LayoutLM · Hugging Face (original) (raw)

This model was released on 2019-12-31 and added to Hugging Face Transformers on 2020-11-16.

LayoutLM jointly learns text and the document layout rather than focusing only on text. It incorporates positional layout information and visual features of words from the document images.

You can find all the original LayoutLM checkpoints under the LayoutLM collection.

Click on the LayoutLM models in the right sidebar for more examples of how to apply LayoutLM to different vision and language tasks.

The example below demonstrates question answering with the AutoModel class.

import torch from datasets import load_dataset from transformers import AutoTokenizer, LayoutLMForQuestionAnswering

tokenizer = AutoTokenizer.from_pretrained("impira/layoutlm-document-qa", add_prefix_space=True) model = LayoutLMForQuestionAnswering.from_pretrained("impira/layoutlm-document-qa", dtype=torch.float16)

dataset = load_dataset("nielsr/funsd", split="train") example = dataset[0] question = "what's his name?" words = example["words"] boxes = example["bboxes"]

encoding = tokenizer( question.split(), words, is_split_into_words=True, return_token_type_ids=True, return_tensors="pt" ) bbox = [] for i, s, w in zip(encoding.input_ids[0], encoding.sequence_ids(0), encoding.word_ids(0)): if s == 1: bbox.append(boxes[w]) elif i == tokenizer.sep_token_id: bbox.append([1000] * 4) else: bbox.append([0] * 4) encoding["bbox"] = torch.tensor([bbox])

word_ids = encoding.word_ids(0) outputs = model(**encoding) loss = outputs.loss start_scores = outputs.start_logits end_scores = outputs.end_logits start, end = word_ids[start_scores.argmax(-1)], word_ids[end_scores.argmax(-1)] print(" ".join(words[start : end + 1]))

Notes

• The original LayoutLM was not designed with a unified processing workflow. Instead, it expects preprocessed text (words) and bounding boxes (boxes) from an external OCR engine (like Pytesseract) and provide them as additional inputs to the tokenizer.
• The forward() method expects the input bbox (bounding boxes of the input tokens). Each bounding box should be in the format (x0, y0, x1, y1). (x0, y0) corresponds to the upper left corner of the bounding box and {x1, y1) corresponds to the lower right corner. The bounding boxes need to be normalized on a 0-1000 scale as shown below.

def normalize_bbox(bbox, width, height): return [ int(1000 * (bbox[0] / width)), int(1000 * (bbox[1] / height)), int(1000 * (bbox[2] / width)), int(1000 * (bbox[3] / height)), ]

• width and height correspond to the width and height of the original document in which the token occurs. These values can be obtained as shown below.

from PIL import Image

image = Image.open(name_of_your_document).convert("RGB")

width, height = image.size

Resources

A list of official Hugging Face and community (indicated by 🌎) resources to help you get started with LayoutLM. 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.

• Read fine-tuning LayoutLM for document-understanding using Keras & Hugging Face Transformers to learn more.
• Read fine-tune LayoutLM for document-understanding using only Hugging Face Transformers for more information.
• Refer to this notebook for a practical example of how to fine-tune LayoutLM.
• Refer to this notebook for an example of how to fine-tune LayoutLM for sequence classification.
• Refer to this notebook for an example of how to fine-tune LayoutLM for token classification.
• Read Deploy LayoutLM with Hugging Face Inference Endpoints to learn how to deploy LayoutLM.

LayoutLMConfig

class transformers.LayoutLMConfig

< 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 eos_token_id: int | list[int] | None = None bos_token_id: int | None = None use_cache: bool = True max_2d_position_embeddings: int = 1024 tie_word_embeddings: bool = True )

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.
• eos_token_id (Union[int, list[int]], optional) — Token id used for end-of-stream in the vocabulary.
• bos_token_id (int, optional) — Token id used for beginning-of-stream in the vocabulary.
• use_cache (bool, optional, defaults to True) — Whether or not the model should return the last key/values attentions (not used by all models). Only relevant if config.is_decoder=True or when the model is a decoder-only generative model.
• max_2d_position_embeddings (int, optional, defaults to 1024) — The maximum value that the 2D position embedding might ever used. Typically set this to something large just in case (e.g., 1024).
• tie_word_embeddings (bool, optional, defaults to True) — Whether to tie weight embeddings according to model’s tied_weights_keys mapping.

