LayoutXLM · Hugging Face (original) (raw)

This model was released on 2021-04-18 and added to Hugging Face Transformers on 2021-11-03.

Overview

LayoutXLM was proposed in LayoutXLM: Multimodal Pre-training for Multilingual Visually-rich Document Understanding by Yiheng Xu, Tengchao Lv, Lei Cui, Guoxin Wang, Yijuan Lu, Dinei Florencio, Cha Zhang, Furu Wei. It’s a multilingual extension of the LayoutLMv2 model trained on 53 languages.

The abstract from the paper is the following:

Multimodal pre-training with text, layout, and image has achieved SOTA performance for visually-rich document understanding tasks recently, which demonstrates the great potential for joint learning across different modalities. In this paper, we present LayoutXLM, a multimodal pre-trained model for multilingual document understanding, which aims to bridge the language barriers for visually-rich document understanding. To accurately evaluate LayoutXLM, we also introduce a multilingual form understanding benchmark dataset named XFUN, which includes form understanding samples in 7 languages (Chinese, Japanese, Spanish, French, Italian, German, Portuguese), and key-value pairs are manually labeled for each language. Experiment results show that the LayoutXLM model has significantly outperformed the existing SOTA cross-lingual pre-trained models on the XFUN dataset.

This model was contributed by nielsr. The original code can be found here.

Usage tips and examples

One can directly plug in the weights of LayoutXLM into a LayoutLMv2 model, like so:

from transformers import LayoutLMv2Model

model = LayoutLMv2Model.from_pretrained("microsoft/layoutxlm-base")

Note that LayoutXLM has its own tokenizer, based onLayoutXLMTokenizer/LayoutXLMTokenizerFast. You can initialize it as follows:

from transformers import LayoutXLMTokenizer

tokenizer = LayoutXLMTokenizer.from_pretrained("microsoft/layoutxlm-base")

Similar to LayoutLMv2, you can use LayoutXLMProcessor (which internally appliesLayoutLMv2ImageProcessor andLayoutXLMTokenizer/LayoutXLMTokenizerFast in sequence) to prepare all data for the model.

As LayoutXLM’s architecture is equivalent to that of LayoutLMv2, one can refer to LayoutLMv2’s documentation page for all tips, code examples and notebooks.

LayoutXLMConfig

class transformers.LayoutXLMConfig

< source >

( transformers_version: str | None = None architectures: list[str] | None = None output_hidden_states: bool | None = False return_dict: bool | None = True dtype: typing.Union[str, ForwardRef('torch.dtype'), NoneType] = None chunk_size_feed_forward: int = 0 is_encoder_decoder: bool = False id2label: dict[int, str] | dict[str, str] | None = None label2id: dict[str, int] | dict[str, str] | None = None problem_type: typing.Optional[typing.Literal['regression', 'single_label_classification', 'multi_label_classification']] = None vocab_size: int = 30522 hidden_size: int = 768 num_hidden_layers: int = 12 num_attention_heads: int = 12 intermediate_size: int = 3072 hidden_act: str = 'gelu' hidden_dropout_prob: float | int = 0.1 attention_probs_dropout_prob: float | int = 0.1 max_position_embeddings: int = 512 type_vocab_size: int = 2 initializer_range: float = 0.02 layer_norm_eps: float = 1e-12 pad_token_id: int | None = 0 max_2d_position_embeddings: int = 1024 max_rel_pos: int = 128 rel_pos_bins: int = 32 fast_qkv: bool = True max_rel_2d_pos: int = 256 rel_2d_pos_bins: int = 64 convert_sync_batchnorm: bool = True image_feature_pool_shape: list[int] | tuple[int, ...] = (7, 7, 256) coordinate_size: int = 128 shape_size: int = 128 has_relative_attention_bias: bool = True has_spatial_attention_bias: bool = True has_visual_segment_embedding: bool = False detectron2_config_args: dict | None = None )

