIDIA hardware with compute capability >= 7.5 (Volta).
return_tensors (str or TensorType, optional) —
If set, will return tensors instead of list of python integers. Acceptable values are:
'tf': Return TensorFlow tf.constant objects.
'pt': Return PyTorch torch.Tensor objects.
'np': Return Numpy np.ndarray objects.
return_token_type_ids (bool, optional) —
Whether to return token type IDs. If left to the default, will return the token type IDs according to
the specific tokenizer’s default, defined by the return_outputs attribute.
What are token type IDs?
return_attention_mask (bool, optional) —
Whether to return the attention mask. If left to the default, will return the attention mask according
to the specific tokenizer’s default, defined by the return_outputs attribute.
What are attention masks?
return_overflowing_tokens (bool, optional, defaults to False) —
Whether or not to return overflowing token sequences. If a pair of sequences of input ids (or a batch
of pairs) is provided with truncation_strategy = longest_first or True, an error is raised instead
of returning overflowing tokens.
return_special_tokens_mask (bool, optional, defaults to False) —
Whether or not to return special tokens mask information.
return_offsets_mapping (bool, optional, defaults to False) —
Whether or not to return (char_start, char_end) for each token.
This is only available on fast tokenizers inheriting from PreTrainedTokenizerFast, if using
Python’s tokenizer, this method will raise NotImplementedError.
return_length  (bool, optional, defaults to False) —
Whether or not to return the lengths of the encoded inputs.
verbose (bool, optional, defaults to True) —
Whether or not to print more information and warnings.
**kwargs — passed to the self.tokenize() method
Returns
BatchEncoding
A BatchEncoding with the following fields:
input_ids — List of token ids to be fed to a model.
What are input IDs?
bbox — List of bounding boxes to be fed to a model.
token_type_ids — List of token type ids to be fed to a model (when return_token_type_ids=True or
if “token_type_ids” is in self.model_input_names).
What are token type IDs?
attention_mask — List of indices specifying which tokens should be attended to by the model (when
return_attention_mask=True or if “attention_mask” is in self.model_input_names).
What are attention masks?
labels — List of labels to be fed to a model. (when word_labels is specified).
overflowing_tokens — List of overflowing tokens sequences (when a max_length is specified and
return_overflowing_tokens=True).
num_truncated_tokens — Number of tokens truncated (when a max_length is specified and
return_overflowing_tokens=True).
special_tokens_mask — List of 0s and 1s, with 1 specifying added special tokens and 0 specifying
regular sequence tokens (when add_special_tokens=True and return_special_tokens_mask=True).
length — The length of the inputs (when return_length=True).
Main method to tokenize and prepare for the model one or several sequence(s) or one or several pair(s) of
sequences with word-level normalized bounding boxes and optional labels.
save_vocabulary
<
source
>
(
save_directory: str
filename_prefix: typing.Optional[str] = None
)
LayoutLMv2TokenizerFast
	
class transformers.LayoutLMv2TokenizerFast
<
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]'
cls_token_box = [0, 0, 0, 0]
sep_token_box = [1000, 1000, 1000, 1000]
pad_token_box = [0, 0, 0, 0]
pad_token_label = -100
only_label_first_subword = True
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.
cls_token_box (List[int], optional, defaults to [0, 0, 0, 0]) —
The bounding box to use for the special [CLS] token.
sep_token_box (List[int], optional, defaults to [1000, 1000, 1000, 1000]) —
The bounding box to use for the special [SEP] token.
pad_token_box (List[int], optional, defaults to [0, 0, 0, 0]) —
The bounding box to use for the special [PAD] token.
pad_token_label (int, optional, defaults to -100) —
The label to use for padding tokens. Defaults to -100, which is the ignore_index of PyTorch’s
CrossEntropyLoss.
only_label_first_subword (bool, optional, defaults to True) —
Whether or not to only label the first subword, in case word labels are provided.
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 LayoutLMv2).
Construct a “fast” LayoutLMv2 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.
