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  • LayoutLMv3
  • Overview
  • Resources
  • LayoutLMv3Config
  • LayoutLMv3FeatureExtractor
  • LayoutLMv3ImageProcessor
  • LayoutLMv3Tokenizer
  • LayoutLMv3TokenizerFast
  • LayoutLMv3Processor
  • LayoutLMv3Model
  • LayoutLMv3ForSequenceClassification
  • LayoutLMv3ForTokenClassification
  • LayoutLMv3ForQuestionAnswering
  • TFLayoutLMv3Model
  • TFLayoutLMv3ForSequenceClassification
  • TFLayoutLMv3ForTokenClassification
  • TFLayoutLMv3ForQuestionAnswering
  1. API
  2. MODELS
  3. MULTIMODAL MODELS

LayoutLMV3

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Last updated 1 year ago

LayoutLMv3

Overview

The LayoutLMv3 model was proposed in by Yupan Huang, Tengchao Lv, Lei Cui, Yutong Lu, Furu Wei. LayoutLMv3 simplifies by using patch embeddings (as in ) 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-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 , except that:

    • images need to be resized 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 which internally combines a (for the image modality) and a / (for the text modality) to prepare all data for the model.

  • Regarding usage of , we refer to the of its predecessor.

  • Demo notebooks for LayoutLMv3 can be found .

  • Demo scripts can be found .

Resources

A list of official BOINC AI 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.

Text Classification

Token Classification

Question Answering

Document question answering

LayoutLMv3Config

class transformers.LayoutLMv3Config

( vocab_size = 50265hidden_size = 768num_hidden_layers = 12num_attention_heads = 12intermediate_size = 3072hidden_act = 'gelu'hidden_dropout_prob = 0.1attention_probs_dropout_prob = 0.1max_position_embeddings = 512type_vocab_size = 2initializer_range = 0.02layer_norm_eps = 1e-05pad_token_id = 1bos_token_id = 0eos_token_id = 2max_2d_position_embeddings = 1024coordinate_size = 128shape_size = 128has_relative_attention_bias = Truerel_pos_bins = 32max_rel_pos = 128rel_2d_pos_bins = 64max_rel_2d_pos = 256has_spatial_attention_bias = Truetext_embed = Truevisual_embed = Trueinput_size = 224num_channels = 3patch_size = 16classifier_dropout = None**kwargs )

Parameters

  • hidden_size (int, optional, defaults to 768) — Dimension of the encoder layers and the pooler layer.

  • num_hidden_layers (int, optional, defaults to 12) — Number of hidden layers in the Transformer encoder.

  • num_attention_heads (int, optional, defaults to 12) — Number of attention heads for each attention layer in the Transformer encoder.

  • intermediate_size (int, optional, defaults to 3072) — Dimension of the “intermediate” (i.e., feed-forward) layer in the Transformer encoder.

  • hidden_act (str or function, optional, defaults to "gelu") — The non-linear activation function (function or string) in the encoder and pooler. If string, "gelu", "relu", "selu" and "gelu_new" are supported.

  • hidden_dropout_prob (float, optional, defaults to 0.1) — The dropout probabilitiy for all fully connected layers in the embeddings, encoder, and pooler.

  • attention_probs_dropout_prob (float, optional, defaults to 0.1) — The dropout ratio for the attention probabilities.

  • max_position_embeddings (int, optional, defaults to 512) — The maximum sequence length that this model might ever be used with. Typically set this to something large just in case (e.g., 512 or 1024 or 2048).

  • 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_embeddings (int, optional, defaults to 1024) — The maximum value that the 2D position embedding might ever be used with. Typically set this to something large just in case (e.g., 1024).

  • 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 number of relative 2D positions in the self-attention mechanism.

  • rel_2d_pos_bins (int, optional, defaults to 64) — The number of 2D relative position bins in the self-attention mechanism.

  • 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.

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.config

LayoutLMv3FeatureExtractor

class transformers.LayoutLMv3FeatureExtractor

( *args**kwargs )

__call__

( images**kwargs )

Preprocess an image or a batch of images.

LayoutLMv3ImageProcessor

class transformers.LayoutLMv3ImageProcessor

( do_resize: bool = Truesize: typing.Dict[str, int] = Noneresample: Resampling = <Resampling.BILINEAR: 2>do_rescale: bool = Truerescale_value: float = 0.00392156862745098do_normalize: bool = Trueimage_mean: typing.Union[float, typing.Iterable[float]] = Noneimage_std: typing.Union[float, typing.Iterable[float]] = Noneapply_ocr: bool = Trueocr_lang: typing.Optional[str] = Nonetesseract_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

( images: typing.Union[ForwardRef('PIL.Image.Image'), numpy.ndarray, ForwardRef('torch.Tensor'), typing.List[ForwardRef('PIL.Image.Image')], typing.List[numpy.ndarray], typing.List[ForwardRef('torch.Tensor')]]do_resize: bool = Nonesize: typing.Dict[str, int] = Noneresample = Nonedo_rescale: bool = Nonerescale_factor: float = Nonedo_normalize: bool = Noneimage_mean: typing.Union[float, typing.Iterable[float]] = Noneimage_std: typing.Union[float, typing.Iterable[float]] = Noneapply_ocr: bool = Noneocr_lang: typing.Optional[str] = Nonetesseract_config: typing.Optional[str] = Nonereturn_tensors: typing.Union[str, transformers.utils.generic.TensorType, NoneType] = Nonedata_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 passing 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.

    • TensorType.PYTORCH or 'pt': Return a batch of type torch.Tensor.

    • TensorType.NUMPY or 'np': Return a batch of type np.ndarray.

    • TensorType.JAX or 'jax': Return a batch of type jax.numpy.ndarray.

  • data_format (ChannelDimension or str, optional, defaults to ChannelDimension.FIRST) — The channel dimension format for the output image. Can be one of:

    • ChannelDimension.FIRST: image in (num_channels, height, width) format.

    • ChannelDimension.LAST: image in (height, width, num_channels) format.

  • 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 images.

LayoutLMv3Tokenizer

class transformers.LayoutLMv3Tokenizer

( vocab_filemerges_fileerrors = '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 = Truecls_token_box = [0, 0, 0, 0]sep_token_box = [0, 0, 0, 0]pad_token_box = [0, 0, 0, 0]pad_token_label = -100only_label_first_subword = True**kwargs )

Parameters

  • vocab_file (str) — Path to the vocabulary file.

  • merges_file (str) — Path to the merges file.

  • 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 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 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 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).

  • 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.