Parameters

• vocab_size (int, optional, defaults to 30522) — Vocabulary size of the model. Defines the number of different tokens that can be represented by the input_ids.
• hidden_size (int, optional, defaults to 768) — Dimension of the hidden representations.
• num_hidden_layers (int, optional, defaults to 12) — Number of hidden layers in the Transformer decoder.
• num_attention_heads (int, optional, defaults to 12) — Number of attention heads for each attention layer in the Transformer decoder.
• intermediate_size (int, optional, defaults to 3072) — Dimension of the MLP representations.
• hidden_act (str, optional, defaults to gelu) — The non-linear activation function (function or string) in the decoder. For example, "gelu","relu", "silu", etc.
• hidden_dropout_prob (Union[float, int], optional, defaults to 0.1) — The dropout probability for all fully connected layers in the embeddings, encoder, and pooler.
• attention_probs_dropout_prob (Union[float, int], 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.
• type_vocab_size (int, optional, defaults to 2) — The vocabulary size of the token_type_ids.
• initializer_range (float, optional, defaults to 0.02) — The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
• layer_norm_eps (float, optional, defaults to 1e-12) — The epsilon used by the layer normalization layers.
• pad_token_id (int, optional, defaults to 0) — Token id used for padding in the vocabulary.
• max_2d_position_embeddings (int, optional, defaults to 1024) — The maximum value that the 2D position embedding might ever be used with. Typically set this to something large just in case (e.g., 1024).
• max_rel_pos (int, optional, defaults to 128) — The maximum number of relative positions to be used in the self-attention mechanism.
• rel_pos_bins (int, optional, defaults to 32) — The number of relative position bins to be used in the self-attention mechanism.
• fast_qkv (bool, optional, defaults to True) — Whether or not to use a single matrix for the queries, keys, values in the self-attention layers.
• max_rel_2d_pos (int, optional, defaults to 256) — The maximum number of relative 2D positions in the self-attention mechanism.
• rel_2d_pos_bins (int, optional, defaults to 64) — The number of 2D relative position bins in the self-attention mechanism.
• convert_sync_batchnorm (bool, optional, defaults to True) — Whether or not to convert batch normalization layers to synchronized batch normalization layers.
• image_feature_pool_shape (list[int], optional, defaults to [7, 7, 256]) — The shape of the average-pooled feature map.
• coordinate_size (int, optional, defaults to 128) — Dimension of the coordinate embeddings.
• shape_size (int, optional, defaults to 128) — Dimension of the width and height embeddings.
• has_relative_attention_bias (bool, optional, defaults to True) — Whether or not to use a relative attention bias in the self-attention mechanism.
• has_spatial_attention_bias (bool, optional, defaults to True) — Whether or not to use a spatial attention bias in the self-attention mechanism.
• has_visual_segment_embedding (bool, optional, defaults to False) — Whether or not to add visual segment embeddings.
• detectron2_config_args (dict, optional) — Dictionary containing the configuration arguments of the Detectron2 visual backbone. Refer to this filefor details regarding default values.

This is the configuration class to store the configuration of a LayoutxlmModel. It is used to instantiate a Layoutxlm 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 microsoft/layoutxlm-base

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

Example:

from transformers import LayoutXLMConfig, LayoutXLMModel

configuration = LayoutXLMConfig()

model = LayoutXLMModel(configuration)

configuration = model.config

LayoutXLMTokenizer

class transformers.LayoutXLMTokenizer

< source >

( vocab: str | list | None = None bos_token = '' eos_token = '' sep_token = '' cls_token = '' unk_token = '' pad_token = '' mask_token = '' cls_token_box = [0, 0, 0, 0] sep_token_box = [1000, 1000, 1000, 1000] pad_token_box = [0, 0, 0, 0] pad_token_label = -100 only_label_first_subword = True add_prefix_space = True **kwargs )