This is the configuration class to store the configuration of a LayoutLMModel. It is used to instantiate a Layoutlm 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/layoutlm-base-uncased

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

Examples:

from transformers import LayoutLMConfig, LayoutLMModel

configuration = LayoutLMConfig()

model = LayoutLMModel(configuration)

configuration = model.config

LayoutLMTokenizer

class transformers.BertTokenizer

< source >

( vocab: str | dict[str, int] | None = None do_lower_case: bool = True unk_token: str = '[UNK]' sep_token: str = '[SEP]' pad_token: str = '[PAD]' cls_token: str = '[CLS]' mask_token: str = '[MASK]' tokenize_chinese_chars: bool = True strip_accents: bool | None = None **kwargs )

Parameters

• vocab (str or dict[str, int], optional) — Custom vocabulary dictionary. If not provided, vocabulary is loaded from vocab_file.
• do_lower_case (bool, optional, defaults to True) — Whether or not to lowercase the input when tokenizing.
• unk_token (str, optional, defaults to "[UNK]") — The unknown token. A token that is not in the vocabulary cannot be converted to an ID and is set to be this token instead.
• sep_token (str, optional, defaults to "[SEP]") — The separator token, which is used when building a sequence from multiple sequences, e.g. two sequences for sequence classification or for a text and a question for question answering. It is also used as the last token of a sequence built with special tokens.
• pad_token (str, optional, defaults to "[PAD]") — The token used for padding, for example when batching sequences of different lengths.
• cls_token (str, optional, defaults to "[CLS]") — The classifier token which is used when doing sequence classification (classification of the whole sequence instead of per-token classification). It is the first token of the sequence when built with special tokens.
• mask_token (str, optional, defaults to "[MASK]") — The token used for masking values. This is the token used when training this model with masked language modeling. This is the token which the model will try to predict.
• tokenize_chinese_chars (bool, optional, defaults to True) — Whether or not to tokenize Chinese characters.
• strip_accents (bool, optional) — Whether or not to strip all accents. If this option is not specified, then it will be determined by the value for lowercase (as in the original BERT).

Construct a BERT tokenizer (backed by HuggingFace’s tokenizers library). Based on WordPiece.

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: TextInput | PreTokenizedInput | list[TextInput] | list[PreTokenizedInput] | None = None text_pair: TextInput | PreTokenizedInput | list[TextInput] | list[PreTokenizedInput] | None = None text_target: TextInput | PreTokenizedInput | list[TextInput] | list[PreTokenizedInput] | None = None text_pair_target: TextInput | PreTokenizedInput | list[TextInput] | list[PreTokenizedInput] | None = None add_special_tokens: bool = True padding: bool | str | PaddingStrategy = False truncation: bool | str | TruncationStrategy | None = None max_length: int | None = None stride: int = 0 is_split_into_words: bool = False pad_to_multiple_of: int | None = None padding_side: str | None = None return_tensors: str | 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 tokenizer_kwargs: dict[str, Any] | None = None **kwargs ) → BatchEncoding

Parameters

• text (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 the sequences are provided as list of strings (pretokenized), you must setis_split_into_words=True (to lift the ambiguity with a batch of sequences).
• text_pair (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 the sequences are provided as list of strings (pretokenized), you must setis_split_into_words=True (to lift the ambiguity with a batch of sequences).
• text_target (str, list[str], list[list[str]], optional) — The sequence or batch of sequences to be encoded as target texts. Each sequence can be a string or a list of strings (pretokenized string). If the sequences are provided as list of strings (pretokenized), you must set is_split_into_words=True (to lift the ambiguity with a batch of sequences).
• text_pair_target (str, list[str], list[list[str]], optional) — The sequence or batch of sequences to be encoded as target texts. Each sequence can be a string or a list of strings (pretokenized string). If the sequences are provided as list of strings (pretokenized), you must set is_split_into_words=True (to lift the ambiguity with a batch of sequences).
• tokenizer_kwargs (dict[str, Any], optional) — Additional kwargs to pass to the tokenizer. These will be merged with the explicit parameters and other kwargs, with explicit parameters taking precedence.
• 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, 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.
• is_split_into_words (bool, optional, defaults to False) — Whether or not the input is already pre-tokenized (e.g., split into words). If set to True, the tokenizer assumes the input is already split into words (for instance, by splitting it on whitespace) which it will tokenize. This is useful for NER or token classification.
• 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 the use of Tensor Cores on NVIDIA hardware with compute capability>= 7.5 (Volta).
• padding_side (str, optional) — The side on which the model should have padding applied. Should be selected between [‘right’, ‘left’]. Default value is picked from the class attribute of the same name.
• 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?
• 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?
• 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.