__call__

( 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] = Noneboxes: typing.Union[typing.List[typing.List[int]], typing.List[typing.List[typing.List[int]]]] = Noneword_labels: typing.Union[typing.List[int], typing.List[typing.List[int]], NoneType] = Noneadd_special_tokens: bool = Truepadding: typing.Union[bool, str, transformers.utils.generic.PaddingStrategy] = Falsetruncation: typing.Union[bool, str, transformers.tokenization_utils_base.TruncationStrategy] = Nonemax_length: typing.Optional[int] = Nonestride: int = 0pad_to_multiple_of: typing.Optional[int] = Nonereturn_tensors: typing.Union[str, transformers.utils.generic.TensorType, NoneType] = Nonereturn_token_type_ids: typing.Optional[bool] = Nonereturn_attention_mask: typing.Optional[bool] = Nonereturn_overflowing_tokens: bool = Falsereturn_special_tokens_mask: bool = Falsereturn_offsets_mapping: bool = Falsereturn_length: bool = Falseverbose: 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.

    • 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).

    • True or 'longest_first': Truncate to a maximum length specified with the argument max_length or to the maximum acceptable input length for the model if that argument is not provided. This will truncate token by token, removing a token from the longest sequence in the pair if a pair of sequences (or a batch of pairs) is provided.

    • 'only_first': Truncate to a maximum length specified with the argument max_length or to the maximum acceptable input length for the model if that argument is not provided. This will only truncate the first sequence of a pair if a pair of sequences (or a batch of pairs) is provided.

    • 'only_second': Truncate to a maximum length specified with the argument max_length or to the maximum acceptable input length for the model if that argument is not provided. This will only truncate the second sequence of a pair if a pair of sequences (or a batch of pairs) is provided.

    • False or 'do_not_truncate' (default): No truncation (i.e., can output batch with sequence lengths greater than the model maximum admissible input size).

  • max_length (int, optional) — Controls the maximum length to use by one of the truncation/padding parameters.

    If left unset or set to None, this will use the predefined model maximum length if a maximum length is required by one of the truncation/padding parameters. If the model has no specific maximum input length (like XLNet) truncation/padding to a maximum length will be deactivated.

  • stride (int, optional, defaults to 0) — If set to a number along with max_length, the overflowing tokens returned when return_overflowing_tokens=True will contain some tokens from the end of the truncated sequence returned to provide some overlap between truncated and overflowing sequences. The value of this argument defines the number of overlapping tokens.

  • pad_to_multiple_of (int, optional) — If set will pad the sequence to a multiple of the provided value. This is especially useful to enable the use of Tensor Cores on NVIDIA hardware with compute capability >= 7.5 (Volta).

    • '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.

    • 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).

    • True or 'longest_first': Truncate to a maximum length specified with the argument max_length or to the maximum acceptable input length for the model if that argument is not provided. This will truncate token by token, removing a token from the longest sequence in the pair if a pair of sequences (or a batch of pairs) is provided.

    • 'only_first': Truncate to a maximum length specified with the argument max_length or to the maximum acceptable input length for the model if that argument is not provided. This will only truncate the first sequence of a pair if a pair of sequences (or a batch of pairs) is provided.

    • 'only_second': Truncate to a maximum length specified with the argument max_length or to the maximum acceptable input length for the model if that argument is not provided. This will only truncate the second sequence of a pair if a pair of sequences (or a batch of pairs) is provided.

    • False or 'do_not_truncate' (default): No truncation (i.e., can output batch with sequence lengths greater than the model maximum admissible input size).

  • max_length (int, optional) — Controls the maximum length to use by one of the truncation/padding parameters. If left unset or set to None, this will use the predefined model maximum length if a maximum length is required by one of the truncation/padding parameters. If the model has no specific maximum input length (like XLNet) truncation/padding to a maximum length will be deactivated.

  • stride (int, optional, defaults to 0) — If set to a number along with max_length, the overflowing tokens returned when return_overflowing_tokens=True will contain some tokens from the end of the truncated sequence returned to provide some overlap between truncated and overflowing sequences. The value of this argument defines the number of overlapping tokens.

  • pad_to_multiple_of (int, optional) — If set will pad the sequence to a multiple of the provided value. This is especially useful to enable the use of Tensor Cores on NVIDIA hardware with compute capability >= 7.5 (Volta).

    • '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

( save_directory: strfilename_prefix: typing.Optional[str] = None )

LayoutLMv3TokenizerFast

class transformers.LayoutLMv3TokenizerFast

( vocab_file = Nonemerges_file = Nonetokenizer_file = Noneerrors = '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 = Truetrim_offsets = Truecls_token_box = [0, 0, 0, 0]sep_token_box = [0, 0, 0, 0]pad_token_box = [0, 0, 0, 0]pad_token_label = -100only_label_first_subword = True**kwargs )

Parameters

  • vocab_file (str) — Path to the vocabulary file.

  • merges_file (str) — Path to the merges file.

  • 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 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 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 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 BOINC AI’s tokenizers library). Based on BPE.

__call__

( 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] = Noneboxes: typing.Union[typing.List[typing.List[int]], typing.List[typing.List[typing.List[int]]]] = Noneword_labels: typing.Union[typing.List[int], typing.List[typing.List[int]], NoneType] = Noneadd_special_tokens: bool = Truepadding: typing.Union[bool, str, transformers.utils.generic.PaddingStrategy] = Falsetruncation: typing.Union[bool, str, transformers.tokenization_utils_base.TruncationStrategy] = Nonemax_length: typing.Optional[int] = Nonestride: int = 0pad_to_multiple_of: typing.Optional[int] = Nonereturn_tensors: typing.Union[str, transformers.utils.generic.TensorType, NoneType] = Nonereturn_token_type_ids: typing.Optional[bool] = Nonereturn_attention_mask: typing.Optional[bool] = Nonereturn_overflowing_tokens: bool = Falsereturn_special_tokens_mask: bool = Falsereturn_offsets_mapping: bool = Falsereturn_length: bool = Falseverbose: 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.

    • 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).

    • True or 'longest_first': Truncate to a maximum length specified with the argument max_length or to the maximum acceptable input length for the model if that argument is not provided. This will truncate token by token, removing a token from the longest sequence in the pair if a pair of sequences (or a batch of pairs) is provided.

    • 'only_first': Truncate to a maximum length specified with the argument max_length or to the maximum acceptable input length for the model if that argument is not provided. This will only truncate the first sequence of a pair if a pair of sequences (or a batch of pairs) is provided.

    • 'only_second': Truncate to a maximum length specified with the argument max_length or to the maximum acceptable input length for the model if that argument is not provided. This will only truncate the second sequence of a pair if a pair of sequences (or a batch of pairs) is provided.

    • False or 'do_not_truncate' (default): No truncation (i.e., can output batch with sequence lengths greater than the model maximum admissible input size).

  • max_length (int, optional) — Controls the maximum length to use by one of the truncation/padding parameters.

    If left unset or set to None, this will use the predefined model maximum length if a maximum length is required by one of the truncation/padding parameters. If the model has no specific maximum input length (like XLNet) truncation/padding to a maximum length will be deactivated.