__call__
<
source
>
(
text: typing.Union[str, typing.List[str], typing.List[typing.List[str]]]
text_pair: typing.Union[typing.List[str], typing.List[typing.List[str]], NoneType] = None
boxes: typing.Union[typing.List[typing.List[int]], typing.List[typing.List[typing.List[int]]]] = None
word_labels: typing.Union[typing.List[int], typing.List[typing.List[int]], NoneType] = None
add_special_tokens: bool = True
padding: typing.Union[bool, str, transformers.utils.generic.PaddingStrategy] = False
truncation: typing.Union[bool, str, transformers.tokenization_utils_base.TruncationStrategy] = None
max_length: typing.Optional[int] = None
stride: int = 0
pad_to_multiple_of: typing.Optional[int] = None
return_tensors: typing.Union[str, transformers.utils.generic.TensorType, NoneType] = None
return_token_type_ids: typing.Optional[bool] = None
return_attention_mask: typing.Optional[bool] = None
return_overflowing_tokens: bool = False
return_special_tokens_mask: bool = False
return_offsets_mapping: bool = False
return_length: bool = False
verbose: bool = True
**kwargs
)
→
BatchEncoding
Parameters 
text (str, List[str], List[List[str]]) —
The sequence or batch of sequences to be encoded. Each sequence can be a string, a list of strings
(words of a single example or questions of a batch of examples) or a list of list of strings (batch of
words).
text_pair (List[str], List[List[str]]) —
The sequence or batch of sequences to be encoded. Each sequence should be a list of strings
(pretokenized string).
boxes (List[List[int]], List[List[List[int]]]) —
Word-level bounding boxes. Each bounding box should be normalized to be on a 0-1000 scale.
word_labels (List[int], List[List[int]], optional) —
Word-level integer labels (for token classification tasks such as FUNSD, CORD).
add_special_tokens (bool, optional, defaults to True) —
Whether or not to encode the sequences with the special tokens relative to their model.
padding (bool, str or PaddingStrategy, optional, defaults to False) —
Activates and controls padding. Accepts the following values:
True or 'longest': Pad to the longest sequence in the batch (or no padding if only a single
sequence if provided).
'max_length': Pad to a maximum length specified with the argument max_length or to the maximum
acceptable input length for the model if that argument is not provided.
False or 'do_not_pad' (default): No padding (i.e., can output a batch with sequences of different
lengths).
truncation (bool, str or TruncationStrategy, optional, defaults to False) —
Activates and controls truncation. Accepts the following values:
True or 'longest_first': Truncate to a maximum length specified with the argument max_length or
to the maximum acceptable input length for the model if that argument is not provided. This will
truncate token by token, removing a token from the longest sequence in the pair if a pair of
sequences (or a batch of pairs) is provided.
'only_first': Truncate to a maximum length specified with the argument max_length or to the
maximum acceptable input length for the model if that argument is not provided. This will only
truncate the first sequence of a pair if a pair of sequences (or a batch of pairs) is provided.
'only_second': Truncate to a maximum length specified with the argument max_length or to the
maximum acceptable input length for the model if that argument is not provided. This will only
truncate the second sequence of a pair if a pair of sequences (or a batch of pairs) is provided.
False or 'do_not_truncate' (default): No truncation (i.e., can output batch with sequence lengths
greater than the model maximum admissible input size).
max_length (int, optional) —
Controls the maximum length to use by one of the truncation/padding parameters.
If left unset or set to None, this will use the predefined model maximum length if a maximum length
is required by one of the truncation/padding parameters. If the model has no specific maximum input
length (like XLNet) truncation/padding to a maximum length will be deactivated.
stride (int, optional, defaults to 0) —
If set to a number along with max_length, the overflowing tokens returned when
return_overflowing_tokens=True will contain some tokens from the end of the truncated sequence
returned to provide some overlap between truncated and overflowing sequences. The value of this
argument defines the number of overlapping tokens.
pad_to_multiple_of (int, optional) —
If set will pad the sequence to a multiple of the provided value. This is especially useful to enable
the use of Tensor Cores on NVIDIA hardware with compute capability >= 7.5 (Volta).
return_tensors (str or TensorType, optional) —
If set, will return tensors instead of list of python integers. Acceptable values are:
'tf': Return TensorFlow tf.constant objects.
'pt': Return PyTorch torch.Tensor objects.
'np': Return Numpy np.ndarray objects.
return_token_type_ids (bool, optional) —
Whether to return token type IDs. If left to the default, will return the token type IDs according to
the specific tokenizer’s default, defined by the return_outputs attribute.
What are token type IDs?
return_attention_mask (bool, optional) —
Whether to return the attention mask. If left to the default, will return the attention mask according
to the specific tokenizer’s default, defined by the return_outputs attribute.
What are attention masks?
return_overflowing_tokens (bool, optional, defaults to False) —
Whether or not to return overflowing token sequences. If a pair of sequences of input ids (or a batch
of pairs) is provided with truncation_strategy = longest_first or True, an error is raised instead
of returning overflowing tokens.
return_special_tokens_mask (bool, optional, defaults to False) —
Whether or not to return special tokens mask information.
return_offsets_mapping (bool, optional, defaults to False) —
Whether or not to return (char_start, char_end) for each token.