Parameters

• vocab (str, dict or list, optional) — Vocabulary for the tokenizer as a path, a dictionary or a list of (token, score) tuples.
• 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.
• cls_token_box (list[int], optional, defaults to [0, 0, 0, 0]) — The bounding box to use for the special [CLS] token.
• sep_token_box (list[int], optional, defaults to [1000, 1000, 1000, 1000]) — The bounding box to use for the special [SEP] token.
• pad_token_box (list[int], optional, defaults to [0, 0, 0, 0]) — The bounding box to use for the special [PAD] token.
• pad_token_label (int, optional, defaults to -100) — The label to use for padding tokens. Defaults to -100, which is the ignore_index of PyTorch’s CrossEntropyLoss.
• only_label_first_subword (bool, optional, defaults to True) — Whether or not to only label the first subword, in case word labels are provided.
• add_prefix_space (bool, optional, defaults to True) — Whether or not to add an initial space to the input.
• additional_special_tokens (list[str], optional, defaults to ["<s>NOTUSED", "</s>NOTUSED"]) — Additional special tokens used by the tokenizer.

Construct a “fast” LayoutXLM tokenizer (backed by HuggingFace’s tokenizers library). Adapted fromRobertaTokenizer and XLNetTokenizer. Based onBPE.

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

__call__

< source >

( text: str | list[str] | list[list[str]] text_pair: list[str] | list[list[str]] | None = None boxes: list[list[int]] | list[list[list[int]]] | None = None word_labels: list[int] | list[list[int]] | None = None add_special_tokens: bool = True padding: bool | str | transformers.utils.generic.PaddingStrategy = False truncation: bool | str | transformers.tokenization_utils_base.TruncationStrategy = None max_length: int | None = None stride: int = 0 pad_to_multiple_of: int | None = None padding_side: str | None = None return_tensors: str | transformers.utils.generic.TensorType | None = None return_token_type_ids: bool | None = None return_attention_mask: bool | None = None return_overflowing_tokens: bool = False return_special_tokens_mask: bool = False return_offsets_mapping: bool = False return_length: bool = False verbose: bool = True **kwargs ) → BatchEncoding

Parameters

• text (str, list[str], list[list[str]]) — The sequence or batch of sequences to be encoded. Each sequence can be a string, a list of strings (words of a single example or questions of a batch of examples) or a list of list of strings (batch of words).
• text_pair (list[str], list[list[str]]) — The sequence or batch of sequences to be encoded. Each sequence should be a list of strings (pretokenized string).
• boxes (list[list[int]], list[list[list[int]]]) — Word-level bounding boxes. Each bounding box should be normalized to be on a 0-1000 scale.
• word_labels (list[int], list[list[int]], optional) — Word-level integer labels (for token classification tasks such as FUNSD, CORD).
• add_special_tokens (bool, optional, defaults to True) — Whether or not to encode the sequences with the special tokens relative to their model.
• padding (bool, str or PaddingStrategy, optional, defaults to False) — Activates and controls padding. Accepts the following values:
  • True or 'longest': Pad to the longest sequence in the batch (or no padding if only a single sequence if provided).
  • 'max_length': Pad to a maximum length specified with the argument max_length or to the maximum acceptable input length for the model if that argument is not provided.
  • False or 'do_not_pad' (default): No padding (i.e., can output a batch with sequences of different lengths).
• truncation (bool, str or TruncationStrategy, optional, defaults to False) — Activates and controls truncation. Accepts the following values:
  • True or 'longest_first': Truncate to a maximum length specified with the argument max_length or to the maximum acceptable input length for the model if that argument is not provided. This will truncate token by token, removing a token from the longest sequence in the pair if a pair of sequences (or a batch of pairs) is provided.
  • 'only_first': Truncate to a maximum length specified with the argument max_length or to the maximum acceptable input length for the model if that argument is not provided. This will only truncate the first sequence of a pair if a pair of sequences (or a batch of pairs) is provided.
  • 'only_second': Truncate to a maximum length specified with the argument max_length or to the maximum acceptable input length for the model if that argument is not provided. This will only truncate the second sequence of a pair if a pair of sequences (or a batch of pairs) is provided.
  • False or 'do_not_truncate' (default): No truncation (i.e., can output batch with sequence lengths greater than the model maximum admissible input size).
• max_length (int, optional) — Controls the maximum length to use by one of the truncation/padding parameters.
  If left unset or set to None, this will use the predefined model maximum length if a maximum length is required by one of the truncation/padding parameters. If the model has no specific maximum input length (like XLNet) truncation/padding to a maximum length will be deactivated.
• stride (int, optional, defaults to 0) — If set to a number along with max_length, the overflowing tokens returned whenreturn_overflowing_tokens=True will contain some tokens from the end of the truncated sequence returned to provide some overlap between truncated and overflowing sequences. The value of this argument defines the number of overlapping tokens.
• pad_to_multiple_of (int, optional) — If set will pad the sequence to a multiple of the provided value. This is especially useful to enable the use of Tensor Cores on NVIDIA hardware with compute capability >= 7.5 (Volta).
• return_tensors (str or TensorType, optional) — If set, will return tensors instead of list of python integers. Acceptable values are:
  • 'pt': Return PyTorch torch.Tensor objects.
  • 'np': Return Numpy np.ndarray objects.
• return_token_type_ids (bool, optional) — Whether to return token type IDs. If left to the default, will return the token type IDs according to the specific tokenizer’s default, defined by the return_outputs attribute.
  What are token type IDs?
• return_attention_mask (bool, optional) — Whether to return the attention mask. If left to the default, will return the attention mask according to the specific tokenizer’s default, defined by the return_outputs attribute.
  What are attention masks?
• return_overflowing_tokens (bool, optional, defaults to False) — Whether or not to return overflowing token sequences. If a pair of sequences of input ids (or a batch of pairs) is provided with truncation_strategy = longest_first or True, an error is raised instead of returning overflowing tokens.
• return_special_tokens_mask (bool, optional, defaults to False) — Whether or not to return special tokens mask information.
• return_offsets_mapping (bool, optional, defaults to False) — Whether or not to return (char_start, char_end) for each token.
  This is only available on fast tokenizers inheriting from PreTrainedTokenizerFast, if using Python’s tokenizer, this method will raise NotImplementedError.
• return_length (bool, optional, defaults to False) — Whether or not to return the lengths of the encoded inputs.
• verbose (bool, optional, defaults to True) — Whether or not to print more information and warnings.
• * *kwargs — passed to the self.tokenize() method

