SEWModel

volution and 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?   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 (SEWConfig) and inputs.
last_hidden_state (torch.FloatTensor of shape (batch_size, sequence_length, hidd


SEWModel

en_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 SEWModel forward method, overrides the __call__ special method. Although the recipe for forward pa


SEWModel

ss 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, SEWModel
>>> import torch
>>> from datasets import load_dataset
>>> dataset = load_dataset("hf-internal-testing/librispeech_asr_demo", "clean", split="validation")
>>> dataset = dataset.sort("id")
>>> sampling_rate = dataset.features["audio"].sampling_rate
>>> processor = AutoProcessor.from_pretrained("asapp/sew-tiny-100k-ft-ls100h")
>>> model = SEWModel.from_pretrained("asapp/sew-tiny-100k-ft-ls100h")
>>> # audio file is decoded on the fly
>>> inputs = processor(dataset[0]["audio"]["array"], sampling_rate=sampling_rate, return_tensors="pt")
>>> with torch.no_grad():
...     outputs = model(**inputs)
>>> last_hidden_states = outputs.last_hidden_state
>>> list(last_hidden_states.shape)
[1, 292, 512]


SEWForCTC

class transformers.SEWForCTC  < source > ( config target_lang: typing.Optional[str] = None  )   Parameters   config (SEWConfig) — 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.    SEW Model with a language modeling head on top for Connectionist Temporal Classification (CTC).
SEW was proposed in Performance-Efficiency Trade-offs in Unsupervised Pre-training for Speech
Recognition by Felix Wu, Kwangyoun Kim, Jing Pan, Kyu Han, Kilian Q. Weinberger,
Yoav Artzi. This model inherits from PreTrainedModel. Check the superclass documentation for the generic methods the
library implements for all its model (such as downloading or saving etc.). 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 r


SEWForCTC

elated to general usage and
behavior.  forward  < source > ( input_values: typing.Optional[torch.Tensor] attention_mask: typing.Optional[torch.Tensor] = None output_attentions: typing.Optional[bool] = None output_hidden_states: typing.Optional[bool] = None return_dict: typing.Optional[bool] = None labels: typing.Optional[torch.Tensor] = None  ) → transformers.modeling_outputs.CausalLMOutput or tuple(torch.FloatTensor)  Parameters   input_values (torch.FloatTensor of shape (batch_size, sequence_length)) —
Float values of input raw speech waveform. Values can be obtained by loading a .flac or .wav audio file
into an array of type List[float] or a numpy.ndarray, e.g. via the soundfile library (pip install soundfile). To prepare the array into input_values, the AutoProcessor should be used for padding and
conversion into a tensor of type torch.FloatTensor. See Wav2Vec2Processor.call() for details.   attention_mask (torch.LongTensor of shape (batch_size, sequence_len


SEWForCTC

gth), optional) —
Mask to avoid performing convolution and 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?   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, target_length), optional) —
Labels for connectionist temporal classification. Note that target_length has to be smaller or equal to
the sequence length of the output logits. Indices are selected in [-100, 0, ..., config.vocab_size - 1].
All labels set to -100 are ignored (masked), the l


SEWForCTC

oss is only computed for labels in [0, ..., config.vocab_size - 1].   Returns
transformers.modeling_outputs.CausalLMOutput or tuple(torch.FloatTensor)

A transformers.modeling_outputs.CausalLMOutput 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 (SEWConfig) and inputs.
loss (torch.FloatTensor of shape (1,), optional, returned when labels is provided) — Language modeling loss (for next-token prediction).
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 o


SEWForCTC

f 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 SEWForCTC 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, SEWForCTC
>>> from datasets import load_dataset
>>> im


SEWForCTC

port torch
>>> dataset = load_dataset("hf-internal-testing/librispeech_asr_demo", "clean", split="validation")
>>> dataset = dataset.sort("id")
>>> sampling_rate = dataset.features["audio"].sampling_rate
>>> processor = AutoProcessor.from_pretrained("asapp/sew-tiny-100k-ft-ls100h")
>>> model = SEWForCTC.from_pretrained("asapp/sew-tiny-100k-ft-ls100h")
>>> # audio file is decoded on the fly
>>> inputs = processor(dataset[0]["audio"]["array"], sampling_rate=sampling_rate, return_tensors="pt")
>>> with torch.no_grad():
...     logits = model(**inputs).logits
>>> predicted_ids = torch.argmax(logits, dim=-1)
>>> # transcribe speech
>>> transcription = processor.batch_decode(predicted_ids)
>>> transcription[0]
'MISTER QUILTER IS THE APPOSTILE OF THE MIDDLE CLASSES AND WE ARE GLAD TO WELCOME HIS GOSPOLLE'
>>> inputs["labels"] = processor(text=dataset[0]["text"], return_tensors="pt").input_ids
>>> # compute loss
>>> loss = model(**inputs).loss
>>> round(loss.item(), 2)