This is only available on fast tokenizers inheriting from PreTrainedTokenizerFast, if using
Python’s tokenizer, this method will raise NotImplementedError.
return_length  (bool, optional, defaults to False) —
Whether or not to return the lengths of the encoded inputs.
verbose (bool, optional, defaults to True) —
Whether or not to print more information and warnings.
**kwargs — passed to the self.tokenize() method
Returns
BatchEncoding
A BatchEncoding with the following fields:
input_ids — List of token ids to be fed to a model.
What are input IDs?
bbox — List of bounding boxes to be fed to a model.
token_type_ids — List of token type ids to be fed to a model (when return_token_type_ids=True or
if “token_type_ids” is in self.model_input_names).
What are token type IDs?
attention_mask — List of indices specifying which tokens should be attended to by the model (when
return_attention_mask=True or if “attention_mask” is in self.model_input_names).
What are attention masks?
labels — List of labels to be fed to a model. (when word_labels is specified).
overflowing_tokens — List of overflowing tokens sequences (when a max_length is specified and
return_overflowing_tokens=True).
num_truncated_tokens — Number of tokens truncated (when a max_length is specified and
return_overflowing_tokens=True).
special_tokens_mask — List of 0s and 1s, with 1 specifying added special tokens and 0 specifying
regular sequence tokens (when add_special_tokens=True and return_special_tokens_mask=True).
length — The length of the inputs (when return_length=True).
Main method to tokenize and prepare for the model one or several sequence(s) or one or several pair(s) of
sequences with word-level normalized bounding boxes and optional labels.
LayoutLMv2Processor
	
class transformers.LayoutLMv2Processor
<
source
>
(
image_processor = None
tokenizer = None
**kwargs
)
Parameters 
image_processor (LayoutLMv2ImageProcessor) —
An instance of LayoutLMv2ImageProcessor. The image processor is a required input.
tokenizer (LayoutLMv2Tokenizer or LayoutLMv2TokenizerFast) —
An instance of LayoutLMv2Tokenizer or LayoutLMv2TokenizerFast. The tokenizer is a required input.
Constructs a LayoutLMv2 processor which combines a LayoutLMv2 image processor and a LayoutLMv2 tokenizer into a
single processor.
LayoutLMv2Processor offers all the functionalities you need to prepare data for the model.
It first uses LayoutLMv2ImageProcessor to resize document images to a fixed size, and optionally applies OCR to
get words and normalized bounding boxes. These are then provided to LayoutLMv2Tokenizer or
LayoutLMv2TokenizerFast, which turns the words and bounding boxes into token-level input_ids,
attention_mask, token_type_ids, bbox. Optionally, one can provide integer word_labels, which are turned
into token-level labels for token classification tasks (such as FUNSD, CORD).
__call__
<
source
>
(
images
text: typing.Union[str, typing.List[str], typing.List[typing.List[str]]] = None
text_pair: typing.Union[typing.List[str], typing.List[typing.List[str]], NoneType] = None
boxes: typing.Union[typing.List[typing.List[int]], typing.List[typing.List[typing.List[int]]]] = None
word_labels: typing.Union[typing.List[int], typing.List[typing.List[int]], NoneType] = None
add_special_tokens: bool = True
padding: typing.Union[bool, str, transformers.utils.generic.PaddingStrategy] = False
truncation: typing.Union[bool, str, transformers.tokenization_utils_base.TruncationStrategy] = False
max_length: typing.Optional[int] = None
stride: int = 0
pad_to_multiple_of: typing.Optional[int] = None
return_token_type_ids: typing.Optional[bool] = None
return_attention_mask: typing.Optional[bool] = None
return_overflowing_tokens: bool = False
return_special_tokens_mask: bool = False
return_offsets_mapping: bool = False
return_length: bool = False
verbose: bool = True
return_tensors: typing.Union[str, transformers.utils.generic.TensorType, NoneType] = None
**kwargs
)
This method first forwards the images argument to call(). In case
LayoutLMv2ImageProcessor was initialized with apply_ocr set to True, it passes the obtained words and
bounding boxes along with the additional arguments to call() and returns the output,
together with resized images. In case LayoutLMv2ImageProcessor was initialized with apply_ocr set to
False, it passes the words (text/text_pair`) and `boxes` specified by the user along with the additional arguments to [__call__()](/docs/transformers/v4.31.0/en/model_doc/layoutlmv2#transformers.LayoutLMv2Tokenizer.__call__) and returns the output, together with resized `images.
