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 examplesOne 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 on LayoutXLMTokenizer/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 applies LayoutLMv2ImageProcessor and LayoutXLMTokenizer/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.
LayoutXLMTokenizer class transformers.LayoutXLMTokenizer < source >( vocab_file bos_token = '<s>' eos_token = '</s>' sep_token = '</s>' cls_token = '<s>' unk_token = '<unk>' pad_token = '<pad>' mask_token = '<mask>' cls_token_box = [0, 0, 0, 0] sep_token_box = [1000, 1000, 1000, 1000] pad_token_box = [0, 0, 0, 0] pad_token_label = -100 only_label_first_subword = True sp_model_kwargs: typing.Optional[dict[str, typing.Any]] = None **kwargs )
Parameters
str) — Path to the vocabulary file. 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.
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.
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. 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. 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. str, optional, defaults to "<pad>") — The token used for padding, for example when batching sequences of different lengths. 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. list[int], optional, defaults to [0, 0, 0, 0]) — The bounding box to use for the special [CLS] token. list[int], optional, defaults to [1000, 1000, 1000, 1000]) — The bounding box to use for the special [SEP] token. list[int], optional, defaults to [0, 0, 0, 0]) — The bounding box to use for the special [PAD] token. int, optional, defaults to -100) — The label to use for padding tokens. Defaults to -100, which is the ignore_index of PyTorch’s CrossEntropyLoss. bool, optional, defaults to True) — Whether or not to only label the first subword, in case word labels are provided. dict, optional) — Will be passed to the SentencePieceProcessor.__init__() method. The Python wrapper for SentencePiece can be used, among other things, to set:
enable_sampling: Enable subword regularization.
nbest_size: Sampling parameters for unigram. Invalid for BPE-Dropout.
nbest_size = {0,1}: No sampling is performed.nbest_size > 1: samples from the nbest_size results.nbest_size < 0: assuming that nbest_size is infinite and samples from the all hypothesis (lattice) using forward-filtering-and-backward-sampling algorithm.alpha: Smoothing parameter for unigram sampling, and dropout probability of merge operations for BPE-dropout.
SentencePieceProcessor) — The SentencePiece processor that is used for every conversion (string, tokens and IDs). Adapted from RobertaTokenizer and XLNetTokenizer. Based on SentencePiece.
This tokenizer inherits from PreTrainedTokenizer which contains most of the main methods. Users should refer to this superclass for more information regarding those methods.
__call__ < source >( text: typing.Union[str, list[str], list[list[str]]] text_pair: typing.Union[list[str], list[list[str]], NoneType] = None boxes: typing.Union[list[list[int]], list[list[list[int]]], NoneType] = None word_labels: typing.Union[list[int], list[list[int]], NoneType] = None add_special_tokens: bool = True padding: typing.Union[bool, str, transformers.utils.generic.PaddingStrategy] = False truncation: typing.Union[bool, str, transformers.tokenization_utils_base.TruncationStrategy] = None max_length: typing.Optional[int] = None stride: int = 0 pad_to_multiple_of: typing.Optional[int] = None padding_side: typing.Optional[str] = None return_tensors: typing.Union[str, transformers.utils.generic.TensorType, NoneType] = None return_token_type_ids: typing.Optional[bool] = None return_attention_mask: typing.Optional[bool] = None return_overflowing_tokens: bool = False return_special_tokens_mask: bool = False return_offsets_mapping: bool = False return_length: bool = False verbose: bool = True **kwargs ) → BatchEncoding
Parameters
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). list[str], list[list[str]]) — The sequence or batch of sequences to be encoded. Each sequence should be a list of strings (pretokenized string). list[list[int]], list[list[list[int]]]) — Word-level bounding boxes. Each bounding box should be normalized to be on a 0-1000 scale. list[int], list[list[int]], optional) — Word-level integer labels (for token classification tasks such as FUNSD, CORD). bool, optional, defaults to True) — Whether or not to encode the sequences with the special tokens relative to their model. 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).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).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.
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. 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). str or TensorType, optional) — If set, will return tensors instead of list of python integers. Acceptable values are:
'tf': Return TensorFlow tf.constant objects.'pt': Return PyTorch torch.Tensor objects.'np': Return Numpy np.ndarray objects.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.
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.