LayoutLMTokenizerFast

class transformers.BertTokenizer

< source >

( vocab: str | dict[str, int] | None = None do_lower_case: bool = True unk_token: str = '[UNK]' sep_token: str = '[SEP]' pad_token: str = '[PAD]' cls_token: str = '[CLS]' mask_token: str = '[MASK]' tokenize_chinese_chars: bool = True strip_accents: bool | None = None **kwargs )

Parameters

• vocab (str or dict[str, int], optional) — Custom vocabulary dictionary. If not provided, vocabulary is loaded from vocab_file.
• do_lower_case (bool, optional, defaults to True) — Whether or not to lowercase the input when tokenizing.
• unk_token (str, optional, defaults to "[UNK]") — The unknown token. A token that is not in the vocabulary cannot be converted to an ID and is set to be this token instead.
• sep_token (str, optional, defaults to "[SEP]") — The separator token, which is used when building a sequence from multiple sequences, e.g. two sequences for sequence classification or for a text and a question for question answering. It is also used as the last token of a sequence built with special tokens.
• pad_token (str, optional, defaults to "[PAD]") — The token used for padding, for example when batching sequences of different lengths.
• cls_token (str, optional, defaults to "[CLS]") — The classifier token which is used when doing sequence classification (classification of the whole sequence instead of per-token classification). It is the first token of the sequence when built with special tokens.
• mask_token (str, optional, defaults to "[MASK]") — The token used for masking values. This is the token used when training this model with masked language modeling. This is the token which the model will try to predict.
• tokenize_chinese_chars (bool, optional, defaults to True) — Whether or not to tokenize Chinese characters.
• strip_accents (bool, optional) — Whether or not to strip all accents. If this option is not specified, then it will be determined by the value for lowercase (as in the original BERT).

Construct a BERT tokenizer (backed by HuggingFace’s tokenizers library). Based on WordPiece.

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: TextInput | PreTokenizedInput | list[TextInput] | list[PreTokenizedInput] | None = None text_pair: TextInput | PreTokenizedInput | list[TextInput] | list[PreTokenizedInput] | None = None text_target: TextInput | PreTokenizedInput | list[TextInput] | list[PreTokenizedInput] | None = None text_pair_target: TextInput | PreTokenizedInput | list[TextInput] | list[PreTokenizedInput] | None = None add_special_tokens: bool = True padding: bool | str | PaddingStrategy = False truncation: bool | str | TruncationStrategy | None = None max_length: int | None = None stride: int = 0 is_split_into_words: bool = False pad_to_multiple_of: int | None = None padding_side: str | None = None return_tensors: str | 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 tokenizer_kwargs: dict[str, Any] | None = None **kwargs ) → BatchEncoding

Parameters

• text (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 the sequences are provided as list of strings (pretokenized), you must setis_split_into_words=True (to lift the ambiguity with a batch of sequences).
• text_pair (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 the sequences are provided as list of strings (pretokenized), you must setis_split_into_words=True (to lift the ambiguity with a batch of sequences).
• text_target (str, list[str], list[list[str]], optional) — The sequence or batch of sequences to be encoded as target texts. Each sequence can be a string or a list of strings (pretokenized string). If the sequences are provided as list of strings (pretokenized), you must set is_split_into_words=True (to lift the ambiguity with a batch of sequences).
• text_pair_target (str, list[str], list[list[str]], optional) — The sequence or batch of sequences to be encoded as target texts. Each sequence can be a string or a list of strings (pretokenized string). If the sequences are provided as list of strings (pretokenized), you must set is_split_into_words=True (to lift the ambiguity with a batch of sequences).
• tokenizer_kwargs (dict[str, Any], optional) — Additional kwargs to pass to the tokenizer. These will be merged with the explicit parameters and other kwargs, with explicit parameters taking precedence.
• 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, 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.
• is_split_into_words (bool, optional, defaults to False) — Whether or not the input is already pre-tokenized (e.g., split into words). If set to True, the tokenizer assumes the input is already split into words (for instance, by splitting it on whitespace) which it will tokenize. This is useful for NER or token classification.
• 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 the use of Tensor Cores on NVIDIA hardware with compute capability>= 7.5 (Volta).
• padding_side (str, optional) — The side on which the model should have padding applied. Should be selected between [‘right’, ‘left’]. Default value is picked from the class attribute of the same name.
• 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?
• 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?
• 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.

LayoutLMModel

class transformers.LayoutLMModel

< source >

( config )

Parameters

• config (LayoutLMModel) — 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 thefrom_pretrained() method to load the model weights.

The bare Layoutlm Model outputting raw hidden-states without any specific head on top.

This model inherits from PreTrainedModel. Check the superclass documentation for the generic methods the library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads etc.)