  • stride (int, optional, defaults to 0) — If set to a number along with max_length, the overflowing tokens returned when return_overflowing_tokens=True will contain some tokens from the end of the truncated sequence returned to provide some overlap between truncated and overflowing sequences. The value of this argument defines the number of overlapping tokens.

  • pad_to_multiple_of (int, optional) — If set will pad the sequence to a multiple of the provided value. This is especially useful to enable the use of Tensor Cores on NVIDIA hardware with compute capability >= 7.5 (Volta).

    • '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.

    • 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).

    • True or 'longest_first': Truncate to a maximum length specified with the argument max_length or to the maximum acceptable input length for the model if that argument is not provided. This will truncate token by token, removing a token from the longest sequence in the pair if a pair of sequences (or a batch of pairs) is provided.

    • 'only_first': Truncate to a maximum length specified with the argument max_length or to the maximum acceptable input length for the model if that argument is not provided. This will only truncate the first sequence of a pair if a pair of sequences (or a batch of pairs) is provided.

    • 'only_second': Truncate to a maximum length specified with the argument max_length or to the maximum acceptable input length for the model if that argument is not provided. This will only truncate the second sequence of a pair if a pair of sequences (or a batch of pairs) is provided.

    • False or 'do_not_truncate' (default): No truncation (i.e., can output batch with sequence lengths greater than the model maximum admissible input size).

  • max_length (int, optional) — Controls the maximum length to use by one of the truncation/padding parameters. If left unset or set to None, this will use the predefined model maximum length if a maximum length is required by one of the truncation/padding parameters. If the model has no specific maximum input length (like XLNet) truncation/padding to a maximum length will be deactivated.

  • stride (int, optional, defaults to 0) — If set to a number along with max_length, the overflowing tokens returned when return_overflowing_tokens=True will contain some tokens from the end of the truncated sequence returned to provide some overlap between truncated and overflowing sequences. The value of this argument defines the number of overlapping tokens.

  • pad_to_multiple_of (int, optional) — If set will pad the sequence to a multiple of the provided value. This is especially useful to enable the use of Tensor Cores on NVIDIA hardware with compute capability >= 7.5 (Volta).

    • '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.

LayoutLMv3Processor

class transformers.LayoutLMv3Processor

( image_processor = Nonetokenizer = None**kwargs )

Parameters

Constructs a LayoutLMv3 processor which combines a LayoutLMv3 image processor and a LayoutLMv3 tokenizer into a single processor.

__call__

( imagestext: typing.Union[str, typing.List[str], typing.List[typing.List[str]]] = Nonetext_pair: typing.Union[typing.List[str], typing.List[typing.List[str]], NoneType] = Noneboxes: typing.Union[typing.List[typing.List[int]], typing.List[typing.List[typing.List[int]]]] = Noneword_labels: typing.Union[typing.List[int], typing.List[typing.List[int]], NoneType] = Noneadd_special_tokens: bool = Truepadding: typing.Union[bool, str, transformers.utils.generic.PaddingStrategy] = Falsetruncation: typing.Union[bool, str, transformers.tokenization_utils_base.TruncationStrategy] = Nonemax_length: typing.Optional[int] = Nonestride: int = 0pad_to_multiple_of: typing.Optional[int] = Nonereturn_token_type_ids: typing.Optional[bool] = Nonereturn_attention_mask: typing.Optional[bool] = Nonereturn_overflowing_tokens: bool = Falsereturn_special_tokens_mask: bool = Falsereturn_offsets_mapping: bool = Falsereturn_length: bool = Falseverbose: bool = Truereturn_tensors: typing.Union[str, transformers.utils.generic.TensorType, NoneType] = None**kwargs )

Please refer to the docstring of the above two methods for more information.

LayoutLMv3Model

class transformers.LayoutLMv3Model

( config )

Parameters

forward

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.

  • bbox (torch.LongTensor of shape (batch_size, token_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.

  • 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, 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.

  • 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.

  • position_ids (torch.LongTensor of shape (batch_size, token_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.

  • 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 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.

Returns

  • last_hidden_state (torch.FloatTensor of shape (batch_size, sequence_length, hidden_size)) — Sequence of hidden-states at the output of the last layer of the model.

  • hidden_states (tuple(torch.FloatTensor), optional, returned when output_hidden_states=True is passed or when config.output_hidden_states=True) — Tuple of torch.FloatTensor (one for the output of the embeddings, if the model has an embedding layer, + one for the output of each layer) of shape (batch_size, sequence_length, hidden_size).

    Hidden-states of the model at the output of each layer plus the optional initial embedding outputs.

  • attentions (tuple(torch.FloatTensor), optional, returned when output_attentions=True is passed or when config.output_attentions=True) — Tuple of torch.FloatTensor (one for each layer) of shape (batch_size, num_heads, sequence_length, sequence_length).

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

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_state

LayoutLMv3ForSequenceClassification

class transformers.LayoutLMv3ForSequenceClassification

( config )

Parameters

forward

Parameters

  • input_ids (torch.LongTensor of shape (batch_size, sequence_length)) — Indices of input sequence tokens in the vocabulary.

  • 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.

  • token_type_ids (torch.LongTensor of shape (batch_size, sequence_length), optional) — Segment token indices to indicate first and second portions of the inputs. Indices are selected in [0, 1]:

    • 0 corresponds to a sentence A token,

    • 1 corresponds to a sentence B token.

  • position_ids (torch.LongTensor of shape (batch_size, sequence_length), optional) — Indices of positions of each input sequence tokens in the position embeddings. Selected in the range [0, config.max_position_embeddings - 1].

  • head_mask (torch.FloatTensor of shape (num_heads,) or (num_layers, num_heads), optional) — Mask to nullify selected heads of the self-attention modules. Mask values selected in [0, 1]:

    • 1 indicates the head is not masked,

    • 0 indicates the head is masked.

  • inputs_embeds (torch.FloatTensor of shape (batch_size, sequence_length, hidden_size), optional) — Optionally, instead of passing input_ids you can choose to directly pass an embedded representation. This is useful if you want more control over how to convert input_ids indices into associated vectors than the model’s internal embedding lookup matrix.

  • output_attentions (bool, optional) — 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.

Returns

  • 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.

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, 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.logits

LayoutLMv3ForTokenClassification

class transformers.LayoutLMv3ForTokenClassification

( config )

Parameters

forward

Parameters

  • input_ids (torch.LongTensor of shape (batch_size, sequence_length)) — Indices of input sequence tokens in the vocabulary.

  • 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.

  • token_type_ids (torch.LongTensor of shape (batch_size, sequence_length), optional) — Segment token indices to indicate first and second portions of the inputs. Indices are selected in [0, 1]:

    • 0 corresponds to a sentence A token,

    • 1 corresponds to a sentence B token.

  • position_ids (torch.LongTensor of shape (batch_size, sequence_length), optional) — Indices of positions of each input sequence tokens in the position embeddings. Selected in the range [0, config.max_position_embeddings - 1].