A BatchEncoding with the following fields:

• input_ids — List of token ids to be fed to a model.
  What are input IDs?
• bbox — List of bounding boxes to be fed to a model.
• token_type_ids — List of token type ids to be fed to a model (when return_token_type_ids=True or if “token_type_ids” is in self.model_input_names).
  What are token type IDs?
• attention_mask — List of indices specifying which tokens should be attended to by the model (whenreturn_attention_mask=True or if “attention_mask” is in self.model_input_names).
  What are attention masks?
• labels — List of labels to be fed to a model. (when word_labels is specified).
• overflowing_tokens — List of overflowing tokens sequences (when a max_length is specified andreturn_overflowing_tokens=True).
• num_truncated_tokens — Number of tokens truncated (when a max_length is specified andreturn_overflowing_tokens=True).
• special_tokens_mask — List of 0s and 1s, with 1 specifying added special tokens and 0 specifying regular sequence tokens (when add_special_tokens=True and return_special_tokens_mask=True).
• length — The length of the inputs (when return_length=True).

Main method to tokenize and prepare for the model one or several sequence(s) or one or several pair(s) of sequences with word-level normalized bounding boxes and optional labels.

build_inputs_with_special_tokens

< source >

( token_ids_0: list token_ids_1: list[int] | None = None ) → list[int]

Parameters

• token_ids_0 (list[int]) — List of IDs to which the special tokens will be added.
• token_ids_1 (list[int], optional) — Optional second list of IDs for sequence pairs.

List of input IDs with the appropriate special tokens.

Build model inputs from a sequence or a pair of sequence for sequence classification tasks by concatenating and adding special tokens. An XLM-RoBERTa sequence has the following format:

• single sequence: <s> X </s>
• pair of sequences: <s> A </s></s> B </s>

get_special_tokens_mask

< source >

( token_ids_0: list[int] token_ids_1: list[int] | None = None already_has_special_tokens: bool = False ) → A list of integers in the range [0, 1]

Parameters

• token_ids_0 — List of IDs for the (possibly already formatted) sequence.
• token_ids_1 — Unused when already_has_special_tokens=True. Must be None in that case.
• already_has_special_tokens — Whether the sequence is already formatted with special tokens.