Please refer to the docstring of the above two methods for more information.
LayoutLMv2Model
	
class transformers.LayoutLMv2Model
<
source
>
(
config
)
Parameters 
config (LayoutLMv2Config) — 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 LayoutLMv2 Model transformer outputting raw hidden-states without any specific head on top.
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
image: typing.Optional[torch.FloatTensor] = 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
output_attentions: typing.Optional[bool] = None
output_hidden_states: typing.Optional[bool] = None
return_dict: typing.Optional[bool] = None
)
→
transformers.modeling_outputs.BaseModelOutput 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.
image (torch.FloatTensor of shape (batch_size, num_channels, height, width) or detectron.structures.ImageList whose tensors is of shape (batch_size, num_channels, height, width)) —
Batch of document images.
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 tokens that are masked.
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) —
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.
Returns
transformers.modeling_outputs.BaseModelOutput or tuple(torch.FloatTensor)
A transformers.modeling_outputs.BaseModelOutput 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 (LayoutLMv2Config) 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.
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 LayoutLMv2Model 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 AutoProcessor, LayoutLMv2Model, set_seed
 from PIL import Image
 import torch
 from datasets import load_dataset
 set_seed(88)
 processor = AutoProcessor.from_pretrained("microsoft/layoutlmv2-base-uncased")
 model = LayoutLMv2Model.from_pretrained("microsoft/layoutlmv2-base-uncased")
 dataset = load_dataset("hf-internal-testing/fixtures_docvqa")
 image_path = dataset["test"][0]["file"]
 image = Image.open(image_path).convert("RGB")
 encoding = processor(image, return_tensors="pt")
 outputs = model(**encoding)
 last_hidden_states = outputs.last_hidden_state
 last_hidden_states.shape
torch.Size([1, 342, 768])
LayoutLMv2ForSequenceClassification
	
class transformers.LayoutLMv2ForSequenceClassification
<
source
>
(
config
)
Parameters 
config (LayoutLMv2Config) — 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.
LayoutLMv2 Model with a sequence classification head on top (a linear layer on top of the concatenation of the
final hidden state of the [CLS] token, average-pooled initial visual embeddings and average-pooled final visual
embeddings, e.g. for document image classification tasks such as the
RVL-CDIP dataset.
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
image: typing.Optional[torch.FloatTensor] = 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.
image (torch.FloatTensor of shape (batch_size, num_channels, height, width) or detectron.structures.ImageList whose tensors is of shape (batch_size, num_channels, height, width)) —
Batch of document images.
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 tokens that are masked.
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) —
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.
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 (LayoutLMv2Config) 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 LayoutLMv2ForSequenceClassification 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.
Example:
	Copied
 from transformers import AutoProcessor, LayoutLMv2ForSequenceClassification, set_seed
 from PIL import Image
 import torch
 from datasets import load_dataset
 set_seed(88)
 dataset = load_dataset("rvl_cdip", split="train", streaming=True)
 data = next(iter(dataset))
 image = data["image"].convert("RGB")
 processor = AutoProcessor.from_pretrained("microsoft/layoutlmv2-base-uncased")
 model = LayoutLMv2ForSequenceClassification.from_pretrained(
...     "microsoft/layoutlmv2-base-uncased", num_labels=dataset.info.features["label"].num_classes
... )
 encoding = processor(image, return_tensors="pt")
 sequence_label = torch.tensor([data["label"]])
 outputs = model(**encoding, labels=sequence_label)
 loss, logits = outputs.loss, outputs.logits
 predicted_idx = logits.argmax(dim=-1).item()
 predicted_answer = dataset.info.features["label"].names[4]
 predicted_idx, predicted_answer
(4, 'advertisement')
LayoutLMv2ForTokenClassification
	
class transformers.LayoutLMv2ForTokenClassification
<
source
>
(
config
)
Parameters 
config (LayoutLMv2Config) — 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.
LayoutLMv2 Model with a token classification head on top (a linear layer on top of the text part of the hidden
states) e.g. for sequence labeling (information extraction) tasks such as
FUNSD, SROIE,
CORD and Kleister-NDA.
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
image: typing.Optional[torch.FloatTensor] = 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.
image (torch.FloatTensor of shape (batch_size, num_channels, height, width) or detectron.structures.ImageList whose tensors is of shape (batch_size, num_channels, height, width)) —
Batch of document images.
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 tokens that are masked.
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) —
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.
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 (LayoutLMv2Config) 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 LayoutLMv2ForTokenClassification 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.