  • head_mask (torch.FloatTensor of shape (num_heads,) or (num_layers, num_heads), optional) — Mask to nullify selected heads of the self-attention modules. Mask values selected in [0, 1]:

    • 1 indicates the head is not masked,

    • 0 indicates the head is masked.

  • inputs_embeds (torch.FloatTensor of shape (batch_size, sequence_length, hidden_size), optional) — Optionally, instead of passing input_ids you can choose to directly pass an embedded representation. This is useful if you want more control over how to convert input_ids indices into associated vectors than the model’s internal embedding lookup matrix.

  • output_attentions (bool, optional) — 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.

  • labels (torch.LongTensor of shape (batch_size, sequence_length), optional) — Labels for computing the token classification loss. Indices should be in [0, ..., config.num_labels - 1].

Returns

  • loss (torch.FloatTensor of shape (1,), optional, returned when labels is provided) — Classification loss.

  • logits (torch.FloatTensor of shape (batch_size, sequence_length, config.num_labels)) — Classification scores (before SoftMax).

  • hidden_states (tuple(torch.FloatTensor), optional, returned when output_hidden_states=True is passed or when config.output_hidden_states=True) — Tuple of torch.FloatTensor (one for the output of the embeddings, if the model has an embedding layer, + one for the output of each layer) of shape (batch_size, sequence_length, hidden_size).

    Hidden-states of the model at the output of each layer plus the optional initial embedding outputs.

  • attentions (tuple(torch.FloatTensor), optional, returned when output_attentions=True is passed or when config.output_attentions=True) — Tuple of torch.FloatTensor (one for each layer) of shape (batch_size, num_heads, sequence_length, sequence_length).

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

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.logits

LayoutLMv3ForQuestionAnswering

class transformers.LayoutLMv3ForQuestionAnswering

( config )

Parameters

forward

Parameters

  • input_ids (torch.LongTensor of shape (batch_size, sequence_length)) — Indices of input sequence tokens in the vocabulary.

  • 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.

  • token_type_ids (torch.LongTensor of shape (batch_size, sequence_length), optional) — Segment token indices to indicate first and second portions of the inputs. Indices are selected in [0, 1]:

    • 0 corresponds to a sentence A token,

    • 1 corresponds to a sentence B token.

  • position_ids (torch.LongTensor of shape (batch_size, sequence_length), optional) — Indices of positions of each input sequence tokens in the position embeddings. Selected in the range [0, config.max_position_embeddings - 1].

  • head_mask (torch.FloatTensor of shape (num_heads,) or (num_layers, num_heads), optional) — Mask to nullify selected heads of the self-attention modules. Mask values selected in [0, 1]:

    • 1 indicates the head is not masked,

    • 0 indicates the head is masked.

  • inputs_embeds (torch.FloatTensor of shape (batch_size, sequence_length, hidden_size), optional) — Optionally, instead of passing input_ids you can choose to directly pass an embedded representation. This is useful if you want more control over how to convert input_ids indices into associated vectors than the model’s internal embedding lookup matrix.

  • output_attentions (bool, optional) — 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.

  • start_positions (torch.LongTensor of shape (batch_size,), optional) — Labels for position (index) of the start of the labelled span for computing the token classification loss. Positions are clamped to the length of the sequence (sequence_length). Position outside of the sequence are not taken into account for computing the loss.

  • end_positions (torch.LongTensor of shape (batch_size,), optional) — Labels for position (index) of the end of the labelled span for computing the token classification loss. Positions are clamped to the length of the sequence (sequence_length). Position outside of the sequence are not taken into account for computing the loss.

Returns

  • loss (torch.FloatTensor of shape (1,), optional, returned when labels is provided) — Total span extraction loss is the sum of a Cross-Entropy for the start and end positions.

  • start_logits (torch.FloatTensor of shape (batch_size, sequence_length)) — Span-start scores (before SoftMax).

  • end_logits (torch.FloatTensor of shape (batch_size, sequence_length)) — Span-end scores (before SoftMax).

  • hidden_states (tuple(torch.FloatTensor), optional, returned when output_hidden_states=True is passed or when config.output_hidden_states=True) — Tuple of torch.FloatTensor (one for the output of the embeddings, if the model has an embedding layer, + one for the output of each layer) of shape (batch_size, sequence_length, hidden_size).

    Hidden-states of the model at the output of each layer plus the optional initial embedding outputs.

  • attentions (tuple(torch.FloatTensor), optional, returned when output_attentions=True is passed or when config.output_attentions=True) — Tuple of torch.FloatTensor (one for each layer) of shape (batch_size, num_heads, sequence_length, sequence_length).

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

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_logits

TFLayoutLMv3Model

class transformers.TFLayoutLMv3Model

( *args**kwargs )

Parameters

TensorFlow models and layers in transformers accept two formats as input:

  • having all inputs as keyword arguments (like PyTorch models), or

  • having all inputs as a list, tuple or dict in the first positional argument.

The reason the second format is supported is that Keras methods prefer this format when passing inputs to models and layers. Because of this support, when using methods like model.fit() things should “just work” for you - just pass your inputs and labels in any format that model.fit() supports! If, however, you want to use the second format outside of Keras methods like fit() and predict(), such as when creating your own layers or models with the Keras Functional API, there are three possibilities you can use to gather all the input Tensors in the first positional argument:

  • a single Tensor with input_ids only and nothing else: model(input_ids)

  • a list of varying length with one or several input Tensors IN THE ORDER given in the docstring: model([input_ids, attention_mask]) or model([input_ids, attention_mask, token_type_ids])

  • a dictionary with one or several input Tensors associated to the input names given in the docstring: model({"input_ids": input_ids, "token_type_ids": token_type_ids})

call

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.

  • 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.

  • pixel_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.

  • token_type_ids (Numpy array or tf.Tensor of shape (batch_size, sequence_length), optional) — Segment token indices to indicate first and second portions of the inputs. Indices are selected in [0, 1]:

    • 0 corresponds to a sentence A token,

    • 1 corresponds to a sentence B token.

    Note that sequence_length = token_sequence_length + patch_sequence_length + 1 where 1 is for [CLS] token. See pixel_values for patch_sequence_length.

  • 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.

  • 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_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.

Returns

  • 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 heads.

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_state

TFLayoutLMv3ForSequenceClassification

class transformers.TFLayoutLMv3ForSequenceClassification

( *args**kwargs )

Parameters

TensorFlow models and layers in transformers accept two formats as input:

  • having all inputs as keyword arguments (like PyTorch models), or

  • having all inputs as a list, tuple or dict in the first positional argument.