Returns

A list of integers in the range [0, 1]

1 for a special token, 0 for a sequence token.

Retrieve sequence ids from a token list that has no special tokens added.

For fast tokenizers, data collators call this with already_has_special_tokens=True to build a mask over an already-formatted sequence. In that case, we compute the mask by checking membership in all_special_ids.

create_token_type_ids_from_sequences

< source >

( token_ids_0: list token_ids_1: list[int] | None = None ) → list[int]

Parameters

• token_ids_0 (list[int]) — List of IDs.
• token_ids_1 (list[int], optional) — Optional second list of IDs for sequence pairs.

List of zeros.

Create a mask from the two sequences passed to be used in a sequence-pair classification task. XLM-RoBERTa does not make use of token type ids, therefore a list of zeros is returned.

save_vocabulary

< source >

( save_directory: str filename_prefix: str | None = None )

LayoutXLMTokenizerFast

class transformers.LayoutXLMTokenizer

< source >

( vocab: str | list | None = None bos_token = '' eos_token = '' sep_token = '' cls_token = '' unk_token = '' pad_token = '' mask_token = '' cls_token_box = [0, 0, 0, 0] sep_token_box = [1000, 1000, 1000, 1000] pad_token_box = [0, 0, 0, 0] pad_token_label = -100 only_label_first_subword = True add_prefix_space = True **kwargs )

Parameters

• vocab (str, dict or list, optional) — Vocabulary for the tokenizer as a path, a dictionary or a list of (token, score) tuples.
• 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.
• cls_token_box (list[int], optional, defaults to [0, 0, 0, 0]) — The bounding box to use for the special [CLS] token.
• sep_token_box (list[int], optional, defaults to [1000, 1000, 1000, 1000]) — The bounding box to use for the special [SEP] token.
• pad_token_box (list[int], optional, defaults to [0, 0, 0, 0]) — The bounding box to use for the special [PAD] token.
• pad_token_label (int, optional, defaults to -100) — The label to use for padding tokens. Defaults to -100, which is the ignore_index of PyTorch’s CrossEntropyLoss.
• only_label_first_subword (bool, optional, defaults to True) — Whether or not to only label the first subword, in case word labels are provided.
• add_prefix_space (bool, optional, defaults to True) — Whether or not to add an initial space to the input.
• additional_special_tokens (list[str], optional, defaults to ["<s>NOTUSED", "</s>NOTUSED"]) — Additional special tokens used by the tokenizer.

Construct a “fast” LayoutXLM tokenizer (backed by HuggingFace’s tokenizers library). Adapted fromRobertaTokenizer and XLNetTokenizer. Based onBPE.

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

__call__

< source >

( text: str | list[str] | list[list[str]] text_pair: list[str] | list[list[str]] | None = None boxes: list[list[int]] | list[list[list[int]]] | None = None word_labels: list[int] | list[list[int]] | None = None add_special_tokens: bool = True padding: bool | str | transformers.utils.generic.PaddingStrategy = False truncation: bool | str | transformers.tokenization_utils_base.TruncationStrategy = None max_length: int | None = None stride: int = 0 pad_to_multiple_of: int | None = None padding_side: str | None = None return_tensors: str | transformers.utils.generic.TensorType | None = None return_token_type_ids: bool | None = None return_attention_mask: bool | None = None return_overflowing_tokens: bool = False return_special_tokens_mask: bool = False return_offsets_mapping: bool = False return_length: bool = False verbose: bool = True **kwargs ) → BatchEncoding