This model is also a PyTorch torch.nn.Module subclass. Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and behavior.

forward

< source >

( input_ids: torch.LongTensor | None = None bbox: torch.LongTensor | None = None attention_mask: torch.FloatTensor | None = None token_type_ids: torch.LongTensor | None = None position_ids: torch.LongTensor | None = None inputs_embeds: torch.FloatTensor | None = None **kwargs: typing_extensions.Unpack[transformers.utils.generic.TransformersKwargs] ) → BaseModelOutputWithPooling or tuple(torch.FloatTensor)

Parameters

• input_ids (torch.LongTensor of shape (batch_size, sequence_length), optional) — Indices of input sequence tokens in the vocabulary. Padding will be ignored by default.
  Indices can be obtained using AutoTokenizer. See PreTrainedTokenizer.encode() andPreTrainedTokenizer.call() for details.
  What are input IDs?
• bbox (torch.LongTensor of shape (batch_size, sequence_length, 4), optional) — Bounding boxes of each input sequence tokens. Selected in the range [0, config.max_2d_position_embeddings-1]. Each bounding box should be a normalized version in (x0, y0, x1, y1) format, where (x0, y0) corresponds to the position of the upper left corner in the bounding box, and (x1, y1) represents the position of the lower right corner. See Overview for normalization.
• attention_mask (torch.FloatTensor of shape (batch_size, sequence_length), optional) — Mask to avoid performing attention on padding token indices. Mask values selected in [0, 1]:
  • 1 for tokens that are not masked,
  • 0 for tokens that are masked.
    What are attention masks?
• token_type_ids (torch.LongTensor of shape (batch_size, sequence_length), optional) — Segment token indices to indicate first and second portions of the inputs. Indices are selected in [0, 1]:
  • 0 corresponds to a sentence A token,
  • 1 corresponds to a sentence B token.
    What are token type IDs?
• position_ids (torch.LongTensor of shape (batch_size, sequence_length), optional) — Indices of positions of each input sequence tokens in the position embeddings. Selected in the range [0, config.n_positions - 1].
  What are position IDs?
• 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.

Returns

BaseModelOutputWithPooling or tuple(torch.FloatTensor)

A BaseModelOutputWithPooling or a tuple oftorch.FloatTensor (if return_dict=False is passed or when config.return_dict=False) comprising various elements depending on the configuration (LayoutLMConfig) and inputs.

The LayoutLMModel forward method, overrides the __call__ special method.

Although the recipe for forward pass needs to be defined within this function, one should call the Moduleinstance afterwards instead of this since the former takes care of running the pre and post processing steps while the latter silently ignores them.

• 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.
• pooler_output (torch.FloatTensor of shape (batch_size, hidden_size)) — Last layer hidden-state of the first token of the sequence (classification token) after further processing through the layers used for the auxiliary pretraining task. E.g. for BERT-family of models, this returns the classification token after processing through a linear layer and a tanh activation function. The linear layer weights are trained from the next sentence prediction (classification) objective during pretraining.
• 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.

Examples:

from transformers import AutoTokenizer, LayoutLMModel import torch

tokenizer = AutoTokenizer.from_pretrained("microsoft/layoutlm-base-uncased") model = LayoutLMModel.from_pretrained("microsoft/layoutlm-base-uncased")

words = ["Hello", "world"] normalized_word_boxes = [637, 773, 693, 782], [698, 773, 733, 782]

token_boxes = [] for word, box in zip(words, normalized_word_boxes): ... word_tokens = tokenizer.tokenize(word) ... token_boxes.extend([box] * len(word_tokens))

token_boxes = [[0, 0, 0, 0]] + token_boxes + [[1000, 1000, 1000, 1000]]

encoding = tokenizer(" ".join(words), return_tensors="pt") input_ids = encoding["input_ids"] attention_mask = encoding["attention_mask"] token_type_ids = encoding["token_type_ids"] bbox = torch.tensor([token_boxes])

outputs = model( ... input_ids=input_ids, bbox=bbox, attention_mask=attention_mask, token_type_ids=token_type_ids ... )

last_hidden_states = outputs.last_hidden_state

LayoutLMForMaskedLM

class transformers.LayoutLMForMaskedLM

< source >

( config )

Parameters

• config (LayoutLMForMaskedLM) — 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 thefrom_pretrained() method to load the model weights.

The Layoutlm Model with a language modeling head on top.”

This model inherits from PreTrainedModel. Check the superclass documentation for the generic methods the library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads etc.)