The reason the second format is supported is that Keras methods prefer this format when passing inputs to models and layers. Because of this support, when using methods like model.fit() things should “just work” for you - just pass your inputs and labels in any format that model.fit() supports! If, however, you want to use the second format outside of Keras methods like fit() and predict(), such as when creating your own layers or models with the Keras Functional API, there are three possibilities you can use to gather all the input Tensors in the first positional argument:

  • a single Tensor with input_ids only and nothing else: model(input_ids)

  • a list of varying length with one or several input Tensors IN THE ORDER given in the docstring: model([input_ids, attention_mask]) or model([input_ids, attention_mask, token_type_ids])

  • a dictionary with one or several input Tensors associated to the input names given in the docstring: model({"input_ids": input_ids, "token_type_ids": token_type_ids})

call

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.

  • 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.

  • pixel_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.

  • token_type_ids (Numpy array or tf.Tensor of shape (batch_size, sequence_length), optional) — Segment token indices to indicate first and second portions of the inputs. Indices are selected in [0, 1]:

    • 0 corresponds to a sentence A token,

    • 1 corresponds to a sentence B token.

    Note that sequence_length = token_sequence_length + patch_sequence_length + 1 where 1 is for [CLS] token. See pixel_values for patch_sequence_length.

  • 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.

  • 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_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.

Returns

  • loss (tf.Tensor of shape (batch_size, ), optional, returned when labels is provided) — Classification (or regression if config.num_labels==1) loss.

  • logits (tf.Tensor of shape (batch_size, config.num_labels)) — Classification (or regression if config.num_labels==1) scores (before SoftMax).

  • hidden_states (tuple(tf.Tensor), optional, returned when output_hidden_states=True is passed or when config.output_hidden_states=True) — Tuple of tf.Tensor (one for the output of the embeddings + one for the output of each layer) of shape (batch_size, sequence_length, hidden_size).

    Hidden-states of the model at the output of each layer plus the initial embedding outputs.

  • attentions (tuple(tf.Tensor), optional, returned when output_attentions=True is passed or when config.output_attentions=True) — Tuple of tf.Tensor (one for each layer) of shape (batch_size, num_heads, sequence_length, sequence_length).

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

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, TFAutoModelForSequenceClassification
>>> from datasets import load_dataset
>>> import tensorflow as tf

>>> processor = AutoProcessor.from_pretrained("microsoft/layoutlmv3-base", apply_ocr=False)
>>> model = TFAutoModelForSequenceClassification.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")
>>> sequence_label = tf.convert_to_tensor([1])

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

TFLayoutLMv3ForTokenClassification

class transformers.TFLayoutLMv3ForTokenClassification

( *args**kwargs )

Parameters

TensorFlow models and layers in transformers accept two formats as input:

  • having all inputs as keyword arguments (like PyTorch models), or

  • having all inputs as a list, tuple or dict in the first positional argument.

The reason the second format is supported is that Keras methods prefer this format when passing inputs to models and layers. Because of this support, when using methods like model.fit() things should “just work” for you - just pass your inputs and labels in any format that model.fit() supports! If, however, you want to use the second format outside of Keras methods like fit() and predict(), such as when creating your own layers or models with the Keras Functional API, there are three possibilities you can use to gather all the input Tensors in the first positional argument:

  • a single Tensor with input_ids only and nothing else: model(input_ids)

  • a list of varying length with one or several input Tensors IN THE ORDER given in the docstring: model([input_ids, attention_mask]) or model([input_ids, attention_mask, token_type_ids])

  • a dictionary with one or several input Tensors associated to the input names given in the docstring: model({"input_ids": input_ids, "token_type_ids": token_type_ids})

call

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.

  • 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.

  • pixel_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.

  • token_type_ids (Numpy array or tf.Tensor of shape (batch_size, sequence_length), optional) — Segment token indices to indicate first and second portions of the inputs. Indices are selected in [0, 1]:

    • 0 corresponds to a sentence A token,

    • 1 corresponds to a sentence B token.

    Note that sequence_length = token_sequence_length + patch_sequence_length + 1 where 1 is for [CLS] token. See pixel_values for patch_sequence_length.

  • 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.

  • 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_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.

  • labels (tf.Tensor of shape (batch_size, sequence_length), optional) — Labels for computing the token classification loss. Indices should be in [0, ..., config.num_labels - 1].

Returns

  • loss (tf.Tensor of shape (n,), optional, where n is the number of unmasked labels, returned when labels is provided) — Classification loss.

  • logits (tf.Tensor of shape (batch_size, sequence_length, config.num_labels)) — Classification scores (before SoftMax).

  • hidden_states (tuple(tf.Tensor), optional, returned when output_hidden_states=True is passed or when config.output_hidden_states=True) — Tuple of tf.Tensor (one for the output of the embeddings + one for the output of each layer) of shape (batch_size, sequence_length, hidden_size).

    Hidden-states of the model at the output of each layer plus the initial embedding outputs.

  • attentions (tuple(tf.Tensor), optional, returned when output_attentions=True is passed or when config.output_attentions=True) — Tuple of tf.Tensor (one for each layer) of shape (batch_size, num_heads, sequence_length, sequence_length).

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

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, TFAutoModelForTokenClassification
>>> from datasets import load_dataset

>>> processor = AutoProcessor.from_pretrained("microsoft/layoutlmv3-base", apply_ocr=False)
>>> model = TFAutoModelForTokenClassification.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="tf")

>>> outputs = model(**encoding)
>>> loss = outputs.loss
>>> logits = outputs.logits

TFLayoutLMv3ForQuestionAnswering

class transformers.TFLayoutLMv3ForQuestionAnswering

( *args**kwargs )

Parameters

TensorFlow models and layers in transformers accept two formats as input:

  • having all inputs as keyword arguments (like PyTorch models), or

  • having all inputs as a list, tuple or dict in the first positional argument.

The reason the second format is supported is that Keras methods prefer this format when passing inputs to models and layers. Because of this support, when using methods like model.fit() things should “just work” for you - just pass your inputs and labels in any format that model.fit() supports! If, however, you want to use the second format outside of Keras methods like fit() and predict(), such as when creating your own layers or models with the Keras Functional API, there are three possibilities you can use to gather all the input Tensors in the first positional argument:

  • a single Tensor with input_ids only and nothing else: model(input_ids)

  • a list of varying length with one or several input Tensors IN THE ORDER given in the docstring: model([input_ids, attention_mask]) or model([input_ids, attention_mask, token_type_ids])

  • a dictionary with one or several input Tensors associated to the input names given in the docstring: model({"input_ids": input_ids, "token_type_ids": token_type_ids})

call

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.

  • 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.

  • pixel_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.

  • token_type_ids (Numpy array or tf.Tensor of shape (batch_size, sequence_length), optional) — Segment token indices to indicate first and second portions of the inputs. Indices are selected in [0, 1]:

    • 0 corresponds to a sentence A token,

    • 1 corresponds to a sentence B token.

    Note that sequence_length = token_sequence_length + patch_sequence_length + 1 where 1 is for [CLS] token. See pixel_values for patch_sequence_length.

  • 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.

  • 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_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.