Parameters

• text (str, list[str], list[list[str]]) — The sequence or batch of sequences to be encoded. Each sequence can be a string, a list of strings (words of a single example or questions of a batch of examples) or a list of list of strings (batch of words).
• text_pair (list[str], list[list[str]]) — The sequence or batch of sequences to be encoded. Each sequence should be a list of strings (pretokenized string).
• boxes (list[list[int]], list[list[list[int]]]) — Word-level bounding boxes. Each bounding box should be normalized to be on a 0-1000 scale.
• word_labels (list[int], list[list[int]], optional) — Word-level integer labels (for token classification tasks such as FUNSD, CORD).
• add_special_tokens (bool, optional, defaults to True) — Whether or not to encode the sequences with the special tokens relative to their model.
• padding (bool, str or PaddingStrategy, optional, defaults to False) — Activates and controls padding. Accepts the following values:
  • True or 'longest': Pad to the longest sequence in the batch (or no padding if only a single sequence if provided).
  • 'max_length': Pad to a maximum length specified with the argument max_length or to the maximum acceptable input length for the model if that argument is not provided.
  • False or 'do_not_pad' (default): No padding (i.e., can output a batch with sequences of different lengths).
• truncation (bool, str or TruncationStrategy, optional, defaults to False) — Activates and controls truncation. Accepts the following values:
  • True or 'longest_first': Truncate to a maximum length specified with the argument max_length or to the maximum acceptable input length for the model if that argument is not provided. This will truncate token by token, removing a token from the longest sequence in the pair if a pair of sequences (or a batch of pairs) is provided.
  • 'only_first': Truncate to a maximum length specified with the argument max_length or to the maximum acceptable input length for the model if that argument is not provided. This will only truncate the first sequence of a pair if a pair of sequences (or a batch of pairs) is provided.
  • 'only_second': Truncate to a maximum length specified with the argument max_length or to the maximum acceptable input length for the model if that argument is not provided. This will only truncate the second sequence of a pair if a pair of sequences (or a batch of pairs) is provided.
  • False or 'do_not_truncate' (default): No truncation (i.e., can output batch with sequence lengths greater than the model maximum admissible input size).
• max_length (int, optional) — Controls the maximum length to use by one of the truncation/padding parameters.
  If left unset or set to None, this will use the predefined model maximum length if a maximum length is required by one of the truncation/padding parameters. If the model has no specific maximum input length (like XLNet) truncation/padding to a maximum length will be deactivated.
• stride (int, optional, defaults to 0) — If set to a number along with max_length, the overflowing tokens returned whenreturn_overflowing_tokens=True will contain some tokens from the end of the truncated sequence returned to provide some overlap between truncated and overflowing sequences. The value of this argument defines the number of overlapping tokens.
• pad_to_multiple_of (int, optional) — If set will pad the sequence to a multiple of the provided value. This is especially useful to enable the use of Tensor Cores on NVIDIA hardware with compute capability >= 7.5 (Volta).
• return_tensors (str or TensorType, optional) — If set, will return tensors instead of list of python integers. Acceptable values are:
  • 'pt': Return PyTorch torch.Tensor objects.
  • 'np': Return Numpy np.ndarray objects.
• return_token_type_ids (bool, optional) — Whether to return token type IDs. If left to the default, will return the token type IDs according to the specific tokenizer’s default, defined by the return_outputs attribute.
  What are token type IDs?
• return_attention_mask (bool, optional) — Whether to return the attention mask. If left to the default, will return the attention mask according to the specific tokenizer’s default, defined by the return_outputs attribute.
  What are attention masks?
• return_overflowing_tokens (bool, optional, defaults to False) — Whether or not to return overflowing token sequences. If a pair of sequences of input ids (or a batch of pairs) is provided with truncation_strategy = longest_first or True, an error is raised instead of returning overflowing tokens.
• return_special_tokens_mask (bool, optional, defaults to False) — Whether or not to return special tokens mask information.
• return_offsets_mapping (bool, optional, defaults to False) — Whether or not to return (char_start, char_end) for each token.
  This is only available on fast tokenizers inheriting from PreTrainedTokenizerFast, if using Python’s tokenizer, this method will raise NotImplementedError.
• return_length (bool, optional, defaults to False) — Whether or not to return the lengths of the encoded inputs.
• verbose (bool, optional, defaults to True) — Whether or not to print more information and warnings.
• * *kwargs — passed to the self.tokenize() method