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. bool, optional, defaults to False) — Whether or not to return special tokens mask information. 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.
bool, optional, defaults to False) — Whether or not to return the lengths of the encoded inputs. bool, optional, defaults to True) — Whether or not to print more information and warnings. self.tokenize() method A BatchEncoding with the following fields:
input_ids — List of token ids to be fed to a model.
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).
attention_mask — List of indices specifying which tokens should be attended to by the model (when return_attention_mask=True or if “attention_mask” is in self.model_input_names).
labels — List of labels to be fed to a model. (when word_labels is specified).
overflowing_tokens — List of overflowing tokens sequences (when a max_length is specified and return_overflowing_tokens=True).
num_truncated_tokens — Number of tokens truncated (when a max_length is specified and return_overflowing_tokens=True).
special_tokens_mask — List of 0s and 1s, with 1 specifying added special tokens and 0 specifying regular sequence tokens (when add_special_tokens=True and return_special_tokens_mask=True).
length — The length of the inputs (when return_length=True).
Main method to tokenize and prepare for the model one or several sequence(s) or one or several pair(s) of sequences with word-level normalized bounding boxes and optional labels.
build_inputs_with_special_tokens < source >( token_ids_0: list token_ids_1: typing.Optional[list[int]] = None ) → list[int]
Parameters
list[int]) — List of IDs to which the special tokens will be added. 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:
<s> X </s><s> A </s></s> B </s>( token_ids_0: list token_ids_1: typing.Optional[list[int]] = None already_has_special_tokens: bool = False ) → list[int]
Parameters
list[int]) — List of IDs. list[int], optional) — Optional second list of IDs for sequence pairs. bool, optional, defaults to False) — Whether or not the token list is already formatted with special tokens for the model. A list of integers in the range [0, 1]: 1 for a special token, 0 for a sequence token.
Retrieve sequence ids from a token list that has no special tokens added. This method is called when adding special tokens using the tokenizer prepare_for_model method.
( token_ids_0: list token_ids_1: typing.Optional[list[int]] = None ) → list[int]
Parameters
list[int]) — List of IDs. 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: typing.Optional[str] = None )
LayoutXLMTokenizerFast class transformers.LayoutXLMTokenizerFast < source >( vocab_file = None tokenizer_file = None bos_token = '<s>' eos_token = '</s>' sep_token = '</s>' cls_token = '<s>' unk_token = '<unk>' pad_token = '<pad>' mask_token = '<mask>' cls_token_box = [0, 0, 0, 0] sep_token_box = [1000, 1000, 1000, 1000] pad_token_box = [0, 0, 0, 0] pad_token_label = -100 only_label_first_subword = True **kwargs )
Parameters
str) — Path to the vocabulary file. 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.
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.
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. 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. 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. str, optional, defaults to "<pad>") — The token used for padding, for example when batching sequences of different lengths. 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. list[int], optional, defaults to [0, 0, 0, 0]) — The bounding box to use for the special [CLS] token. list[int], optional, defaults to [1000, 1000, 1000, 1000]) — The bounding box to use for the special [SEP] token. list[int], optional, defaults to [0, 0, 0, 0]) — The bounding box to use for the special [PAD] token. int, optional, defaults to -100) — The label to use for padding tokens. Defaults to -100, which is the ignore_index of PyTorch’s CrossEntropyLoss. bool, optional, defaults to True) — Whether or not to only label the first subword, in case word labels are provided. 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 from RobertaTokenizer and XLNetTokenizer. Based on BPE.
This tokenizer inherits from PreTrainedTokenizerFast which contains most of the main methods. Users should refer to this superclass for more information regarding those methods.
__call__ < source >( text: typing.Union[str, list[str], list[list[str]]] text_pair: typing.Union[list[str], list[list[str]], NoneType] = None boxes: typing.Union[list[list[int]], list[list[list[int]]], NoneType] = None word_labels: typing.Union[list[int], list[list[int]], NoneType] = None add_special_tokens: bool = True padding: typing.Union[bool, str, transformers.utils.generic.PaddingStrategy] = False truncation: typing.Union[bool, str, transformers.tokenization_utils_base.TruncationStrategy] = None max_length: typing.Optional[int] = None stride: int = 0 pad_to_multiple_of: typing.Optional[int] = None padding_side: typing.Optional[str] = None return_tensors: typing.Union[str, transformers.utils.generic.TensorType, NoneType] = None return_token_type_ids: typing.Optional[bool] = None return_attention_mask: typing.Optional[bool] = None return_overflowing_tokens: bool = False return_special_tokens_mask: bool = False return_offsets_mapping: bool = False return_length: bool = False verbose: bool = True **kwargs ) → BatchEncoding
Parameters
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). list[str], list[list[str]]) — The sequence or batch of sequences to be encoded. Each sequence should be a list of strings (pretokenized string). list[list[int]], list[list[list[int]]]) — Word-level bounding boxes. Each bounding box should be normalized to be on a 0-1000 scale. list[int], list[list[int]], optional) — Word-level integer labels (for token classification tasks such as FUNSD, CORD). bool, optional, defaults to True) — Whether or not to encode the sequences with the special tokens relative to their model. 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).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).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.