This model is also a PyTorch torch.nn.Module subclass. Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and behavior.

forward

< source >

( input_ids: torch.LongTensor | None = None bbox: torch.LongTensor | None = None attention_mask: torch.FloatTensor | None = None token_type_ids: torch.LongTensor | None = None position_ids: torch.LongTensor | None = None inputs_embeds: torch.FloatTensor | None = None labels: torch.LongTensor | None = None **kwargs: typing_extensions.Unpack[transformers.utils.generic.TransformersKwargs] ) → MaskedLMOutput or tuple(torch.FloatTensor)

Parameters

• input_ids (torch.LongTensor of shape (batch_size, sequence_length), optional) — Indices of input sequence tokens in the vocabulary. Padding will be ignored by default.
  Indices can be obtained using AutoTokenizer. See PreTrainedTokenizer.encode() andPreTrainedTokenizer.call() for details.
  What are input IDs?
• bbox (torch.LongTensor of shape (batch_size, sequence_length, 4), optional) — Bounding boxes of each input sequence tokens. Selected in the range [0, config.max_2d_position_embeddings-1]. Each bounding box should be a normalized version in (x0, y0, x1, y1) format, where (x0, y0) corresponds to the position of the upper left corner in the bounding box, and (x1, y1) represents the position of the lower right corner. See Overview for normalization.
• attention_mask (torch.FloatTensor of shape (batch_size, sequence_length), optional) — Mask to avoid performing attention on padding token indices. Mask values selected in [0, 1]:
  • 1 for tokens that are not masked,
  • 0 for tokens that are masked.
    What are attention masks?
• token_type_ids (torch.LongTensor of shape (batch_size, sequence_length), optional) — Segment token indices to indicate first and second portions of the inputs. Indices are selected in [0, 1]:
  • 0 corresponds to a sentence A token,
  • 1 corresponds to a sentence B token.
    What are token type IDs?
• position_ids (torch.LongTensor of shape (batch_size, sequence_length), optional) — Indices of positions of each input sequence tokens in the position embeddings. Selected in the range [0, config.n_positions - 1].
  What are position IDs?
• 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.
• labels (torch.LongTensor of shape (batch_size, sequence_length), optional) — Labels for computing the masked language modeling loss. Indices should be in [-100, 0, ..., config.vocab_size] (see input_ids docstring) Tokens with indices set to -100 are ignored (masked), the loss is only computed for the tokens with labels in [0, ..., config.vocab_size]

Returns

MaskedLMOutput or tuple(torch.FloatTensor)

A MaskedLMOutput or a tuple oftorch.FloatTensor (if return_dict=False is passed or when config.return_dict=False) comprising various elements depending on the configuration (LayoutLMConfig) and inputs.

The LayoutLMForMaskedLM forward method, overrides the __call__ special method.

Although the recipe for forward pass needs to be defined within this function, one should call the Moduleinstance afterwards instead of this since the former takes care of running the pre and post processing steps while the latter silently ignores them.

• loss (torch.FloatTensor of shape (1,), optional, returned when labels is provided) — Masked language modeling (MLM) loss.
• logits (torch.FloatTensor of shape (batch_size, sequence_length, config.vocab_size)) — Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax).
• hidden_states (tuple(torch.FloatTensor), optional, returned when output_hidden_states=True is passed or when config.output_hidden_states=True) — Tuple of torch.FloatTensor (one for the output of the embeddings, if the model has an embedding layer, + one for the output 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.

Examples:

from transformers import AutoTokenizer, LayoutLMForMaskedLM import torch

tokenizer = AutoTokenizer.from_pretrained("microsoft/layoutlm-base-uncased") model = LayoutLMForMaskedLM.from_pretrained("microsoft/layoutlm-base-uncased")

words = ["Hello", "[MASK]"] normalized_word_boxes = [637, 773, 693, 782], [698, 773, 733, 782]

token_boxes = [] for word, box in zip(words, normalized_word_boxes): ... word_tokens = tokenizer.tokenize(word) ... token_boxes.extend([box] * len(word_tokens))

token_boxes = [[0, 0, 0, 0]] + token_boxes + [[1000, 1000, 1000, 1000]]

encoding = tokenizer(" ".join(words), return_tensors="pt") input_ids = encoding["input_ids"] attention_mask = encoding["attention_mask"] token_type_ids = encoding["token_type_ids"] bbox = torch.tensor([token_boxes])

labels = tokenizer("Hello world", return_tensors="pt")["input_ids"]

outputs = model( ... input_ids=input_ids, ... bbox=bbox, ... attention_mask=attention_mask, ... token_type_ids=token_type_ids, ... labels=labels, ... )

loss = outputs.loss

LayoutLMForSequenceClassification

class transformers.LayoutLMForSequenceClassification

< source >

( config )

Parameters

• config (LayoutLMForSequenceClassification) — 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 thefrom_pretrained() method to load the model weights.