A BatchEncoding with the following fields:

• input_ids — List of token ids to be fed to a model.
  What are input IDs?
• bbox — List of bounding boxes to be fed to a model.
• token_type_ids — List of token type ids to be fed to a model (when return_token_type_ids=True or if “token_type_ids” is in self.model_input_names).
  What are token type IDs?
• attention_mask — List of indices specifying which tokens should be attended to by the model (whenreturn_attention_mask=True or if “attention_mask” is in self.model_input_names).
  What are attention masks?
• labels — List of labels to be fed to a model. (when word_labels is specified).
• overflowing_tokens — List of overflowing tokens sequences (when a max_length is specified andreturn_overflowing_tokens=True).
• num_truncated_tokens — Number of tokens truncated (when a max_length is specified andreturn_overflowing_tokens=True).
• special_tokens_mask — List of 0s and 1s, with 1 specifying added special tokens and 0 specifying regular sequence tokens (when add_special_tokens=True and return_special_tokens_mask=True).
• length — The length of the inputs (when return_length=True).

Main method to tokenize and prepare for the model one or several sequence(s) or one or several pair(s) of sequences with word-level normalized bounding boxes and optional labels.

LayoutXLMProcessor

class transformers.LayoutXLMProcessor

< source >

( image_processor = None tokenizer = None **kwargs )

Parameters

• image_processor (LayoutLMv2ImageProcessor) — The image processor is a required input.
• tokenizer (LayoutXLMTokenizer) — The tokenizer is a required input.

Constructs a LayoutXLMProcessor which wraps a image processor and a tokenizer into a single processor.

LayoutXLMProcessor offers all the functionalities of LayoutLMv2ImageProcessor and LayoutXLMTokenizer. See the~LayoutLMv2ImageProcessor and ~LayoutXLMTokenizer for more information.

__call__

< source >

( images text: str | list[str] | list[list[str]] = None text_pair: list[str] | list[list[str]] | None = None boxes: list[list[int]] | list[list[list[int]]] | None = None word_labels: list[int] | list[list[int]] | None = None add_special_tokens: bool = True padding: bool | str | transformers.utils.generic.PaddingStrategy = False truncation: bool | str | transformers.tokenization_utils_base.TruncationStrategy = None max_length: int | None = None stride: int = 0 pad_to_multiple_of: int | None = None return_token_type_ids: bool | None = None return_attention_mask: bool | None = None return_overflowing_tokens: bool = False return_special_tokens_mask: bool = False return_offsets_mapping: bool = False return_length: bool = False verbose: bool = True return_tensors: str | transformers.utils.generic.TensorType | None = None **kwargs ) → ~tokenization_utils_base.BatchEncoding