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. 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). str or TensorType, optional) — If set, will return tensors instead of list of python integers. Acceptable values are:
'tf': Return TensorFlow tf.constant objects.'pt': Return PyTorch torch.Tensor objects.'np': Return Numpy np.ndarray objects.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.
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.
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. bool, optional, defaults to False) — Whether or not to return special tokens mask information. 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.
bool, optional, defaults to False) — Whether or not to return the lengths of the encoded inputs. bool, optional, defaults to True) — Whether or not to print more information and warnings. self.tokenize() method A BatchEncoding with the following fields:
input_ids — List of token ids to be fed to a model.
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).
attention_mask — List of indices specifying which tokens should be attended to by the model (when return_attention_mask=True or if “attention_mask” is in self.model_input_names).
labels — List of labels to be fed to a model. (when word_labels is specified).
overflowing_tokens — List of overflowing tokens sequences (when a max_length is specified and return_overflowing_tokens=True).
num_truncated_tokens — Number of tokens truncated (when a max_length is specified and return_overflowing_tokens=True).
special_tokens_mask — List of 0s and 1s, with 1 specifying added special tokens and 0 specifying regular sequence tokens (when add_special_tokens=True and return_special_tokens_mask=True).
length — The length of the inputs (when return_length=True).
Main method to tokenize and prepare for the model one or several sequence(s) or one or several pair(s) of sequences with word-level normalized bounding boxes and optional labels.
LayoutXLMProcessor class transformers.LayoutXLMProcessor < source >( image_processor = None tokenizer = None **kwargs )
Constructs a LayoutXLM processor which combines a LayoutXLM image processor and a LayoutXLM tokenizer into a single processor.
LayoutXLMProcessor offers all the functionalities you need to prepare data for the model.
It first uses LayoutLMv2ImageProcessor to resize document images to a fixed size, and optionally applies OCR to get words and normalized bounding boxes. These are then provided to LayoutXLMTokenizer or LayoutXLMTokenizerFast, 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).
( images text: typing.Union[str, list[str], list[list[str]]] = None text_pair: typing.Union[list[str], list[list[str]], NoneType] = None boxes: typing.Union[list[list[int]], list[list[list[int]]], NoneType] = None word_labels: typing.Union[list[int], list[list[int]], NoneType] = None add_special_tokens: bool = True padding: typing.Union[bool, str, transformers.utils.generic.PaddingStrategy] = False truncation: typing.Union[bool, str, transformers.tokenization_utils_base.TruncationStrategy] = None max_length: typing.Optional[int] = None stride: int = 0 pad_to_multiple_of: typing.Optional[int] = None return_token_type_ids: typing.Optional[bool] = None return_attention_mask: typing.Optional[bool] = None return_overflowing_tokens: bool = False return_special_tokens_mask: bool = False return_offsets_mapping: bool = False return_length: bool = False verbose: bool = True return_tensors: typing.Union[str, transformers.utils.generic.TensorType, NoneType] = None **kwargs )
This method first forwards the images argument to ~LayoutLMv2ImagePrpcessor.__call__. In case LayoutLMv2ImagePrpcessor was initialized with apply_ocr set to True, it passes the obtained words and bounding boxes along with the additional arguments to call() and returns the output, together with resized images. In case LayoutLMv2ImagePrpcessor was initialized with apply_ocr set to False, it passes the words (text/text_pair`) and `boxes` specified by the user along with the additional arguments to [__call__()](/docs/transformers/v4.53.1/en/model_doc/layoutxlm#transformers.LayoutXLMTokenizer.__call__) and returns the output, together with resized `images.
Please refer to the docstring of the above two methods for more information.
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