  • start_positions (tf.Tensor of shape (batch_size,), optional) — Labels for position (index) of the start of the labelled span for computing the token classification loss. Positions are clamped to the length of the sequence (sequence_length). Position outside of the sequence are not taken into account for computing the loss.

  • end_positions (tf.Tensor of shape (batch_size,), optional) — Labels for position (index) of the end of the labelled span for computing the token classification loss. Positions are clamped to the length of the sequence (sequence_length). Position outside of the sequence are not taken into account for computing the loss.

Returns

  • loss (tf.Tensor of shape (batch_size, ), optional, returned when start_positions and end_positions are provided) — Total span extraction loss is the sum of a Cross-Entropy for the start and end positions.

  • start_logits (tf.Tensor of shape (batch_size, sequence_length)) — Span-start scores (before SoftMax).

  • end_logits (tf.Tensor of shape (batch_size, sequence_length)) — Span-end scores (before SoftMax).

  • hidden_states (tuple(tf.Tensor), optional, returned when output_hidden_states=True is passed or when config.output_hidden_states=True) — Tuple of tf.Tensor (one for the output of the embeddings + one for the output of each layer) of shape (batch_size, sequence_length, hidden_size).

    Hidden-states of the model at the output of each layer plus the initial embedding outputs.

  • attentions (tuple(tf.Tensor), optional, returned when output_attentions=True is passed or when config.output_attentions=True) — Tuple of tf.Tensor (one for each layer) of shape (batch_size, num_heads, sequence_length, sequence_length).

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

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, TFAutoModelForQuestionAnswering
>>> from datasets import load_dataset
>>> import tensorflow as tf

>>> processor = AutoProcessor.from_pretrained("microsoft/layoutlmv3-base", apply_ocr=False)
>>> model = TFAutoModelForQuestionAnswering.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="tf")
>>> start_positions = tf.convert_to_tensor([1])
>>> end_positions = tf.convert_to_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_logits

LayoutLMv3 architecture. Taken from the .

This model was contributed by . The TensorFlow version of this model was added by , , and . The original code can be found .

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 instead when preparing data for the model!

is supported by this .

is supported by this and .

A for how to perform inference with and a for how to perform inference when no labels are available with .

A for how to finetune with the 🌍 Trainer.

is supported by this .

vocab_size (int, optional, defaults to 50265) — Vocabulary size of the LayoutLMv3 model. Defines the number of different tokens that can be represented by the inputs_ids passed when calling .

type_vocab_size (int, optional, defaults to 2) — The vocabulary size of the token_type_ids passed when calling .

This is the configuration class to store the configuration of a . 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 architecture.

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

errors (str, optional, defaults to "replace") — Paradigm to follow when decoding bytes to UTF-8. See for more information.

Construct a LayoutLMv3 tokenizer. Based on RoBERTatokenizer (Byte Pair Encoding or BPE). 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 which contains most of the main methods. Users should refer to this superclass for more information regarding those methods.

runs end-to-end tokenization: punctuation splitting and wordpiece. It also turns the word-level bounding boxes into token-level bounding boxes.

padding (bool, str or , optional, defaults to False) — Activates and controls padding. Accepts the following values:

truncation (bool, str or , optional, defaults to False) — Activates and controls truncation. Accepts the following values:

return_tensors (str or , optional) — If set, will return tensors instead of list of python integers. Acceptable values are:

padding (bool, str or , optional, defaults to False) — Activates and controls padding. Accepts the following values:

truncation (bool, str or , optional, defaults to False) — Activates and controls truncation. Accepts the following values:

return_tensors (str or , optional) — If set, will return tensors instead of list of python integers. Acceptable values are:

errors (str, optional, defaults to "replace") — Paradigm to follow when decoding bytes to UTF-8. See for more information.

This tokenizer inherits from which contains most of the main methods. Users should refer to this superclass for more information regarding those methods.

padding (bool, str or , optional, defaults to False) — Activates and controls padding. Accepts the following values:

truncation (bool, str or , optional, defaults to False) — Activates and controls truncation. Accepts the following values:

return_tensors (str or , optional) — If set, will return tensors instead of list of python integers. Acceptable values are:

padding (bool, str or , optional, defaults to False) — Activates and controls padding. Accepts the following values:

truncation (bool, str or , optional, defaults to False) — Activates and controls truncation. Accepts the following values:

return_tensors (str or , optional) — If set, will return tensors instead of list of python integers. Acceptable values are:

image_processor (LayoutLMv3ImageProcessor) — An instance of . The image processor is a required input.

tokenizer (LayoutLMv3Tokenizer or LayoutLMv3TokenizerFast) — An instance of or . The tokenizer is a required input.

offers all the functionalities you need to prepare data for the model.

It first uses to resize and normalize document images, and optionally applies OCR to get words and normalized bounding boxes. These are then provided to or , which turns the words and bounding boxes into token-level input_ids, attention_mask, token_type_ids, bbox. Optionally, one can provide integer word_labels, which are turned into token-level labels for token classification tasks (such as FUNSD, CORD).

This method first forwards the images argument to . In case was initialized with apply_ocr set to True, it passes the obtained words and bounding boxes along with the additional arguments to and returns the output, together with resized and normalized pixel_values. In case 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 and returns the output, together with resized and normalized pixel_values.

config () — 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 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 sub-class. Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and behavior.

( input_ids: typing.Optional[torch.LongTensor] = Nonebbox: typing.Optional[torch.LongTensor] = Noneattention_mask: typing.Optional[torch.FloatTensor] = Nonetoken_type_ids: typing.Optional[torch.LongTensor] = Noneposition_ids: typing.Optional[torch.LongTensor] = Nonehead_mask: typing.Optional[torch.FloatTensor] = Noneinputs_embeds: typing.Optional[torch.FloatTensor] = Nonepixel_values: typing.Optional[torch.FloatTensor] = Noneoutput_attentions: typing.Optional[bool] = Noneoutput_hidden_states: typing.Optional[bool] = Nonereturn_dict: typing.Optional[bool] = None ) → or tuple(torch.FloatTensor)

Indices can be obtained using . See and for details.

return_dict (bool, optional) — Whether or not to return a instead of a plain tuple.

or tuple(torch.FloatTensor)

A 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 () and inputs.

The forward method, overrides the __call__ special method.

config () — 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 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 dataset.

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

( input_ids: typing.Optional[torch.LongTensor] = Noneattention_mask: typing.Optional[torch.FloatTensor] = Nonetoken_type_ids: typing.Optional[torch.LongTensor] = Noneposition_ids: typing.Optional[torch.LongTensor] = Nonehead_mask: typing.Optional[torch.FloatTensor] = Noneinputs_embeds: typing.Optional[torch.FloatTensor] = Nonelabels: typing.Optional[torch.LongTensor] = Noneoutput_attentions: typing.Optional[bool] = Noneoutput_hidden_states: typing.Optional[bool] = Nonereturn_dict: typing.Optional[bool] = Nonebbox: typing.Optional[torch.LongTensor] = Nonepixel_values: typing.Optional[torch.LongTensor] = None ) → or tuple(torch.FloatTensor)

Indices can be obtained using . See and for details.

return_dict (bool, optional) — Whether or not to return a instead of a plain tuple.

or tuple(torch.FloatTensor)

A 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 () and inputs.