SEWForSequenceClassification

class transformers.SEWForSequenceClassification  < source > ( config  )   Parameters   config (SEWConfig) — 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.    SEW Model with a sequence classification head on top (a linear layer over the pooled output) for tasks like SUPERB
Keyword Spotting. SEW was proposed in Performance-Efficiency Trade-offs in Unsupervised Pre-training for Speech
Recognition by Felix Wu, Kwangyoun Kim, Jing Pan, Kyu Han, Kilian Q. Weinberger,
Yoav Artzi. This model inherits from PreTrainedModel. Check the superclass documentation for the generic methods the
library implements for all its model (such as downloading or saving etc.). This model is a PyTorch torch.nn.Module sub-class. Use
it as a regular PyTorch Module and refer to the PyTorch documentation for al


SEWForSequenceClassification

l matter related to general usage and
behavior.  forward  < source > ( input_values: typing.Optional[torch.Tensor] attention_mask: typing.Optional[torch.Tensor] = None output_attentions: typing.Optional[bool] = None output_hidden_states: typing.Optional[bool] = None return_dict: typing.Optional[bool] = None labels: typing.Optional[torch.Tensor] = None  ) → transformers.modeling_outputs.SequenceClassifierOutput or tuple(torch.FloatTensor)  Parameters   input_values (torch.FloatTensor of shape (batch_size, sequence_length)) —
Float values of input raw speech waveform. Values can be obtained by loading a .flac or .wav audio file
into an array of type List[float] or a numpy.ndarray, e.g. via the soundfile library (pip install soundfile). To prepare the array into input_values, the AutoProcessor should be used for padding and
conversion into a tensor of type torch.FloatTensor. See Wav2Vec2Processor.call() for details.   attention_mask (torch.LongTensor of shape (batch_siz


SEWForSequenceClassification

e, sequence_length), optional) —
Mask to avoid performing convolution and 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?   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-E


SEWForSequenceClassification

ntropy).   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 (SEWConfig) 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).
Hidd


SEWForSequenceClassification

en-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 SEWForSequenceClassification 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 AutoFeatureExtractor, SEWForSequenceClassification
>>> from datasets import load_dataset
>>> import torch
>>> dataset = load_d


SEWForSequenceClassification

ataset("hf-internal-testing/librispeech_asr_demo", "clean", split="validation")
>>> dataset = dataset.sort("id")
>>> sampling_rate = dataset.features["audio"].sampling_rate
>>> feature_extractor = AutoFeatureExtractor.from_pretrained("anton-l/sew-mid-100k-ft-keyword-spotting")
>>> model = SEWForSequenceClassification.from_pretrained("anton-l/sew-mid-100k-ft-keyword-spotting")
>>> # audio file is decoded on the fly
>>> inputs = feature_extractor(dataset[0]["audio"]["array"], sampling_rate=sampling_rate, return_tensors="pt")
>>> with torch.no_grad():
...     logits = model(**inputs).logits
>>> predicted_class_ids = torch.argmax(logits, dim=-1).item()
>>> predicted_label = model.config.id2label[predicted_class_ids]
>>> predicted_label
'_unknown_'
>>> # compute loss - target_label is e.g. "down"
>>> target_label = model.config.id2label[0]
>>> inputs["labels"] = torch.tensor([model.config.label2id[target_label]])
>>> loss = model(**inputs).loss
>>> round(loss.item(), 2)
9


Overview

The LayoutLMv3 model was proposed in LayoutLMv3: Pre-training for Document AI with Unified Text and Image Masking by Yupan Huang, Tengchao Lv, Lei Cui, Yutong Lu, Furu Wei.
LayoutLMv3 simplifies LayoutLMv2 by using patch embeddings (as in ViT) instead of leveraging a CNN backbone, and pre-trains the model on 3 objectives: masked language modeling (MLM), masked image modeling (MIM)
and word-patch alignment (WPA).
The abstract from the paper is the following:
Self-supervised pre-training techniques have achieved remarkable progress in Document AI. Most multimodal pre-trained models use a masked language modeling objective to learn bidirectional representations on the text modality, but they differ in pre-training objectives for the image modality. This discrepancy adds difficulty to multimodal representation learning. In this paper, we propose LayoutLMv3 to pre