Parameters

• images (` ) -- 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`.
• text (Union[str, list[str], list[list[str]]], optional) — The sequence or batch of sequences to be encoded. Each sequence can be a string or a list of strings (pretokenized string). If you pass a pretokenized input, set is_split_into_words=True to avoid ambiguity with batched inputs.
• text_pair (str, list[str] or list[int], optional) — Optional second sequence to be encoded. This can be a string, a list of strings (tokenized string using the tokenize method) or a list of integers (tokenized string ids using the convert_tokens_to_idsmethod).
• boxes (list[list[int]] or list[list[list[int]]], optional) — Word-level bounding boxes. Each bounding box should be normalized to be on a 0-1000 scale.
• word_labels (list[int] or 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 add special tokens when encoding the sequences. This will use the underlyingPretrainedTokenizerBase.build_inputs_with_special_tokens function, which defines which tokens are automatically added to the input ids. This is useful if you want to add bos or eos tokens automatically.
• padding (bool, str or PaddingStrategy, optional, defaults to False) — Activates and controls padding. Accepts the following values:
  • True or 'longest': Pad to the longest sequence in the batch (or no padding if only a single sequence is provided).
  • 'max_length': Pad to a maximum length specified with the argument max_length or to the maximum acceptable input length for the model if that argument is not provided.
  • False or 'do_not_pad' (default): No padding (i.e., can output a batch with sequences of different lengths).
• truncation (bool, str or TruncationStrategy, optional) — Activates and controls truncation. Accepts the following values:
  • True or 'longest_first': Truncate to a maximum length specified with the argument max_length or to the maximum acceptable input length for the model if that argument is not provided. This will truncate token by token, removing a token from the longest sequence in the pair if a pair of sequences (or a batch of pairs) is provided.
  • 'only_first': Truncate to a maximum length specified with the argument max_length or to the maximum acceptable input length for the model if that argument is not provided. This will only truncate the first sequence of a pair if a pair of sequences (or a batch of pairs) is provided.
  • 'only_second': Truncate to a maximum length specified with the argument max_length or to the maximum acceptable input length for the model if that argument is not provided. This will only truncate the second sequence of a pair if a pair of sequences (or a batch of pairs) is provided.
  • False or 'do_not_truncate' (default): No truncation (i.e., can output batch with sequence lengths greater than the model maximum admissible input size).
• max_length (int, optional) — Controls the maximum length to use by one of the truncation/padding parameters.
  If left unset or set to None, this will use the predefined model maximum length if a maximum length is required by one of the truncation/padding parameters. If the model has no specific maximum input length (like XLNet) truncation/padding to a maximum length will be deactivated.
• stride (int, optional, defaults to 0) — If set to a number along with max_length, the overflowing tokens returned whenreturn_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. Requires padding to be activated. This is especially useful to enable using Tensor Cores on NVIDIA hardware with compute capability>= 7.5 (Volta).
• return_token_type_ids (bool, optional) — Whether to return token type IDs. If left to the default, will return the token type IDs according to the specific tokenizer’s default, defined by the return_outputs attribute.
  What are token type IDs?
• return_attention_mask (bool, optional) — Whether to return the attention mask. If left to the default, will return the attention mask according to the specific tokenizer’s default, defined by the return_outputs attribute.
  What are attention masks?
• return_overflowing_tokens (bool, optional, defaults to False) — Whether or not to return overflowing token sequences. If a pair of sequences of input ids (or a batch of pairs) is provided with truncation_strategy = longest_first or True, an error is raised instead of returning overflowing tokens.
• return_special_tokens_mask (bool, optional, defaults to False) — Whether or not to return special tokens mask information.
• return_offsets_mapping (bool, optional, defaults to False) — Whether or not to return (char_start, char_end) for each token.
  This is only available on fast tokenizers inheriting from PreTrainedTokenizerFast, if using Python’s tokenizer, this method will raise NotImplementedError.
• return_length (bool, optional, defaults to False) — Whether or not to return the lengths of the encoded inputs.
• verbose (bool, optional, defaults to True) — Whether or not to print more information and warnings.
• return_tensors (Union[str, ~utils.generic.TensorType], optional) — If set, will return tensors of a particular framework. Acceptable values are:
  • 'pt': Return PyTorch torch.Tensor objects.
  • 'np': Return NumPy np.ndarray objects.

Returns

~tokenization_utils_base.BatchEncoding

• data (dict, optional) — Dictionary of lists/arrays/tensors returned by the __call__/encode_plus/batch_encode_plus methods (‘input_ids’, ‘attention_mask’, etc.).
• encoding (tokenizers.Encoding or Sequence[tokenizers.Encoding], optional) — If the tokenizer is a fast tokenizer which outputs additional information like mapping from word/character space to token space the tokenizers.Encoding instance or list of instance (for batches) hold this information.
• tensor_type (Union[None, str, TensorType], optional) — You can give a tensor_type here to convert the lists of integers in PyTorch/Numpy Tensors at initialization.
• prepend_batch_axis (bool, optional, defaults to False) — Whether or not to add a batch axis when converting to tensors (see tensor_type above). Note that this parameter has an effect if the parameter tensor_type is set, otherwise has no effect.
• n_sequences (int, optional) — You can give a tensor_type here to convert the lists of integers in PyTorch/Numpy Tensors at initialization.

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