The forward method, overrides the __call__ special method.

config () — 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 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 , , and .

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

( input_ids: typing.Optional[torch.LongTensor] = Nonebbox: typing.Optional[torch.LongTensor] = Noneattention_mask: typing.Optional[torch.FloatTensor] = Nonetoken_type_ids: typing.Optional[torch.LongTensor] = Noneposition_ids: typing.Optional[torch.LongTensor] = Nonehead_mask: typing.Optional[torch.FloatTensor] = Noneinputs_embeds: typing.Optional[torch.FloatTensor] = Nonelabels: typing.Optional[torch.LongTensor] = Noneoutput_attentions: typing.Optional[bool] = Noneoutput_hidden_states: typing.Optional[bool] = Nonereturn_dict: typing.Optional[bool] = Nonepixel_values: typing.Optional[torch.LongTensor] = None ) → or tuple(torch.FloatTensor)

Indices can be obtained using . See and for details.

return_dict (bool, optional) — Whether or not to return a instead of a plain tuple.

or tuple(torch.FloatTensor)

A 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 () and inputs.

The forward method, overrides the __call__ special method.

config () — 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 method to load the model weights.

LayoutLMv3 Model with a span classification head on top for extractive question-answering tasks such as (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 sub-class. Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and behavior.

( input_ids: typing.Optional[torch.LongTensor] = Noneattention_mask: typing.Optional[torch.FloatTensor] = Nonetoken_type_ids: typing.Optional[torch.LongTensor] = Noneposition_ids: typing.Optional[torch.LongTensor] = Nonehead_mask: typing.Optional[torch.FloatTensor] = Noneinputs_embeds: typing.Optional[torch.FloatTensor] = Nonestart_positions: typing.Optional[torch.LongTensor] = Noneend_positions: typing.Optional[torch.LongTensor] = Noneoutput_attentions: typing.Optional[bool] = Noneoutput_hidden_states: typing.Optional[bool] = Nonereturn_dict: typing.Optional[bool] = Nonebbox: typing.Optional[torch.LongTensor] = Nonepixel_values: typing.Optional[torch.LongTensor] = None ) → or tuple(torch.FloatTensor)

Indices can be obtained using . See and for details.

return_dict (bool, optional) — Whether or not to return a instead of a plain tuple.

or tuple(torch.FloatTensor)

A 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 () and inputs.

The forward method, overrides the __call__ special method.

config () — 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 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 . 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 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.

Note that when creating models and layers with 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!

( input_ids: tf.Tensor | None = Nonebbox: tf.Tensor | None = Noneattention_mask: tf.Tensor | None = Nonetoken_type_ids: tf.Tensor | None = Noneposition_ids: tf.Tensor | None = Nonehead_mask: tf.Tensor | None = Noneinputs_embeds: tf.Tensor | None = Nonepixel_values: tf.Tensor | None = Noneoutput_attentions: Optional[bool] = Noneoutput_hidden_states: Optional[bool] = Nonereturn_dict: Optional[bool] = Nonetraining: bool = False ) → or tuple(tf.Tensor)

Indices can be obtained using . See and for details.

return_dict (bool, optional) — Whether or not to return a instead of a plain tuple.

or tuple(tf.Tensor)

A or a tuple of tf.Tensor (if return_dict=False is passed or when config.return_dict=False) comprising various elements depending on the configuration () and inputs.

The forward method, overrides the __call__ special method.

config () — 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 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 dataset.

This model inherits from . 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 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.

Note that when creating models and layers with 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!

( input_ids: tf.Tensor | None = Noneattention_mask: tf.Tensor | None = Nonetoken_type_ids: tf.Tensor | None = Noneposition_ids: tf.Tensor | None = Nonehead_mask: tf.Tensor | None = Noneinputs_embeds: tf.Tensor | None = Nonelabels: tf.Tensor | None = Noneoutput_attentions: Optional[bool] = Noneoutput_hidden_states: Optional[bool] = Nonereturn_dict: Optional[bool] = Nonebbox: tf.Tensor | None = Nonepixel_values: tf.Tensor | None = Nonetraining: Optional[bool] = False ) → or tuple(tf.Tensor)

Indices can be obtained using . See and for details.

return_dict (bool, optional) — Whether or not to return a instead of a plain tuple.

or tuple(tf.Tensor)

A or a tuple of tf.Tensor (if return_dict=False is passed or when config.return_dict=False) comprising various elements depending on the configuration () and inputs.

The forward method, overrides the __call__ special method.

config () — 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 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 , , and .

This model inherits from . 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 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.

Note that when creating models and layers with 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!

( input_ids: tf.Tensor | None = Nonebbox: tf.Tensor | None = Noneattention_mask: tf.Tensor | None = Nonetoken_type_ids: tf.Tensor | None = Noneposition_ids: tf.Tensor | None = Nonehead_mask: tf.Tensor | None = Noneinputs_embeds: tf.Tensor | None = Nonelabels: tf.Tensor | None = Noneoutput_attentions: Optional[bool] = Noneoutput_hidden_states: Optional[bool] = Nonereturn_dict: Optional[bool] = Nonepixel_values: tf.Tensor | None = Nonetraining: Optional[bool] = False ) → or tuple(tf.Tensor)

Indices can be obtained using . See and for details.

return_dict (bool, optional) — Whether or not to return a instead of a plain tuple.

or tuple(tf.Tensor)

A or a tuple of tf.Tensor (if return_dict=False is passed or when config.return_dict=False) comprising various elements depending on the configuration () and inputs.

The forward method, overrides the __call__ special method.

config () — 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 method to load the model weights.

LayoutLMv3 Model with a span classification head on top for extractive question-answering tasks such as (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 inherits from . 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 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.

Note that when creating models and layers with 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!

( input_ids: tf.Tensor | None = Noneattention_mask: tf.Tensor | None = Nonetoken_type_ids: tf.Tensor | None = Noneposition_ids: tf.Tensor | None = Nonehead_mask: tf.Tensor | None = Noneinputs_embeds: tf.Tensor | None = Nonestart_positions: tf.Tensor | None = Noneend_positions: tf.Tensor | None = Noneoutput_attentions: Optional[bool] = Noneoutput_hidden_states: Optional[bool] = Nonebbox: tf.Tensor | None = Nonepixel_values: tf.Tensor | None = Nonereturn_dict: Optional[bool] = Nonetraining: bool = False ) → or tuple(tf.Tensor)

Indices can be obtained using . See and for details.

return_dict (bool, optional) — Whether or not to return a instead of a plain tuple.

or tuple(tf.Tensor)

A or a tuple of tf.Tensor (if return_dict=False is passed or when config.return_dict=False) comprising various elements depending on the configuration () and inputs.