Overview

-train multimodal Transformers for Document AI with unified text and image masking. Additionally, LayoutLMv3 is pre-trained with a word-patch alignment objective to learn cross-modal alignment by predicting whether the corresponding image patch of a text word is masked. The simple unified architecture and training objectives make LayoutLMv3 a general-purpose pre-trained model for both text-centric and image-centric Document AI tasks. Experimental results show that LayoutLMv3 achieves state-of-the-art performance not only in text-centric tasks, including form understanding, receipt understanding, and document visual question answering, but also in image-centric tasks such as document image classification and document layout analysis.
Tips:
In terms of data processing, LayoutLMv3 is identical to its predecessor LayoutLMv2, except that:images need to be resized


Overview

and normalized with channels in regular RGB format. LayoutLMv2 on the other hand normalizes the images internally and expects the channels in BGR format. text is tokenized using byte-pair encoding (BPE), as opposed to WordPiece.
Due to these differences in data preprocessing, one can use LayoutLMv3Processor which internally combines a LayoutLMv3ImageProcessor (for the image modality) and a LayoutLMv3Tokenizer/LayoutLMv3TokenizerFast (for the text modality) to prepare all data for the model. Regarding usage of LayoutLMv3Processor, we refer to the usage guide of its predecessor. Demo notebooks for LayoutLMv3 can be found here. Demo scripts can be found here.
LayoutLMv3 architecture. Taken from the original paper.
This model was contributed by nielsr. The TensorFlow version of this model was added by chriskoo, tokec, and lre. The original code can be found here.


Resources

A list of official Hugging Face and community (indicated by 🌎) resources to help you get started with LayoutLMv3. 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.
LayoutLMv3 is nearly identical to LayoutLMv2, so we’ve also included LayoutLMv2 resources you can adapt for LayoutLMv3 tasks. For these notebooks, take care to use LayoutLMv2Processor instead when preparing data for the model!
 Text Classification
LayoutLMv2ForSequenceClassification is supported


Resources

by this notebook. Text classification task guide
 Token Classification
LayoutLMv3ForTokenClassification is supported by this example script and notebook. A notebook for how to perform inference with LayoutLMv2ForTokenClassification and a notebook for how to perform inference when no labels are available with LayoutLMv2ForTokenClassification. A notebook for how to finetune LayoutLMv2ForTokenClassification with the 🤗 Trainer. Token classification task guide
 Question Answering
LayoutLMv2ForQuestionAnswering is supported by this notebook. Question answering task guide
Document question answering
Document question answering task guide


LayoutLMv3Config

class transformers.LayoutLMv3Config  < source > ( vocab_size = 50265 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-05 pad_token_id = 1 bos_token_id = 0 eos_token_id = 2 max_2d_position_embeddings = 1024 coordinate_size = 128 shape_size = 128 has_relative_attention_bias = True rel_pos_bins = 32 max_rel_pos = 128 rel_2d_pos_bins = 64 max_rel_2d_pos = 256 has_spatial_attention_bias = True text_embed = True visual_embed = True input_size = 224 num_channels = 3 patch_size = 16 classifier_dropout = None **kwargs  )   Parameters   vocab_size (int, optional, defaults to 50265) —
Vocabulary size of the LayoutLMv3 model. Defines th


LayoutLMv3Config

e number of different tokens that can be represented by
the inputs_ids passed when calling LayoutLMv3Model.   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, defau


LayoutLMv3Config

lts 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 LayoutLMv3Model.   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-5) —
The epsilon used by the layer normalization layers.   max_2d_position_e


LayoutLMv3Config

mbeddings (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).   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.   rel_pos_bins (int, optional, defaults to 32) —
The number of relative position bins to be used in the self-attention mechanism.   max_rel_pos (int, optional, defaults to 128) —
The maximum number of relative positions to be used in the self-attention mechanism.   max_rel_2d_pos (int, optional, defaults to 256) —
The maximum nu


LayoutLMv3Config

mber 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.   has_spatial_attention_bias (bool, optional, defaults to True) —
Whether or not to use a spatial attention bias in the self-attention mechanism.   visual_embed (bool, optional, defaults to True) —
Whether or not to add patch embeddings.   input_size (int, optional, defaults to 224) —
The size (resolution) of the images.   num_channels (int, optional, defaults to 3) —
The number of channels of the images.   patch_size (int, optional, defaults to 16) —
The size (resolution) of the patches.   classifier_dropout (float, optional) —
The dropout ratio for the classification head.    This is the configuration class to store the configuration of a LayoutLMv3Mod