LayoutLM Model with a sequence classification head on top (a linear layer on top of the pooled output) e.g. for document image classification tasks such as the RVL-CDIP dataset.

This model inherits from PreTrainedModel. Check the superclass documentation for the generic methods the library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads etc.)

This model is also a PyTorch torch.nn.Module subclass. Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and behavior.

forward

< source >

( input_ids: torch.LongTensor | None = None bbox: torch.LongTensor | None = None attention_mask: torch.FloatTensor | None = None token_type_ids: torch.LongTensor | None = None position_ids: torch.LongTensor | None = None inputs_embeds: torch.FloatTensor | None = None labels: torch.LongTensor | None = None **kwargs: typing_extensions.Unpack[transformers.utils.generic.TransformersKwargs] ) → SequenceClassifierOutput or tuple(torch.FloatTensor)

Parameters

• input_ids (torch.LongTensor of shape (batch_size, sequence_length), optional) — Indices of input sequence tokens in the vocabulary. Padding will be ignored by default.
  Indices can be obtained using AutoTokenizer. See PreTrainedTokenizer.encode() andPreTrainedTokenizer.call() for details.
  What are input IDs?
• bbox (torch.LongTensor of shape (batch_size, sequence_length, 4), optional) — Bounding boxes of each input sequence tokens. Selected in the range [0, config.max_2d_position_embeddings-1]. Each bounding box should be a normalized version in (x0, y0, x1, y1) format, where (x0, y0) corresponds to the position of the upper left corner in the bounding box, and (x1, y1) represents the position of the lower right corner. See Overview for normalization.
• attention_mask (torch.FloatTensor of shape (batch_size, sequence_length), optional) — Mask to avoid performing attention on padding token indices. Mask values selected in [0, 1]:
  • 1 for tokens that are not masked,
  • 0 for tokens that are masked.
    What are attention masks?
• token_type_ids (torch.LongTensor of shape (batch_size, sequence_length), optional) — Segment token indices to indicate first and second portions of the inputs. Indices are selected in [0, 1]:
  • 0 corresponds to a sentence A token,
  • 1 corresponds to a sentence B token.
    What are token type IDs?
• position_ids (torch.LongTensor of shape (batch_size, sequence_length), optional) — Indices of positions of each input sequence tokens in the position embeddings. Selected in the range [0, config.n_positions - 1].
  What are position IDs?
• 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.
• labels (torch.LongTensor of shape (batch_size,), optional) — Labels for computing the sequence classification/regression loss. Indices should be in [0, ..., config.num_labels - 1]. If config.num_labels == 1 a regression loss is computed (Mean-Square loss), Ifconfig.num_labels > 1 a classification loss is computed (Cross-Entropy).

Returns

SequenceClassifierOutput or tuple(torch.FloatTensor)

A SequenceClassifierOutput or a tuple oftorch.FloatTensor (if return_dict=False is passed or when config.return_dict=False) comprising various elements depending on the configuration (LayoutLMConfig) and inputs.

The LayoutLMForSequenceClassification forward method, overrides the __call__ special method.

Although the recipe for forward pass needs to be defined within this function, one should call the Moduleinstance afterwards instead of this since the former takes care of running the pre and post processing steps while the latter silently ignores them.

• 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.

Examples:

from transformers import AutoTokenizer, LayoutLMForSequenceClassification import torch

tokenizer = AutoTokenizer.from_pretrained("microsoft/layoutlm-base-uncased") model = LayoutLMForSequenceClassification.from_pretrained("microsoft/layoutlm-base-uncased")

words = ["Hello", "world"] normalized_word_boxes = [637, 773, 693, 782], [698, 773, 733, 782]

token_boxes = [] for word, box in zip(words, normalized_word_boxes): ... word_tokens = tokenizer.tokenize(word) ... token_boxes.extend([box] * len(word_tokens))

token_boxes = [[0, 0, 0, 0]] + token_boxes + [[1000, 1000, 1000, 1000]]

encoding = tokenizer(" ".join(words), return_tensors="pt") input_ids = encoding["input_ids"] attention_mask = encoding["attention_mask"] token_type_ids = encoding["token_type_ids"] bbox = torch.tensor([token_boxes]) sequence_label = torch.tensor([1])

outputs = model( ... input_ids=input_ids, ... bbox=bbox, ... attention_mask=attention_mask, ... token_type_ids=token_type_ids, ... labels=sequence_label, ... )

loss = outputs.loss logits = outputs.logits

LayoutLMForTokenClassification

class transformers.LayoutLMForTokenClassification

< source >

( config )

Parameters

• config (LayoutLMForTokenClassification) — 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 thefrom_pretrained() method to load the model weights.