The forward method, overrides the __call__ special method.

🌍
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original paper
nielsr
chriskoo
tokec
lre
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LayoutLMv2Processor
LayoutLMv2ForSequenceClassification
notebook
Text classification task guide
LayoutLMv3ForTokenClassification
example script
notebook
notebook
LayoutLMv2ForTokenClassification
notebook
LayoutLMv2ForTokenClassification
notebook
LayoutLMv2ForTokenClassification
Token classification task guide
LayoutLMv2ForQuestionAnswering
notebook
Question answering task guide
Document question answering task guide
<source>
LayoutLMv3Model
LayoutLMv3Model
LayoutLMv3Model
microsoft/layoutlmv3-base
PretrainedConfig
PretrainedConfig
<source>
<source>
<source>
<source>
<source>
bytes.decode
LayoutLMv3Tokenizer
PreTrainedTokenizer
LayoutLMv3Tokenizer
<source>
PaddingStrategy
TruncationStrategy
TensorType
PaddingStrategy
TruncationStrategy
TensorType
<source>
<source>
bytes.decode
PreTrainedTokenizerFast
<source>
PaddingStrategy
TruncationStrategy
TensorType
PaddingStrategy
TruncationStrategy
TensorType
<source>
LayoutLMv3ImageProcessor
LayoutLMv3Tokenizer
LayoutLMv3TokenizerFast
LayoutLMv3Processor
LayoutLMv3ImageProcessor
LayoutLMv3Tokenizer
LayoutLMv3TokenizerFast
<source>
call()
LayoutLMv3ImageProcessor
call()
LayoutLMv3ImageProcessor
call()
<source>
LayoutLMv3Config
from_pretrained()
torch.nn.Module
<source>
transformers.modeling_outputs.BaseModelOutput
AutoTokenizer
PreTrainedTokenizer.encode()
PreTrainedTokenizer.call()
What are input IDs?
What are attention masks?
What are token type IDs?
What are position IDs?
ModelOutput
transformers.modeling_outputs.BaseModelOutput
transformers.modeling_outputs.BaseModelOutput
LayoutLMv3Config
LayoutLMv3Model
<source>
LayoutLMv3Config
from_pretrained()
RVL-CDIP
torch.nn.Module
<source>
transformers.modeling_outputs.SequenceClassifierOutput
AutoTokenizer
PreTrainedTokenizer.encode()
PreTrainedTokenizer.call()
What are input IDs?
What are attention masks?
What are token type IDs?
What are position IDs?
ModelOutput
transformers.modeling_outputs.SequenceClassifierOutput
transformers.modeling_outputs.SequenceClassifierOutput
LayoutLMv3Config
LayoutLMv3ForSequenceClassification
<source>
LayoutLMv3Config
from_pretrained()
FUNSD
SROIE
CORD
Kleister-NDA
torch.nn.Module
<source>
transformers.modeling_outputs.TokenClassifierOutput
AutoTokenizer
PreTrainedTokenizer.encode()
PreTrainedTokenizer.call()
What are input IDs?
What are attention masks?
What are token type IDs?
What are position IDs?
ModelOutput
transformers.modeling_outputs.TokenClassifierOutput
transformers.modeling_outputs.TokenClassifierOutput
LayoutLMv3Config
LayoutLMv3ForTokenClassification
<source>
LayoutLMv3Config
from_pretrained()
DocVQA
torch.nn.Module
<source>
transformers.modeling_outputs.QuestionAnsweringModelOutput
AutoTokenizer
PreTrainedTokenizer.encode()
PreTrainedTokenizer.call()
What are input IDs?
What are attention masks?
What are token type IDs?
What are position IDs?
ModelOutput
transformers.modeling_outputs.QuestionAnsweringModelOutput
transformers.modeling_outputs.QuestionAnsweringModelOutput
LayoutLMv3Config
LayoutLMv3ForQuestionAnswering
<source>
LayoutLMv3Config
from_pretrained()
TFPreTrainedModel
tf.keras.Model
subclassing
<source>
transformers.modeling_tf_outputs.TFBaseModelOutput
AutoTokenizer
PreTrainedTokenizer.encode()
PreTrainedTokenizer.call()
What are input IDs?
What are attention masks?
What are token type IDs?
What are position IDs?
ModelOutput
transformers.modeling_tf_outputs.TFBaseModelOutput
transformers.modeling_tf_outputs.TFBaseModelOutput
LayoutLMv3Config
TFLayoutLMv3Model
<source>
LayoutLMv3Config
from_pretrained()
RVL-CDIP
TFPreTrainedModel
tf.keras.Model
subclassing
<source>
transformers.modeling_tf_outputs.TFSequenceClassifierOutput
AutoTokenizer
PreTrainedTokenizer.encode()
PreTrainedTokenizer.call()
What are input IDs?
What are attention masks?
What are token type IDs?
What are position IDs?
ModelOutput
transformers.modeling_tf_outputs.TFSequenceClassifierOutput
transformers.modeling_tf_outputs.TFSequenceClassifierOutput
LayoutLMv3Config
TFLayoutLMv3ForSequenceClassification
<source>
LayoutLMv3Config
from_pretrained()
FUNSD
SROIE
CORD
Kleister-NDA
TFPreTrainedModel
tf.keras.Model
subclassing
<source>
transformers.modeling_tf_outputs.TFTokenClassifierOutput
AutoTokenizer
PreTrainedTokenizer.encode()
PreTrainedTokenizer.call()
What are input IDs?
What are attention masks?
What are token type IDs?
What are position IDs?
ModelOutput
transformers.modeling_tf_outputs.TFTokenClassifierOutput
transformers.modeling_tf_outputs.TFTokenClassifierOutput
LayoutLMv3Config
TFLayoutLMv3ForTokenClassification
<source>
LayoutLMv3Config
from_pretrained()
DocVQA
TFPreTrainedModel
tf.keras.Model
subclassing
<source>
transformers.modeling_tf_outputs.TFQuestionAnsweringModelOutput
AutoTokenizer
PreTrainedTokenizer.encode()
PreTrainedTokenizer.call()
What are input IDs?
What are attention masks?
What are token type IDs?
What are position IDs?
ModelOutput
transformers.modeling_tf_outputs.TFQuestionAnsweringModelOutput
transformers.modeling_tf_outputs.TFQuestionAnsweringModelOutput
LayoutLMv3Config
TFLayoutLMv3ForQuestionAnswering
LayoutLMv3: Pre-training for Document AI with Unified Text and Image Masking
LayoutLMv2
ViT
LayoutLMv2
LayoutLMv3Processor
LayoutLMv3ImageProcessor
LayoutLMv3Tokenizer
LayoutLMv3TokenizerFast
LayoutLMv3Processor
usage guide
here
here