LayoutLMv3Config

el. It is used to instantiate an
LayoutLMv3 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 LayoutLMv3
microsoft/layoutlmv3-base architecture. Configuration objects inherit from PretrainedConfig and can be used to control the model outputs. Read the
documentation from PretrainedConfig for more information.  Example:   Copied >>> from transformers import LayoutLMv3Config, LayoutLMv3Model
>>> # Initializing a LayoutLMv3 microsoft/layoutlmv3-base style configuration
>>> configuration = LayoutLMv3Config()
>>> # Initializing a model (with random weights) from the microsoft/layoutlmv3-base style configuration
>>> model = LayoutLMv3Model(configuration)
>>> # Accessing the model configuration
>>> configuration = model.con


class transformers.LayoutLMv3FeatureExtractor  < source > ( *args **kwargs  )     __call__  < source > ( images **kwargs  )    Preprocess an image or a batch of images.


class transformers.LayoutLMv3ImageProcessor  < source > ( do_resize: bool = True size: typing.Dict[str, int] = None resample: Resampling = <Resampling.BILINEAR: 2> do_rescale: bool = True rescale_value: float = 0.00392156862745098 do_normalize: bool = True image_mean: typing.Union[float, typing.Iterable[float]] = None image_std: typing.Union[float, typing.Iterable[float]] = None 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 resample in preprocess.   do_rescale (bool, optional, defaults to True) —
Whether to rescale the image’s pixel values by the specified rescale_value. Can be overridden by
do_rescale in preprocess.   rescale_factor (float, optional, defaults to 1 / 255) —
Value by which the image’s pixel values are rescaled. Can be overridden by rescale_factor in
preprocess.   do_normalize (bool, optional, defaults to True) —
Whether to normalize the image. Can be overridden by the do_normalize parameter in the preprocess
method.   image_mean (Iterable[float] or float, optional, defaults to IMAGENET_STANDARD_MEAN) —
Mean to use if normalizing the image. This is a float or list of floats the length of the number of
channels in the image. Can be overridden by the image_mean parameter in the preprocess method.   image_std (Iterable[float] or float, optional, defaults to IMAGENET_STANDARD_STD) —
Standard deviation to use


 if normalizing the image. This is a float or list of floats the length of the
number of channels in the image. Can be overridden by the image_std 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
the apply_ocr parameter in the preprocess method.   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 the ocr_lang parameter in the preprocess method.   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 the tesseract_config parameter in the
preprocess method.    Constructs a LayoutLMv3 image processor.  preprocess  < source > ( images: typing.Union[ForwardRef('PIL.Image.Imag


e'), 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 = None do_rescale: bool = None rescale_factor: float = None do_normalize: bool = None image_mean: typing.Union[float, typing.Iterable[float]] = None image_std: typing.Union[float, typing.Iterable[float]] = 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'> input_data_format: typing.Union[transformers.image_utils.ChannelDimension, str, NoneType] = None **kwargs  )   Parameters   images (ImageInput) —
Image to preprocess. Expects a single or batch of images with pixel values ranging from 0 to 255. If
pa


ssing in images with pixel values between 0 and 1, set do_rescale=False.   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 applying resize.   resample (int, optional, defaults to self.resample) —
Resampling filter to use if resizing the image. This can be one of the PILImageResampling filters.
Only has an effect if do_resize is set to True.   do_rescale (bool, optional, defaults to self.do_rescale) —
Whether to rescale the image pixel values between [0, 1].   rescale_factor (float, optional, defaults to self.rescale_factor) —
Rescale factor to apply to the image pixel values. Only has an effect if do_rescale is set to True.   do_normalize (bool, optional, defaults to self.do_normalize) —
Whether to normalize the image.   image_mean (float or Iterable[float], optional, defaults to self.image_mean) —
Mean values to


 be used for normalization. Only has an effect if do_normalize is set to True.   image_std (float or Iterable[float], optional, defaults to self.image_std) —
Standard deviation values to be used for normalization. Only has an effect if do_normalize 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.
TensorTyp


e.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.
   input_data_format (ChannelDimension or str, optional) —
The channel dimension format for the input image. If unset, the channel dimension format is inferred
from the input image. Can be one of:
"channels_first" or ChannelDimension.FIRST: image in (num_channels, height, width) format.
"channels_last" or ChannelDimension.LAST: image in (height, width, num_channels) format.
"none" or ChannelDimension.NONE: image in (height, width) format.
    Preprocess an image or batch of i


LayoutLMv3Tokenizer

class transformers.LayoutLMv3Tokenizer  < source > ( vocab_file merges_file errors = 'replace' bos_token = '<s>' eos_token = '</s>' sep_token = '</s>' cls_token = '<s>' unk_token = '<unk>' pad_token = '<pad>' mask_token = '<mask>' add_prefix_space = True cls_token_box = [0, 0, 0, 0] sep_token_box = [0, 0, 0, 0] pad_token_box = [0, 0, 0, 0] pad_token_label = -100 only_label_first_subword = True **kwargs  )   Parameters   vocab_file (str) —
Path to the vocabulary file.   merges_file (str) —
Path to the merges file.   errors (str, optional, defaults to "replace") —
Paradigm to follow when decoding bytes to UTF-8. See
bytes.decode for more information.   bos_token (str, optional, defaults to "<s>") —
The beginning of sequence token that was used during pretraining. Can be used a sequence classifier token.
When building a sequence using special tokens, this is not the token that is used for the beginning of
sequence. The t