LayoutLM Model with a token classification head on top (a linear layer on top of the hidden-states output) e.g. for sequence labeling (information extraction) tasks such as the FUNSDdataset and the SROIE dataset.

This model inherits from PreTrainedModel. Check the superclass documentation for the generic methods the library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads etc.)

This model is also a PyTorch torch.nn.Module subclass. Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and behavior.

forward

< source >

( input_ids: torch.LongTensor | None = None bbox: torch.LongTensor | None = None attention_mask: torch.FloatTensor | None = None token_type_ids: torch.LongTensor | None = None position_ids: torch.LongTensor | None = None inputs_embeds: torch.FloatTensor | None = None labels: torch.LongTensor | None = None **kwargs: typing_extensions.Unpack[transformers.utils.generic.TransformersKwargs] ) → TokenClassifierOutput or tuple(torch.FloatTensor)

Parameters

• input_ids (torch.LongTensor of shape (batch_size, sequence_length), optional) — Indices of input sequence tokens in the vocabulary. Padding will be ignored by default.
  Indices can be obtained using AutoTokenizer. See PreTrainedTokenizer.encode() andPreTrainedTokenizer.call() for details.
  What are input IDs?
• bbox (torch.LongTensor of shape (batch_size, sequence_length, 4), optional) — Bounding boxes of each input sequence tokens. Selected in the range [0, config.max_2d_position_embeddings-1]. Each bounding box should be a normalized version in (x0, y0, x1, y1) format, where (x0, y0) corresponds to the position of the upper left corner in the bounding box, and (x1, y1) represents the position of the lower right corner. See Overview for normalization.
• attention_mask (torch.FloatTensor of shape (batch_size, sequence_length), optional) — Mask to avoid performing attention on padding token indices. Mask values selected in [0, 1]:
  • 1 for tokens that are not masked,
  • 0 for tokens that are masked.
    What are attention masks?
• token_type_ids (torch.LongTensor of shape (batch_size, sequence_length), optional) — Segment token indices to indicate first and second portions of the inputs. Indices are selected in [0, 1]:
  • 0 corresponds to a sentence A token,
  • 1 corresponds to a sentence B token.
    What are token type IDs?
• position_ids (torch.LongTensor of shape (batch_size, sequence_length), optional) — Indices of positions of each input sequence tokens in the position embeddings. Selected in the range [0, config.n_positions - 1].
  What are position IDs?
• 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.
• 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

TokenClassifierOutput or tuple(torch.FloatTensor)

A TokenClassifierOutput or a tuple oftorch.FloatTensor (if return_dict=False is passed or when config.return_dict=False) comprising various elements depending on the configuration (LayoutLMConfig) and inputs.

The LayoutLMForTokenClassification forward method, overrides the __call__ special method.

Although the recipe for forward pass needs to be defined within this function, one should call the Moduleinstance afterwards instead of this since the former takes care of running the pre and post processing steps while the latter silently ignores them.

• 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.

Examples:

from transformers import AutoTokenizer, LayoutLMForTokenClassification import torch

tokenizer = AutoTokenizer.from_pretrained("microsoft/layoutlm-base-uncased") model = LayoutLMForTokenClassification.from_pretrained("microsoft/layoutlm-base-uncased")

words = ["Hello", "world"] normalized_word_boxes = [637, 773, 693, 782], [698, 773, 733, 782]

token_boxes = [] for word, box in zip(words, normalized_word_boxes): ... word_tokens = tokenizer.tokenize(word) ... token_boxes.extend([box] * len(word_tokens))

token_boxes = [[0, 0, 0, 0]] + token_boxes + [[1000, 1000, 1000, 1000]]

encoding = tokenizer(" ".join(words), return_tensors="pt") input_ids = encoding["input_ids"] attention_mask = encoding["attention_mask"] token_type_ids = encoding["token_type_ids"] bbox = torch.tensor([token_boxes]) token_labels = torch.tensor([1, 1, 0, 0]).unsqueeze(0)

outputs = model( ... input_ids=input_ids, ... bbox=bbox, ... attention_mask=attention_mask, ... token_type_ids=token_type_ids, ... labels=token_labels, ... )

loss = outputs.loss logits = outputs.logits

LayoutLMForQuestionAnswering

class transformers.LayoutLMForQuestionAnswering

< source >

( config has_visual_segment_embedding = True )

Parameters

• config (LayoutLMForQuestionAnswering) — 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 thefrom_pretrained() method to load the model weights.
• has_visual_segment_embedding (bool, optional, defaults to True) — Whether or not to add visual segment embeddings.