Example:
	Copied
 from transformers import AutoProcessor, LayoutLMv2ForTokenClassification, set_seed
 from PIL import Image
 from datasets import load_dataset
 set_seed(88)
 datasets = load_dataset("nielsr/funsd", split="test")
 labels = datasets.features["ner_tags"].feature.names
 id2label = {v: k for v, k in enumerate(labels)}
 processor = AutoProcessor.from_pretrained("microsoft/layoutlmv2-base-uncased", revision="no_ocr")
 model = LayoutLMv2ForTokenClassification.from_pretrained(
...     "microsoft/layoutlmv2-base-uncased", num_labels=len(labels)
... )
 data = datasets[0]
 image = Image.open(data["image_path"]).convert("RGB")
 words = data["words"]
 boxes = data["bboxes"]  # make sure to normalize your bounding boxes
 word_labels = data["ner_tags"]
 encoding = processor(
...     image,
...     words,
...     boxes=boxes,
...     word_labels=word_labels,
...     padding="max_length",
...     truncation=True,
...     return_tensors="pt",
... )
 outputs = model(**encoding)
 logits, loss = outputs.logits, outputs.loss
 predicted_token_class_ids = logits.argmax(-1)
 predicted_tokens_classes = [id2label[t.item()] for t in predicted_token_class_ids[0]]
 predicted_tokens_classes[:5]
['B-ANSWER', 'B-HEADER', 'B-HEADER', 'B-HEADER', 'B-HEADER']
LayoutLMv2ForQuestionAnswering
	
class transformers.LayoutLMv2ForQuestionAnswering
<
source
>
(
config
has_visual_segment_embedding = True
)
Parameters 
config (LayoutLMv2Config) — 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.
LayoutLMv2 Model with a span classification head on top for extractive question-answering tasks such as
DocVQA (a linear layer on top of the text part of the hidden-states output to
compute span start logits and span end logits).
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
image: typing.Optional[torch.FloatTensor] = 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)
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.
image (torch.FloatTensor of shape (batch_size, num_channels, height, width) or detectron.structures.ImageList whose tensors is of shape (batch_size, num_channels, height, width)) —
Batch of document images.
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 tokens that are masked.
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) —
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.
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.
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 (LayoutLMv2Config) 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.
The LayoutLMv2ForQuestionAnswering 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.
Example:
In this example below, we give the LayoutLMv2 model an image (of texts) and ask it a question. 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 AutoProcessor, LayoutLMv2ForQuestionAnswering, set_seed
 import torch
 from PIL import Image
 from datasets import load_dataset
 set_seed(88)
 processor = AutoProcessor.from_pretrained("microsoft/layoutlmv2-base-uncased")
 model = LayoutLMv2ForQuestionAnswering.from_pretrained("microsoft/layoutlmv2-base-uncased")
 dataset = load_dataset("hf-internal-testing/fixtures_docvqa")
 image_path = dataset["test"][0]["file"]
 image = Image.open(image_path).convert("RGB")
 question = "When is coffee break?"
 encoding = processor(image, question, return_tensors="pt")
 outputs = model(**encoding)
 predicted_start_idx = outputs.start_logits.argmax(-1).item()
 predicted_end_idx = outputs.end_logits.argmax(-1).item()
 predicted_start_idx, predicted_end_idx
(154, 287)
 predicted_answer_tokens = encoding.input_ids.squeeze()[predicted_start_idx : predicted_end_idx + 1]
 predicted_answer = processor.tokenizer.decode(predicted_answer_tokens)
 predicted_answer  # results are not very good without further fine-tuning
'council mem - bers conducted by trrf treasurer philip g. kuehn to get answers which the public ...
	Copied
 target_start_index = torch.tensor([7])
 target_end_index = torch.tensor([14])
 outputs = model(**encoding, start_positions=target_start_index, end_positions=target_end_index)
 predicted_answer_span_start = outputs.start_logits.argmax(-1).item()
 predicted_answer_span_end = outputs.end_logits.argmax(-1).item()
 predicted_answer_span_start, predicted_answer_span_end
(154, 287)
←LayoutLM
LayoutLMV3→
LayoutLMV2
Overview
Resources
Usage: LayoutLMv2Processor
LayoutLMv2Config
LayoutLMv2FeatureExtractor
LayoutLMv2ImageProcessor
LayoutLMv2Tokenizer
LayoutLMv2TokenizerFast
LayoutLMv2Processor
LayoutLMv2Model
LayoutLMv2ForSequenceClassification
LayoutLMv2ForTokenClassification
LayoutLMv2ForQuestionAnswering