LayoutLMv3Tokenizer

oken used is the cls_token.
   eos_token (str, optional, defaults to "</s>") —
The end of sequence token.
When building a sequence using special tokens, this is not the token that is used for the end of sequence.
The token used is the sep_token.
   sep_token (str, optional, defaults to "</s>") —
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.   cls_token (str, optional, defaults to "<s>") —
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.   unk_token (str, optional, defaults to "<unk>") —
The unknown token. A token that is not in the vocabulary c


LayoutLMv3Tokenizer

annot be converted to an ID and is set to be this
token instead.   pad_token (str, optional, defaults to "<pad>") —
The token used for padding, for example when batching sequences of different lengths.   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.   add_prefix_space (bool, optional, defaults to True) —
Whether or not to add an initial space to the input. This allows to treat the leading word just as any
other word. (RoBERTa tokenizer detect beginning of words by the preceding space).   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 [0, 0, 0, 0]) —
The bounding box to use for the special [SEP] token.   pad_token_box (List[int],


LayoutLMv3Tokenizer

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.    Construct a LayoutLMv3 tokenizer. Based on RoBERTatokenizer (Byte Pair Encoding or BPE).
LayoutLMv3Tokenizer 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. LayoutLMv3Tokenizer runs end-to-end tokenization: pu


LayoutLMv3Tokenizer

nctuation 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: typi


LayoutLMv3Tokenizer

ng.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  )   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 ta


LayoutLMv3Tokenizer

sks 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_lengt


LayoutLMv3Tokenizer

h 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 ma


LayoutLMv3Tokenizer

ximum 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 u


LayoutLMv3Tokenizer

se 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.
   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' (defa


LayoutLMv3Tokenizer

ult): 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


LayoutLMv3Tokenizer

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


LayoutLMv3Tokenizer

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.
    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] =


class transformers.LayoutLMv3TokenizerFast  < source > ( vocab_file = None merges_file = None tokenizer_file = None errors = 'replace' bos_token = '<s>' eos_token = '</s>' sep_token = '</s>' cls_token = '<s>' unk_token = '<unk>' pad_token = '<pad>' mask_token = '<mask>' add_prefix_space = True trim_offsets = True cls_token_box = [0, 0, 0, 0] sep_token_box = [0, 0, 0, 0] pad_token_box = [0, 0, 0, 0] pad_token_label = -100 only_label_first_subword = True **kwargs  )   Parameters   vocab_file (str) —
Path to the vocabulary file.   merges_file (str) —
Path to the merges file.   errors (str, optional, defaults to "replace") —
Paradigm to follow when decoding bytes to UTF-8. See
bytes.decode for more information.   bos_token (str, optional, defaults to "<s>") —
The beginning of sequence token that was used during pretraining. Can be used a sequence classifier token.
When building a sequence using special tokens, this is not the token that is used for the beginning of
sequence. T


he token used is the cls_token.
   eos_token (str, optional, defaults to "</s>") —
The end of sequence token.
When building a sequence using special tokens, this is not the token that is used for the end of sequence.
The token used is the sep_token.
   sep_token (str, optional, defaults to "</s>") —
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.   cls_token (str, optional, defaults to "<s>") —
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.   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
to


ken instead.   pad_token (str, optional, defaults to "<pad>") —
The token used for padding, for example when batching sequences of different lengths.   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.   add_prefix_space (bool, optional, defaults to False) —
Whether or not to add an initial space to the input. This allows to treat the leading word just as any
other word. (RoBERTa tokenizer detect beginning of words by the preceding space).   trim_offsets (bool, optional, defaults to True) —
Whether the post processing step should trim offsets to avoid including whitespaces.   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 [0, 0, 0, 0]) —
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.    Construct a “fast” LayoutLMv3 tokenizer (backed by HuggingFace’s tokenizers library). Based on BPE. 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  )   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 m


aximum 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 e


nd 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.
   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 seque


nce 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 pro


vided. 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.
    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 l