The Layoutlm transformer with a span classification head on top for extractive question-answering tasks like SQuAD (a linear layer on top of the hidden-states output to compute span start logits and span end logits).

This model inherits from PreTrainedModel. Check the superclass documentation for the generic methods the library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads etc.)

This model is also a PyTorch torch.nn.Module subclass. Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and behavior.

forward

< source >

( input_ids: torch.LongTensor | None = None bbox: torch.LongTensor | None = None attention_mask: torch.FloatTensor | None = None token_type_ids: torch.LongTensor | None = None position_ids: torch.LongTensor | None = None inputs_embeds: torch.FloatTensor | None = None start_positions: torch.LongTensor | None = None end_positions: torch.LongTensor | None = None **kwargs: typing_extensions.Unpack[transformers.utils.generic.TransformersKwargs] ) → QuestionAnsweringModelOutput or tuple(torch.FloatTensor)

Parameters

• input_ids (torch.LongTensor of shape (batch_size, sequence_length), optional) — Indices of input sequence tokens in the vocabulary. Padding will be ignored by default.
  Indices can be obtained using AutoTokenizer. See PreTrainedTokenizer.encode() andPreTrainedTokenizer.call() for details.
  What are input IDs?
• bbox (torch.LongTensor of shape (batch_size, sequence_length, 4), optional) — Bounding boxes of each input sequence tokens. Selected in the range [0, config.max_2d_position_embeddings-1]. Each bounding box should be a normalized version in (x0, y0, x1, y1) format, where (x0, y0) corresponds to the position of the upper left corner in the bounding box, and (x1, y1) represents the position of the lower right corner. See Overview for normalization.
• attention_mask (torch.FloatTensor of shape (batch_size, sequence_length), optional) — Mask to avoid performing attention on padding token indices. Mask values selected in [0, 1]:
  • 1 for tokens that are not masked,
  • 0 for tokens that are masked.
    What are attention masks?
• token_type_ids (torch.LongTensor of shape (batch_size, sequence_length), optional) — Segment token indices to indicate first and second portions of the inputs. Indices are selected in [0, 1]:
  • 0 corresponds to a sentence A token,
  • 1 corresponds to a sentence B token.
    What are token type IDs?
• position_ids (torch.LongTensor of shape (batch_size, sequence_length), optional) — Indices of positions of each input sequence tokens in the position embeddings. Selected in the range [0, config.n_positions - 1].
  What are position IDs?
• 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.
• 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

QuestionAnsweringModelOutput or tuple(torch.FloatTensor)

A QuestionAnsweringModelOutput or a tuple oftorch.FloatTensor (if return_dict=False is passed or when config.return_dict=False) comprising various elements depending on the configuration (LayoutLMConfig) and inputs.

The LayoutLMForQuestionAnswering forward method, overrides the __call__ special method.

Although the recipe for forward pass needs to be defined within this function, one should call the Moduleinstance afterwards instead of this since the former takes care of running the pre and post processing steps while the latter silently ignores them.

• 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.

Example:

In the example below, we prepare a question + context pair for the LayoutLM model. It will give us a prediction of what it thinks the answer is (the span of the answer within the texts parsed from the image).

from transformers import AutoTokenizer, LayoutLMForQuestionAnswering from datasets import load_dataset import torch

tokenizer = AutoTokenizer.from_pretrained("impira/layoutlm-document-qa", add_prefix_space=True) model = LayoutLMForQuestionAnswering.from_pretrained("impira/layoutlm-document-qa", revision="1e3ebac")

dataset = load_dataset("nielsr/funsd", split="train") example = dataset[0] question = "what's his name?" words = example["words"] boxes = example["bboxes"]

encoding = tokenizer( ... question.split(), words, is_split_into_words=True, return_token_type_ids=True, return_tensors="pt" ... ) bbox = [] for i, s, w in zip(encoding.input_ids[0], encoding.sequence_ids(0), encoding.word_ids(0)): ... if s == 1: ... bbox.append(boxes[w]) ... elif i == tokenizer.sep_token_id: ... bbox.append([1000] * 4) ... else: ... bbox.append([0] * 4) encoding["bbox"] = torch.tensor([bbox])

word_ids = encoding.word_ids(0) outputs = model(**encoding) loss = outputs.loss start_scores = outputs.start_logits end_scores = outputs.end_logits start, end = word_ids[start_scores.argmax(-1)], word_ids[end_scores.argmax(-1)] print(" ".join(words[start : end + 1])) M. Hamann P. Harper, P. Martinez

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