LayoutLMv3Processor

class transformers.LayoutLMv3Processor  < source > ( image_processor = None tokenizer = None **kwargs  )   Parameters   image_processor (LayoutLMv3ImageProcessor, optional) —
An instance of LayoutLMv3ImageProcessor. The image processor is a required input.   tokenizer (LayoutLMv3Tokenizer or LayoutLMv3TokenizerFast, optional) —
An instance of LayoutLMv3Tokenizer or LayoutLMv3TokenizerFast. The tokenizer is a required input.    Constructs a LayoutLMv3 processor which combines a LayoutLMv3 image processor and a LayoutLMv3 tokenizer into a
single processor. LayoutLMv3Processor offers all the functionalities you need to prepare data for the model. It first uses LayoutLMv3ImageProcessor to resize and normalize document images, and optionally applies OCR to
get words and normalized bounding boxes. These are then provided to LayoutLMv3Tokenizer or
LayoutLMv3TokenizerFast, which turns the words and bounding boxes into token-level input_ids,
attention


LayoutLMv3Processor

_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] = None 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_attentio


LayoutLMv3Processor

n_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
LayoutLMv3ImageProcessor 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 and normalized pixel_values. In case LayoutLMv3ImageProcessor 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() and returns the output, together with
resized and normalized pixel_values. Please refer to the docstring of the above two methods for more informatio


LayoutLMv3Model

class transformers.LayoutLMv3Model  < source > ( config  )   Parameters   config (LayoutLMv3Config) — 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 LayoutLMv3 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 attention_mask: typing.Optional[torch.FloatTensor] = None token_type_ids: typing.Optional[torch.LongTensor] = None position_ids: typing.Optional[torch.Lon


LayoutLMv3Model

gTensor] = None head_mask: typing.Optional[torch.FloatTensor] = None inputs_embeds: typing.Optional[torch.FloatTensor] = None pixel_values: 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, token_sequence_length)) —
Indices of input sequence tokens in the vocabulary.
Note that sequence_length = token_sequence_length + patch_sequence_length + 1 where 1 is for [CLS]
token. See pixel_values for patch_sequence_length.
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, token_sequence_le


LayoutLMv3Model

ngth, 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.
Note that sequence_length = token_sequence_length + patch_sequence_length + 1 where 1 is for [CLS]
token. See pixel_values for patch_sequence_length.   pixel_values (torch.FloatTensor of shape (batch_size, num_channels, height, width)) —
Batch of document images. Each image is divided into patches of shape (num_channels, config.patch_size, config.patch_size) and the total number of patches (=patch_sequence_length) equals to ((height / config.patch_size) * (width / config.patch_size)).   attention_mask (torch.FloatTensor of shape (ba


LayoutLMv3Model

tch_size, token_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.
Note that sequence_length = token_sequence_length + patch_sequence_length + 1 where 1 is for [CLS]
token. See pixel_values for patch_sequence_length.
What are attention masks?   token_type_ids (torch.LongTensor of shape (batch_size, token_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.
Note that sequence_length = token_sequence_length + patch_sequence_length + 1 where 1 is for [CLS]
token. See pixel_values for patch_sequence_length.
What are token type IDs?   position_ids (torch.LongTensor of shape (batch_size, token_sequence_le


LayoutLMv3Model

ngth), optional) —
Indices of positions of each input sequence tokens in the position embeddings. Selected in the range [0, config.max_position_embeddings - 1].
Note that sequence_length = token_sequence_length + patch_sequence_length + 1 where 1 is for [CLS]
token. See pixel_values for patch_sequence_length.
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, token_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 t


LayoutLMv3Model

han 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 (LayoutLMv3Config) and inputs.
last_hidden_state (torch.FloatTensor of shape (batch_size, sequence_length, hidden_size)) — Sequence of


LayoutLMv3Model

 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 LayoutLMv3Model forward method


LayoutLMv3Model

, 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, AutoModel
>>> from datasets import load_dataset
>>> processor = AutoProcessor.from_pretrained("microsoft/layoutlmv3-base", apply_ocr=False)
>>> model = AutoModel.from_pretrained("microsoft/layoutlmv3-base")
>>> dataset = load_dataset("nielsr/funsd-layoutlmv3", split="train")
>>> example = dataset[0]
>>> image = example["image"]
>>> words = example["tokens"]
>>> boxes = example["bboxes"]
>>> encoding = processor(image, words, boxes=boxes, return_tensors="pt")
>>> outputs = model(**encoding)
>>> last_hidden_states = outputs.last_hidden_


class transformers.LayoutLMv3ForSequenceClassification  < source > ( config  )   Parameters   config (LayoutLMv3Config) — 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.    LayoutLMv3 Model with a sequence classification head on top (a linear layer on top of the final hidden state of the
[CLS] token) 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 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 bbox: typing.Optional[torch.LongTensor] = None pixel_values: typing.Optional[torch.LongTensor] = 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 sh


ould 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.   pixel_values (torch.FloatTensor of shape (batch_size, num_channels, height, width)) —
Batch of document images. Each image is divided into patches of shape (num_channels, config.patch_size, config.patch_size) and the total number of patches (=patch_sequence_length) equals to ((height / config.patch_size) * (width / config.patch_size)).   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. In


dices 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 matr


ix.   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.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 (LayoutLMv3Config) 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 LayoutLMv3ForSequenceClassification forward method, overrides the __call__ special method. Altho


ugh 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, AutoModelForSequenceClassification
>>> from datasets import load_dataset
>>> import torch
>>> processor = AutoProcessor.from_pretrained("microsoft/layoutlmv3-base", apply_ocr=False)
>>> model = AutoModelForSequenceClassification.from_pretrained("microsoft/layoutlmv3-base")
>>> dataset = load_dataset("nielsr/funsd-layoutlmv3", split="train")
>>> example = dataset[0]
>>> image = example["image"]
>>> words = example["tokens"]
>>> boxes = example["bboxes"]
>>> encoding = processor(image, words, boxes=boxes, return_tensors="pt")
>>> sequence_label = torch.tensor([1])
>>> outputs = model(**encoding, labels=sequence_label)
>>> loss = outputs.loss
>>> logits = outputs.lo


class transformers.LayoutLMv3ForTokenClassification  < source > ( config  )   Parameters   config (LayoutLMv3Config) — 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.    LayoutLMv3 Model with a token classification head on top (a linear layer on top of the final 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 at


tention_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 pixel_values: typing.Optional[torch.LongTensor] = 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.   pixel_values (torch.FloatTensor of shape (batch_size, num_channels, height, width)) —
Batch of document images. Each image is divided into patches of shape (num_channels, config.patch_size, config.patch_size) and the total number of patches (=patch_sequence_length) equals to ((height / config.patch_size) * (width / config.patch_size)).   attention_mask (torch.FloatTensor of shape (batch_size, sequence_length), optional) —
Mask to avoid pe


rforming 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 value


s 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.   label


s (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 (LayoutLMv3Config) 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_hidd


en_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 LayoutLMv3ForTokenClassification 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, AutoModelForTokenClassification
>>> from datasets import load_dataset
>>> processor = AutoProcessor.from_pretrained("microsoft/layoutlmv3-base", apply_ocr=False)
>>> model = AutoModelForTokenClassification.from_pretrained("microsoft/layoutlmv3-base", num_labels=7)
>>> dataset = load_dataset("nielsr/funsd-layoutlmv3", split="train")
>>> example = dataset[0]
>>> image = example["image"]
>>> words = example["tokens"]
>>> boxes = example["bboxes"]
>>> word_labels = example["ner_tags"]
>>> encoding = processor(image, words, boxes=boxes, word_labels=word_labels, return_tensors="pt")
>>> outputs = model(**encoding)
>>> loss = outputs.loss
>>> logits = outputs.


class transformers.LayoutLMv3ForQuestionAnswering  < source > ( config  )   Parameters   config (LayoutLMv3Config) — 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.    LayoutLMv3 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 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 bbox: typing.Optional[torch.LongTensor] = None pixel_values: typing.Optional[torch.LongTensor] = 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.   pixel_values (torch.FloatTensor of shape (batch_size, num_channels, height, width)) —
Batch of document images. Each image is divided into patches of shape (num_channels, config.patch_size, config.patch_size) and the total number of patches (=patch_sequence_length) equals to ((height / config.patch_size) * (width / config.patch_size)).   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 i


ndices 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 t


oken 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 (LayoutLMv3Config) 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 o


utput_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 LayoutLMv3ForQuestionAnswering 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, AutoModelForQuestionAnswering
>>> from datasets import load_dataset
>>> import torch
>>> processor = AutoProcessor.from_pretrained("microsoft/layoutlmv3-base", apply_ocr=False)
>>> model = AutoModelForQuestionAnswering.from_pretrained("microsoft/layoutlmv3-base")
>>> dataset = load_dataset("nielsr/funsd-layoutlmv3", split="train")
>>> example = dataset[0]
>>> image = example["image"]
>>> question = "what's his name?"
>>> words = example["tokens"]
>>> boxes = example["bboxes"]
>>> encoding = processor(image, question, words, boxes=boxes, return_tensors="pt")
>>> start_positions = torch.tensor([1])
>>> end_positions = torch.tensor([3])
>>> outputs = model(**encoding, start_positions=start_positions, end_positions=end_positions)
>>> loss = outputs.loss
>>> start_scores = outputs.start_logits
>>> end_scores = outputs.end


TFLayoutLMv3Model

class transformers.TFLayoutLMv3Model  < source > ( *args **kwargs  )   Parameters   config (LayoutLMv3Config) — 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 LayoutLMv3 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


TFLayoutLMv3Model

 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_


TFLayoutLMv3Model

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: tf.Tensor | None = None bbox: tf.Tensor | None = None attention_mask: tf.Tensor | None = None token_type_ids: tf.Tensor | None = None position_ids: tf.Tensor | None = None head_mask: tf.Tensor | None = None inputs_embeds: tf.Tensor | None = None pixel_values: tf.Tensor | None = None output_attentions: Optional[bool] = None output_hidden_states: Optional[bool] = None return_dict: Optional[bool] = None training: bool = False  ) → transformers.modeling_tf_outputs.TFBaseModelOutput or tuple(tf.Tensor)  Parameters   input_ids (Numpy array or tf.Tensor of shape (batch_


TFLayoutLMv3Model

size, sequence_length)) —
Indices of input sequence tokens in the vocabulary.
Note that sequence_length = token_sequence_length + patch_sequence_length + 1 where 1 is for [CLS]
token. See pixel_values for patch_sequence_length.
Indices can be obtained using AutoTokenizer. See PreTrainedTokenizer.encode() and
PreTrainedTokenizer.call() 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]. 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.
Note that sequence_length = token_sequence_length + patch_sequence_length + 1 where 1 is for [CLS]
token. See pixel_values for patch_sequence_length.   pix


TFLayoutLMv3Model

el_values (tf.Tensor of shape (batch_size, num_channels, height, width)) —
Batch of document images. Each image is divided into patches of shape (num_channels, config.patch_size, config.patch_size) and the total number of patches (=patch_sequence_length) equals to ((height / config.patch_size) * (width / config.patch_size)).   attention_mask (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.
Note that sequence_length = token_sequence_length + patch_sequence_length + 1 where 1 is for [CLS]
token. See pixel_values for patch_sequence_length.
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 correspon


TFLayoutLMv3Model

ds to a sentence A token,
1 corresponds to a sentence B token.
Note that sequence_length = token_sequence_length + patch_sequence_length + 1 where 1 is for [CLS]
token. See pixel_values for patch_sequence_length.
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].
Note that sequence_length = token_sequence_length + patch_sequence_length + 1 where 1 is for [CLS]
token. See pixel_values for patch_sequence_length.
What are position IDs?   head_mask (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_l


TFLayoutLMv3Model

ength, 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_tf_outputs.TFBaseModelOutput or tuple(tf.Tensor)

A transformers.modeling_tf_outputs.TFBaseModelOutput or a tuple of tf.Tensor (if
return_dict=False is passed or when config.return_dict=False) comprising vari


TFLayoutLMv3Model

ous elements depending on the
configuration (LayoutLMv3Config) 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.
hidden_states (tuple(tf.FloatTensor), 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


TFLayoutLMv3Model

heads.
  The TFLayoutLMv3Model 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, TFAutoModel
>>> from datasets import load_dataset
>>> processor = AutoProcessor.from_pretrained("microsoft/layoutlmv3-base", apply_ocr=False)
>>> model = TFAutoModel.from_pretrained("microsoft/layoutlmv3-base")
>>> dataset = load_dataset("nielsr/funsd-layoutlmv3", split="train")
>>> example = dataset[0]
>>> image = example["image"]
>>> words = example["tokens"]
>>> boxes = example["bboxes"]
>>> encoding = processor(image, words, boxes=boxes, return_tensors="tf")
>>> outputs = model(**encoding)
>>> last_hidden_states = outputs.last_hidden_stat


class transformers.TFLayoutLMv3ForSequenceClassification  < source > ( *args **kwargs  )   Parameters   config (LayoutLMv3Config) — 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.    LayoutLMv3 Model with a sequence classification head on top (a linear layer on top of the final hidden state of the
[CLS] token) e.g. for document image classification tasks such as the
RVL-CDIP dataset. 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 on


ly 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: tf.Tensor | None = None attention_mask: tf.Tensor | None = None token_type_ids: tf.Tensor | None = None position_ids: tf.Tensor | None = None head_mask: tf.Tensor | None = None inputs_embeds: tf.Tensor | None = None labels: tf.Tensor | None = None output_attentions: Optional[bool] = None output_hidden_st


ates: Optional[bool] = None return_dict: Optional[bool] = None bbox: tf.Tensor | None = None pixel_values: 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.
Note that sequence_length = token_sequence_length + patch_sequence_length + 1 where 1 is for [CLS]
token. See pixel_values for patch_sequence_length.
Indices can be obtained using AutoTokenizer. See PreTrainedTokenizer.encode() and
PreTrainedTokenizer.call() 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]. Each bounding box shoul